UNITED STATES ENVIRONMENTAL PROTECTION AGENCY 

WASHINGTON, D.C. 20460 

OFFICE OF CHEMICAL SAFETY AND 

POLLUTION PREVENTION 

MEMORANDUM 

Date: January 28, 2014 

SUBJECT: Dicrotophos: Benchmark Dose Analysis of Acute, Subchronic, and Chronic 

Studies to Support Derivation of Points of Departure. 

DP Barcode: D414900 

Registration No.: NA 

PC Code: 035201 · 

Decision No.: 454409 

Petition No.: NA 

Risk Assessment Type: NA 

TXR No.: 0056878 

MRIDNo.: NA 

Regulatory Action: Registration Review 

Case No.: 145 

FROM: 

CAS No.: 141-66-2 

40 CFR: 180.299 

John Liccione, Toxicologist/Risk Assessor 

RABV 

Elizabeth Holman, Physical Scientist 

RABII 

Health Effects Division (7905P) 

THROUGH: Anna Lowit, Ph.D. (;} ;J J I 

Senior Scientist Vr-tJ ~ Immediate Office 

Health Effects Division (7905P) 

TO: Elissa Reaves 

Branch Chief 

RABIV 

Health Effects Division (7905P) 

Page 1 of438 

... 
Page 2 of 438 

I. CONCLUSIONS 

HED performed benchmark dose (BMD) analyses of several acute, subchronic, and chronic 

studies for dicrotophos in order to support derivation of points of departure (POD) for 

dicrotophos as part of the single-chemical registration review risk assessment. 

This memo summarizes the approach and presents the results of BMD analyses. 

II. BACKGROUND 

BMD analyses were performed with EPA’s Benchmark Dose Software (Version 2.2) using an 

exponential model for continuous data. The data selected for evaluation consisted of decreased 

brain and red blood cell (RBC) cholinesterase (ChE) activities. For the acute studies, the 

analyses focused on both pup and adult data from the comparative cholinesterase (CCA) studies 

that entailed single dose exposures. Data from the 11-day repeated dosing and gestational 

dosing in the CCA study were also evaluated. In addition, OPP evaluated data from subchronic 

(oral, dermal, inhalation), chronic, and developmental neurotoxicity studies which included brain 

and/or RBC acetylcholinesterase (AChE) data in adult animals. AChE data from OP exposures 

ranging from approximately 21 days of dosing and longer are considered to be at steady state. 

OPP has used the exponential model for modeling AChE activity for the OP and N-methyl 

carbamate cumulative risk assessments along with multiple single chemical risk assessments of 

ChE-inhibiting pesticides. Model runs for ChE activity were conducted with an appropriate 

benchmark response level (10%). As such the BMD10 (estimated dose to result in 10% change 

from background levels) and BMDL10 (the lower 95% confidence level on the BMD10) are 

provided in the output. Statistical (e.g., goodness of fit values) and graphical results were used in 

model evaluation. 

III. RESULTS 

The results of the single dose and repeated oral dosing BMD analyses are 

summarized below in Tables 1 and 2 respectively. Table 3 summarizes the results of 

the BMD analyses for repeated exposure studies via the dermal and inhalation routes. 

Details are included in the appendix. Good model fit (p>0.1) was obtained for the 

majority of the analyses, with any exceptions being noted in these summary tables. 
Page 3 of 438 

TABLE 1: Results of BMD Exponential Modeling for Brain and RBC ChE Data on Dicrotophos, Single D

Dicrotophos/Study Sex/age Compartment 

BMD Results 

BMD10 (mg/kg) BMD

MRID 46153205 CCA Acute Study – Single Dose 

Male 

PND8 

Brain No reliable fit No 

Female 

PND8 

Brain 0.082 

MRID 46153205 CCA Acute Study – Single Dose 

Male 

PND8 

RBC 0.10 

Female 

PND8 

RBC 0.11 

MRID 46153205 CCA Acute Study – Single Dose 

Male 

PND15 

Brain 0.079 

Female 

PND15 

Brain No reliable fit No 

MRID 46153205 CCA Acute Study – Single Dose 

Male 

PND15 

RBC No reliable fit No 

Female 

PND15 

RBC 0.10 

MRID 46153205 CCA Acute Study – Single Dose 

Male 

PND22 

Brain 0.23 

Female 

PND22 

Brain No reliable fit No 

MRID 46153205 CCA Acute Study – Single Dose 

Male 

PND22 

RBC 0.12 

Female 

PND22 

RBC 0.14 

MRID 46153206 CCA Acute Study – Single Dose 

Adult Male 

PND42 

Brain 0.38 

Adult Female 

PND42 

Brain 0.12 
Page 4 of 438 

Dicrotophos/Study Sex/age Compartment 

BMD Results 

BMD10 (mg/kg) BMD

MRID 46153206 CCA Acute Study – Single Dose 

Adult Male 

PND42 

RBC No reliable fit No 

Adult Female 

PND42 

RBC 0.31 

TABLE 2: Results of BMD Exponential Modeling for Brain and RBC ChE Data on Dicrotophos, RepeateStudies Ranging in Duration from 7 days to 2 years. 

Dicrotophos/Study Sex/age Compartment 

BMD Results 

BMD10 (mg/kg/day) BMDL1

MRID 46153204 CCA Repeat Study – 7 days 

Adult Male 

PND42 

Brain 0.11 

Adult Female 

PND42 

Brain No reliable fit No 

MRID 46153204 CCA Repeat Study – 7 days 

Adult Male 

PND42 

RBC 0.072 

Adult Female 

PND42 

RBC 0.095 

MRID 46153204 CCA Repeat Study – 7 days 

Pup Male 

PND12 

Brain 0.064 

Pup Female 

PND12 

Brain 0.049 

MRID 46153204 CCA Repeat Study – 7 days 

Pup Male 

PND12 

RBC 0.045 

Pup Female 

PND12 

RBC 0.024 

MRID 46153208 CCA Repeat Study – 4-Weeks 

Adult Male Brain 0.060 

Adult Female Brain 0.077 
Page 5 of 438 

Dicrotophos/Study Sex/age Compartment 

BMD Results 

BMD10 (mg/kg/day) BMDL1

MRID 46153208 CCA Repeat Study – 4-Weeks 

Adult Male RBC 0.12 

Adult Female RBC 0.17 

MRID 46153201 Developmental Neurotoxicity in Rats 

Maternal 

Gestation Day 22 

(~15D of dosing) 

Brain 0.028 

Maternal 

Lactation Day 22 

(~37D of dosing) 

Brain 0.023 

MRID 46153201 Developmental Neurotoxicity in Rats 

Maternal 

Gestation Day 22 

(~15D of dosing) 

RBC 0.036 

Maternal 

Lactation Day 22 

(~37D of dosing) 

RBC 0.038 

MRID 46153201 Developmental Neurotoxicity in Rats 

Male Fetus 

Gestation Day 22 

Brain 0.064 

Female Fetus 

Gestation Day 22 

Brain 0.094 

MRID 46153201 Developmental Neurotoxicity in Rats 

Male Fetus 

Gestation Day 22 

RBC 0.066 

Female Fetus 

Gestation Day 22 

RBC 0.049 
Page 6 of 438 

Dicrotophos/Study Sex/age Compartment 

BMD Results 

BMD10 (mg/kg/day) BMDL1

MRID 43980201 Subchronic Neurotoxicity in Rats 

Male Adult 

35D of dosing 

Brain 0.039 

Female Adult 

35D of dosing 

Brain 0.048 

MRID 43980201 Subchronic Neurotoxicity in Rats 

Male Adult 

63D of dosing 

Brain 0.041a 

Female Adult 

63D of dosing 

Brain 0.044 

MRID 43980201 Subchronic Neurotoxicity in Rats 

Male Adult 

98D of dosing 

Brain 0.048 

Female Adult 

98D of dosing 

Brain 0.045 

MRID 43980201 Subchronic Neurotoxicity in Rats 

Male 

35D of dosing 

RBC 0.045 

Female Adult 

35D of dosing 

RBC 0.026 

MRID 43980201 Subchronic Neurotoxicity in Rats 

Male Adult 

63D of dosing 

RBC 0.048 

Female Adult 

63D of dosing 

RBC 0.057 

MRID 43980201 Subchronic Neurotoxicity in Rats 

Male Adult 

98D of dosing 

RBC 0.023 

Female Adult 

98D of dosing 

RBC 0.058 
Page 7 of 438 

Dicrotophos/Study Sex/age Compartment 

BMD Results 

BMD10 (mg/kg/day) BMDL1

MRID 44527802 Chronic Toxicity in Rats (2 year) 

Male Adult 

371D of dosing 

Brain 0.028a 

Female Adult 

371D of dosing 

Brain 0.047b 

MRID 44527802 Chronic Toxicity in Rats (2 year) 

Male Adult 

700D of dosing 

Brain 0.027 

Female Adult 

735D of dosing 

Brain 0.042c 

MRID 44527802 Chronic Toxicity in Rats (2 year) 

Male 

371D of dosing 

RBC 0.039d 

Female 

371D of dosing 

RBC 0.040 

MRID 44527802 Chronic Toxicity in Rats (2 year) 

Male Adult 

700D of dosing 

RBC No reliable fit No 

Female Adult 

735D of dosing 

RBC 0.053d 

a Due to dose spacing issues, adequate statistical model fit (p>0.1) were not obtained for these specific analyses for Brain ChE. Howev

characterization purposes, as these results are similar in magnitude to those obtained using the more robust techniques used in the 2006 

results of both the subchronic neurotoxicity and chronic toxicity brain ChE data). 

b p=0.077 for model fit; based on visual inspection and taking into account the observed dose spacing issues, this model fit is adequate. c p=0.0896 for model fit; based on visual inspection and taking into account the observed dose spacing issues, this model fit is adequated Dropped high dose in order to improve model fit. 
Page 8 of 438 

TABLE 3: Results of BMD Exponential Modeling for Brain and RBC ChE Data on Dicrotophos, Repeatevia the dermal and inhalation routes. 

Dicrotophos/Study Sex/age Compartment 

BMD Results 

BMD10 B

MRID 46484501 28-Day Dermal Toxicity Study 

Male Brain No reliable fit 

(p=0.062; 3.67 mg/kg/day) 

No 

(p=0.062; 

Female Brain 3.35 mg/kg/day 2.14 

MRID 46484501 28-Day Dermal Toxicity Study 

Male RBC 3.15 mg/kg/day 2.12 

Female RBC 3.65 mg/kg/day 2.54 

MRID 48146702 28-Day Inhalation Toxicity Study 

Male RBC 0.42 ug/L/day 0.34 

Female RBC 0.71 ug/L/day 0.49 

MRID 48146702 28-Day Inhalation Toxicity Study 

Male Brain 0.68 ug/L/day 0.6

Female Brain No reliable fit 

(p=0.01; 0.67 ug/L/day) 

No 

(p=0.01; 
Page 9 of 438 

Table 4 - Summary of Points of Departure for Dicrotophos 

Exposure

Scenario

Point of Departure

(BMDL10)

BMD10, Study and Toxicological

Effects

Acute Dietary (all 

populations)

BMDL10 = 0.07mg/kg/day BMD10 = 0.08 mg/kg/day 

CCA Study (MRID 4615205) in the rat – PND 8, 15, 

& 22 

Inhibition of brain ChE in male and female rat pups.

Steady-State 

Dietary (all 

populations)

BMDL10 = 0.03 mg/kg/day BMD10 = 0.04 mg/kg/day 

Subchronic and chronic toxicity studies (OP CRA 

2002, 2006; MRID’s 43980201 and 44527802)

Inhibition of brain ChE in adult rat

Incidental Oral

Single Day

BMDL10 = 0.07mg/kg/day BMD10 = 0.08 mg/kg/day 

CCA Study (MRID 4615205) in the rat – PND 8, 15, 

& 22 

Inhibition of brain ChE in male and female rat pups.

Incidental Oral

Steady State 

BMDL10 = 0.03 mg/kg/day BMD10 = 0.04 mg/kg/day 

Subchronic and chronic toxicity studies (OP CRA 

2002, 2006; MRID’s 43980201 and 44527802)

Inhibition of brain ChE in adult rat 

Dermal

Single Day & 

Steady State (21 

days and longer)

BMDL10 = 2.1 mg/kg/day BMD10 = 3.3 mg/kg/day 

28-day dermal toxicity study (MRID 46484501) 

in rats

Inhibition of brain ChE in adult rat.

Inhalation

Single Day & 

Steady State (21 

days and longer)

BMDL10 = 0.00062 

mg/L/day 

BMD10 = 0.00067 mg/L/day 

28-day inhalation toxicity study (MRID 

48146702) 

Inhibition of brain ChE in adult rat.

Point of Departure (PoD) = A data point or an estimated point that is derived from observed dose-response data and 

used to mark the beginning of extrapolation to determine risk associated with lower environmentally relevant human 

exposures. 
Page 10 of 438 

APPENDIX

 Single Dose CCA Studies

Male PND8 Brain ChE Inhibition

 ==================================================================== 

 Exponential Model. (Version: 1.7; Date: 12/10/2009) 

 Input Data File: C:/Usepa/BMDS212/Data/exp_Testrunscontinuous_Setting.(d) 

 Gnuplot Plotting File: 

 Fri Feb 04 12:50:23 2011 

 ==================================================================== 

 BMDS Model Run 

~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ 

 

 The form of the response function by Model: 

 Model 2: Y[dose] = a * exp{sign * b * dose} 

 Model 3: Y[dose] = a * exp{sign * (b * dose)^d} 

 Model 4: Y[dose] = a * [c-(c-1) * exp{-b * dose}] 

 Model 5: Y[dose] = a * [c-(c-1) * exp{-(b * dose)^d}] 

 Note: Y[dose] is the median response for exposure = dose; 

 sign = +1 for increasing trend in data; 

 sign = -1 for decreasing trend. 

 Model 2 is nested within Models 3 and 4. 

 Model 3 is nested within Model 5. 

 Model 4 is nested within Model 5. 

 Dependent variable = Mean 

 Independent variable = Dose 

 Data are assumed to be distributed: normally 

 Variance Model: exp(lnalpha +rho *ln(Y[dose])) 

 The variance is to be modeled as Var(i) = exp(lalpha + log(mean(i)) * rho) 

 Total number of dose groups = 5 

 Total number of records with missing values = 0 

 Maximum number of iterations = 250 

 Relative Function Convergence has been set to: 1e-008 

 Parameter Convergence has been set to: 1e-008 

 MLE solution provided: Exact 

 Initial Parameter Values 

 Variable Model 2 Model 3 Model 4 Model 5 

 -------- ------- ------- ------- ------- 

 lnalpha -4.24116 -4.24116 -4.24116 -4.24116 

 rho 1.39237 1.39237 1.39237 1.39237 

 a 1.16793 1.17334 3.5595 3.5595 

 b 0.287108 0.0424821 0.951872 0.951872 

 c -- -- 0.171239 

0.171239 

 d -- 2 -- 1 
Page 11 of 438 

 Parameter Estimates by Model 

 Variable Model 2 Model 3 Model 4 Model 5 

 -------- ------- ------- ------- ------- 

 lnalpha -2.37628 -3.64813 -3.97477 -3.97477 

 rho -0.278081 3.73979 1.29258 1.29258 

 a 3.25821 2.40915 3.25782 3.25782 

 b 1.30244 0.26755 1.84681 1.84681 

 c -- -- 0.200697 0.200697 

 d -- 1 -- 1 

 Table of Stats From Input Data 

 Dose N Obs Mean Obs Std Dev 

 ----- --- ---------- ------------- 

 0 5 3.39 0.28 

 0.1 5 2.83 0.23 

 0.3 5 1.95 0.17 

 1 5 1.14 0.2 

 5 5 0.64 0.07 

 Estimated Values of Interest 

 Model Dose Est Mean Est Std Scaled Residual 

 ------- ------ ---------- --------- ---------------- 

 2 0 3.258 0.2586 1.139 

 0.1 2.86 0.2634 -0.2574 

 0.3 2.204 0.2731 -2.083 

 1 0.8858 0.31 1.834 

 5 0.004839 0.6396 2.221 

 3 0 2.409 0.8353 2.626 

 0.1 2.346 0.7946 1.363 

 0.3 2.223 0.7189 -0.8502 

 1 1.844 0.5065 -3.106 

 5 0.6322 0.06847 0.2532 

 4 0 3.258 0.294 1.005 

 0.1 2.819 0.2678 0.09426 

 0.3 2.15 0.2248 -1.991 

 1 1.065 0.1427 1.182 

 5 0.6541 0.1042 -0.3024 

 5 0 3.258 0.294 1.005 

 0.1 2.819 0.2678 0.09426 

 0.3 2.15 0.2248 -1.991 

 1 1.065 0.1427 1.182 

 5 0.6541 0.1042 -0.3024 

 Other models for which likelihoods are calculated: 

 Model A1: Yij = Mu(i) + e(ij) 

 Var{e(ij)} = Sigma^2 

 Model A2: Yij = Mu(i) + e(ij) 

 Var{e(ij)} = Sigma(i)^2 

 Model A3: Yij = Mu(i) + e(ij) 

 Var{e(ij)} = exp(lalpha + log(mean(i)) * rho) 

 Model R: Yij = Mu + e(i) 
Page 12 of 438 

 Var{e(ij)} = Sigma^2 

 Likelihoods of Interest 

 Model Log(likelihood) DF AIC 

 ------- ----------------- ---- ------------ 

 A1 30.21049 6 -48.42098 

 A2 34.20578 10 -48.41155 

 A3 32.84948 7 -51.69896 

 R -13.40698 2 30.81397 

 2 15.51429 4 -23.02857 

 3 8.007102 4 -8.014203 

 4 28.71519 5 -47.43038 

 5 28.71519 5 -47.43038 

 Additive constant for all log-likelihoods = -22.97. This constant added to the 

 above values gives the log-likelihood including the term that does not 

 depend on the model parameters. 

 Explanation of Tests 

 Test 1: Does response and/or variances differ among Dose levels? (A2 vs. R) 

 Test 2: Are Variances Homogeneous? (A2 vs. A1) 

 Test 3: Are variances adequately modeled? (A2 vs. A3) 

 Test 4: Does Model 2 fit the data? (A3 vs. 2) 

 Test 5a: Does Model 3 fit the data? (A3 vs 3) 

 Test 5b: Is Model 3 better than Model 2? (3 vs. 2) 

 Test 6a: Does Model 4 fit the data? (A3 vs 4) 

 Test 6b: Is Model 4 better than Model 2? (4 vs. 2) 

 Test 7a: Does Model 5 fit the data? (A3 vs 5) 

 Test 7b: Is Model 5 better than Model 3? (5 vs. 3) 

 Test 7c: Is Model 5 better than Model 4? (5 vs. 4) 

 Tests of Interest 

 Test -2*log(Likelihood Ratio) D. F. p-value 

 -------- ------------------------ ------ -------------- 

 Test 1 95.23 8 < 0.0001 

 Test 2 7.991 4 0.09192 

 Test 3 2.713 3 0.4381 

 Test 4 34.67 3 < 0.0001 

 Test 5a 49.68 3 < 0.0001 

 Test 5b -15.01 0 N/A 

 Test 6a 8.269 2 0.01601 

 Test 6b 26.4 1 < 0.0001 

 Test 7a 8.269 2 0.01601 

 Test 7b 41.42 1 < 0.0001 

 Test 7c -9.415e-012 0 N/A 

 The p-value for Test 1 is less than .05. There appears to be a 

 difference between response and/or variances among the dose 

 levels, it seems appropriate to model the data. 

 The p-value for Test 2 is less than .1. A non-homogeneous 

 variance model appears to be appropriate. 
Page 13 of 438 

 The p-value for Test 3 is greater than .1. The modeled 

 variance appears to be appropriate here. 

 The p-value for Test 4 is less than .1. Model 2 may not adequately 

 describe the data; you may want to consider another model. 

 The p-value for Test 5a is less than .1. Model 3 may not adequately 

 describe the data; you may want to consider another model. 

 Degrees of freedom for Test 5b are less than or equal to 0. 

 The Chi-Square test for fit is not valid. 

 The p-value for Test 6a is less than .1. Model 4 may not adequately 

 describe the data; you may want to consider another model. 

 The p-value for Test 6b is less than .05. Model 4 appears 

 to fit the data better than Model 2. 

 The p-value for Test 7a is less than .1. Model 5 may not adequately 

 describe the data; you may want to consider another model. 

 The p-value for Test 7b is less than .05. Model 5 appears 

 to fit the data better than Model 3. 

 Degrees of freedom for Test 7c are less than or equal to 0. 

 The Chi-Square test for fit is not valid. 

 Benchmark Dose Computations: 

 Specified Effect = 0.100000 

 Risk Type = Relative deviation 

 Confidence Level = 0.950000 

 BMD and BMDL by Model 

 Model BMD BMDL 

 ------- ------------ ---------- 

 2 0.0808944 0.0667684 

 3 0.393797 0.345824 

 4 0.0723714 0.054721 

 5 0.0723714 0.054721 
Page 14 of 438 

 0.5

 1

 1.5

 2

 2.5

 3

 3.5

 4

 0 1 2 3 4 5

Mean Response

dose

Exponential Model 2 with 0.95 Confidence Level

12:50 02/04 2011

BMDLBMD

 

Exponential

 0.5

 1

 1.5

 2

 2.5

 3

 3.5

 4

 0 1 2 3 4 5

Mean Response

dose

Exponential Model 3 with 0.95 Confidence Level

12:50 02/04 2011

BMDL BMD

 

Exponential
Page 15 of 438 

 0.5

 1

 1.5

 2

 2.5

 3

 3.5

 4

 0 1 2 3 4 5

Mean Response

dose

Exponential Model 4 with 0.95 Confidence Level

12:50 02/04 2011

BMDLBMD

 

Exponential

 0.5

 1

 1.5

 2

 2.5

 3

 3.5

 4

 0 1 2 3 4 5

Mean Response

dose

Exponential Model 5 with 0.95 Confidence Level

12:50 02/04 2011

BMDLBMD

 

Exponential
Page 16 of 438 

Female PND8 Brain ChE Inhibition

==================================================================

== 

 Exponential Model. (Version: 1.7; Date: 12/10/2009) 

 Input Data File: C:/Documents and Settings/jliccion/My 

Documents/BMDS220/Data/exp_UntitledData_Setting.(d) 

 Gnuplot Plotting File: 

 Thu Jul 26 10:49:05 2012 

==================================================================

== 

 BMDS Model Run 

~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

~~~ 

 

 The form of the response function by Model: 

 Model 2: Y[dose] = a * exp{sign * b * dose} 

 Model 3: Y[dose] = a * exp{sign * (b * dose)^d} 

 Model 4: Y[dose] = a * [c-(c-1) * exp{-b * dose}] 

 Model 5: Y[dose] = a * [c-(c-1) * exp{-(b * dose)^d}] 

 Note: Y[dose] is the median response for exposure = dose; 

 sign = +1 for increasing trend in data; 

 sign = -1 for decreasing trend. 

 Model 2 is nested within Models 3 and 4. 

 Model 3 is nested within Model 5. 

 Model 4 is nested within Model 5. 

 Dependent variable = Mean 

 Independent variable = Dose 

 Data are assumed to be distributed: normally 

 Variance Model: exp(lnalpha +rho *ln(Y[dose])) 

 The variance is to be modeled as Var(i) = exp(lalpha + log(mean(i)) * rho) 

 Total number of dose groups = 5 

 Total number of records with missing values = 0 

 Maximum number of iterations = 250 

 Relative Function Convergence has been set to: 1e-008 

 Parameter Convergence has been set to: 1e-008 

 MLE solution provided: Exact 
Page 17 of 438 

 Initial Parameter Values 

 Variable Model 2 Model 3 Model 4 Model 5 

 -------- ------- ------- ------- ------- 

 lnalpha -4.62596 -4.62596 -4.62596 -4.62596 

 rho 1.28187 1.28187 1.28187 1.28187 

 a 1.20084 1.19122 3.3495 3.3495 

 b 0.284194 0.0414891 0.924146 0.924146 

 c -- -- 0.187661 0.187661 

 d -- 2 -- 1 

 Parameter Estimates by Model 

 Variable Model 2 Model 3 Model 4 Model 5 

 -------- ------- ------- ------- ------- 

 lnalpha -2.6115 -4.17284 -4.48155 -4.48155 

 rho -0.385145 4.25018 1.67832 1.67832 

 a 3.19908 2.41313 3.223 3.223 

 b 1.11329 0.260689 1.64398 1.64398 

 c -- -- 0.204218 0.204218 

 d -- 1 -- 1 

 Table of Stats From Input Data 

 Dose N Obs Mean Obs Std Dev 

 ----- --- ---------- ------------- 

 0 5 3.19 0.26 

 0.1 5 2.99 0.09 

 0.3 5 2.09 0.26 

 1 5 1.17 0.18 

 5 5 0.66 0.05 

 Estimated Values of Interest 

 Model Dose Est Mean Est Std Scaled Residual 

 ------- ------ ---------- --------- ---------------- 

 2 0 3.199 0.2166 -0.0937 

 0.1 2.862 0.2213 1.293 

 0.3 2.291 0.231 -1.943 

 1 1.051 0.2684 0.9929 

 5 0.01223 0.6327 2.289 
Page 18 of 438 

 3 0 2.413 0.807 2.152 

 0.1 2.351 0.7636 1.871 

 0.3 2.232 0.6835 -0.4633 

 1 1.859 0.4638 -3.324 

 5 0.6554 0.05057 0.2038 

 4 0 3.223 0.284 -0.2598 

 0.1 2.834 0.255 1.366 

 0.3 2.224 0.2081 -1.445 

 1 1.154 0.1199 0.3031 

 5 0.6589 0.07495 0.03322 

 5 0 3.223 0.284 -0.2598 

 0.1 2.834 0.255 1.366 

 0.3 2.224 0.2081 -1.445 

 1 1.154 0.1199 0.3031 

 5 0.6589 0.07495 0.03322 

 Other models for which likelihoods are calculated: 

 Model A1: Yij = Mu(i) + e(ij) 

 Var{e(ij)} = Sigma^2 

 Model A2: Yij = Mu(i) + e(ij) 

 Var{e(ij)} = Sigma(i)^2 

 Model A3: Yij = Mu(i) + e(ij) 

 Var{e(ij)} = exp(lalpha + log(mean(i)) * rho) 

 Model R: Yij = Mu + e(i) 

 Var{e(ij)} = Sigma^2 

 Likelihoods of Interest 

 Model Log(likelihood) DF AIC 

 ------- ----------------- ---- ------------ 

 A1 31.96788 6 -51.93576 

 A2 39.35241 10 -58.70482 

 A3 34.17331 7 -54.34662 

 R -12.57319 2 29.14637 

 2 18.88165 4 -29.76331 

 3 10.58704 4 -13.17409 

 4 32.03381 5 -54.06762 

 5 32.03381 5 -54.06762 
Page 19 of 438 

 Additive constant for all log-likelihoods = -22.97. This constant added to the 

 above values gives the log-likelihood including the term that does not 

 depend on the model parameters. 

 Explanation of Tests 

 Test 1: Does response and/or variances differ among Dose levels? (A2 vs. R) 

 Test 2: Are Variances Homogeneous? (A2 vs. A1) 

 Test 3: Are variances adequately modeled? (A2 vs. A3) 

 Test 4: Does Model 2 fit the data? (A3 vs. 2) 

 Test 5a: Does Model 3 fit the data? (A3 vs 3) 

 Test 5b: Is Model 3 better than Model 2? (3 vs. 2) 

 Test 6a: Does Model 4 fit the data? (A3 vs 4) 

 Test 6b: Is Model 4 better than Model 2? (4 vs. 2) 

 Test 7a: Does Model 5 fit the data? (A3 vs 5) 

 Test 7b: Is Model 5 better than Model 3? (5 vs. 3) 

 Test 7c: Is Model 5 better than Model 4? (5 vs. 4) 

 Tests of Interest 

 Test -2*log(Likelihood Ratio) D. F. p-value 

 -------- ------------------------ ------ -------------- 

 Test 1 103.9 8 < 0.0001 

 Test 2 14.77 4 0.005205 

 Test 3 10.36 3 0.01575 

 Test 4 30.58 3 < 0.0001 

 Test 5a 47.17 3 < 0.0001 

 Test 5b -16.59 0 N/A 

 Test 6a 4.279 2 0.1177 

 Test 6b 26.3 1 < 0.0001 

 Test 7a 4.279 2 0.1177 

 Test 7b 42.89 1 < 0.0001 

 Test 7c -9.948e-013 0 N/A 

 The p-value for Test 1 is less than .05. There appears to be a 

 difference between response and/or variances among the dose 

 levels, it seems appropriate to model the data. 

 The p-value for Test 2 is less than .1. A non-homogeneous 
Page 20 of 438 

 variance model appears to be appropriate. 

 The p-value for Test 3 is less than .1. You may want to 

 consider a different variance model. 

 The p-value for Test 4 is less than .1. Model 2 may not adequately 

 describe the data; you may want to consider another model. 

 The p-value for Test 5a is less than .1. Model 3 may not adequately 

 describe the data; you may want to consider another model. 

 Degrees of freedom for Test 5b are less than or equal to 0. 

 The Chi-Square test for fit is not valid. 

 The p-value for Test 6a is greater than .1. Model 4 seems 

 to adequately describe the data. 

 The p-value for Test 6b is less than .05. Model 4 appears 

 to fit the data better than Model 2. 

 The p-value for Test 7a is greater than .1. Model 5 seems 

 to adequately describe the data. 

 The p-value for Test 7b is less than .05. Model 5 appears 

 to fit the data better than Model 3. 

 Degrees of freedom for Test 7c are less than or equal to 0. 

 The Chi-Square test for fit is not valid. 

 Benchmark Dose Computations: 

 Specified Effect = 0.100000 

 Risk Type = Relative deviation 

 Confidence Level = 0.950000 

 BMD and BMDL by Model 

 Model BMD BMDL 

 ------- ------------ ---------- 

 2 0.0946385 0.0805217 

 3 0.404161 0.361863 

 4 0.0816853 0.0711499 
Page 21 of 438 

 5 0.0816853 0.0711499 

 0.5

 1

 1.5

 2

 2.5

 3

 3.5

 0 1 2 3 4 5

Mean Response

dose

Exponential Model 2 with 0.95 Confidence Level

10:49 07/26 2012

BMDLBMD

 

Exponential
Page 22 of 438 

 0.5

 1

 1.5

 2

 2.5

 3

 3.5

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Mean Response

dose

Exponential Model 3 with 0.95 Confidence Level

10:49 07/26 2012

BMDL BMD

 

Exponential

 0.5

 1

 1.5

 2

 2.5

 3

 3.5

 0 1 2 3 4 5

Mean Response

dose

Exponential Model 4 with 0.95 Confidence Level

10:49 07/26 2012

BMDLBMD

 

Exponential
Page 23 of 438 

Male PND8 RBC ChE Inhibition

!!! Warning: optimum may not have been found for Model 3 !!! 

!!! Bad completion code in maximum likelihood optimization routine !!! 

!!! Try choosing different initial values !!! 

Warning: optimum may not have been found form model 2 (BMDL). Bad process completion 

in Optimization routine. 

 ==================================================================== 

 Exponential Model. (Version: 1.7; Date: 12/10/2009) 

 Input Data File: C:/Documents and Settings/jliccion/My 

Documents/BMDS220/Data/exp_UntitledData_Setting.(d) 

 Gnuplot Plotting File: 

 Thu Jul 26 10:27:49 2012 

 ==================================================================== 

 BMDS Model Run 

~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ 

 

 The form of the response function by Model: 

 Model 2: Y[dose] = a * exp{sign * b * dose} 

 Model 3: Y[dose] = a * exp{sign * (b * dose)^d} 

 Model 4: Y[dose] = a * [c-(c-1) * exp{-b * dose}] 

 Model 5: Y[dose] = a * [c-(c-1) * exp{-(b * dose)^d}] 

 0.5

 1

 1.5

 2

 2.5

 3

 3.5

 0 1 2 3 4 5

Mean Response

dose

Exponential Model 5 with 0.95 Confidence Level

10:49 07/26 2012

BMDLBMD

 

Exponential
Page 24 of 438 

 Note: Y[dose] is the median response for exposure = dose; 

 sign = +1 for increasing trend in data; 

 sign = -1 for decreasing trend. 

 Model 2 is nested within Models 3 and 4. 

 Model 3 is nested within Model 5. 

 Model 4 is nested within Model 5. 

 Dependent variable = Mean 

 Independent variable = Dose 

 Data are assumed to be distributed: normally 

 Variance Model: exp(lnalpha +rho *ln(Y[dose])) 

 The variance is to be modeled as Var(i) = exp(lalpha + log(mean(i)) * rho) 

 Total number of dose groups = 5 

 Total number of records with missing values = 0 

 Maximum number of iterations = 250 

 Relative Function Convergence has been set to: 1e-008 

 Parameter Convergence has been set to: 1e-008 

 MLE solution provided: Exact 

 Initial Parameter Values 

 Variable Model 2 Model 3 Model 4 Model 5 

 -------- ------- ------- ------- ------- 

 lnalpha -9.80736 -9.80736 -9.80736 -9.80736 

 rho 2.82591 2.82591 2.82591 2.82591 

 a 1841.35 1841.35 3235.05 3235.05 

 b 0.120565 0.120565 0.717396 0.717396 

 c -- -- 0.431288 

0.431288 

 d -- 1 -- 1 

 Parameter Estimates by Model 

 Variable Model 2 Model 3 Model 4 Model 5 

 -------- ------- ------- ------- ------- 

 lnalpha -53.1618 -9.80736 0.618694 6.88865 

 rho 8.55125 2.82591 1.51144 0.685424 

 a 2422.14 1841.35 3060.4 2997.3 

 b 0.101286 0.120565 1.99301 3.31104 

 c -- -- 0.466057 0.51585 

 d -- 1 -- 16.8193 

 Table of Stats From Input Data 

 Dose N Obs Mean Obs Std Dev 

 ----- --- ---------- ------------- 

 0 5 2911 547 

 0.1 5 3081 551 

 0.3 5 2146 435 

 1 5 1624 608 

 5 5 1465 98 

 Estimated Values of Interest 
Page 25 of 438 

 Model Dose Est Mean Est Std Scaled Residual 

 ------- ------ ---------- --------- ---------------- 

 2 0 2422 842.6 1.297 

 0.1 2398 806.9 1.893 

 0.3 2350 740 -0.6154 

 1 2189 546.5 -2.311 

 5 1460 96.66 0.1229 

 3 0 0 0 1.#IO 

 0.1 0 0 1.#IO 

 0.3 0 0 1.#IO 

 1 0 0 1.#IO 

 5 0 0 1.#IO 

 4 0 3060 587 -0.5691 

 0.1 2765 543.6 1.299 

 0.3 2325 476.9 -0.8393 

 1 1649 367.8 -0.1521 

 5 1426 329.7 0.2618 

 5 0 2997 486.8 -0.3964 

 0.1 2997 486.8 0.3845 

 0.3 2140 433.7 0.03031 

 1 1546 388 0.4486 

 5 1546 388 -0.4677 

 Other models for which likelihoods are calculated: 

 Model A1: Yij = Mu(i) + e(ij) 

 Var{e(ij)} = Sigma^2 

 Model A2: Yij = Mu(i) + e(ij) 

 Var{e(ij)} = Sigma(i)^2 

 Model A3: Yij = Mu(i) + e(ij) 

 Var{e(ij)} = exp(lalpha + log(mean(i)) * rho) 

 Model R: Yij = Mu + e(i) 

 Var{e(ij)} = Sigma^2 

 Likelihoods of Interest 

 Model Log(likelihood) DF AIC 

 ------- ----------------- ---- ------------ 

 A1 -164.2631 6 340.5261 

 A2 -158.1441 10 336.2881 

 A3 -163.6662 7 341.3325 

 R -179.1511 2 362.3021 

 2 -167.0551 4 342.1101 

 3 -277.7852 4 563.5703 

 4 -165.2316 5 340.4631 

 5 -164.3516 6 340.7031 

 Additive constant for all log-likelihoods = -22.97. This constant added to the 

 above values gives the log-likelihood including the term that does not 

 depend on the model parameters. 

 Explanation of Tests 

 Test 1: Does response and/or variances differ among Dose levels? (A2 vs. R) 

 Test 2: Are Variances Homogeneous? (A2 vs. A1) 
Page 26 of 438 

 Test 3: Are variances adequately modeled? (A2 vs. A3) 

 Test 4: Does Model 2 fit the data? (A3 vs. 2) 

 Test 5a: Does Model 3 fit the data? (A3 vs 3) 

 Test 5b: Is Model 3 better than Model 2? (3 vs. 2) 

 Test 6a: Does Model 4 fit the data? (A3 vs 4) 

 Test 6b: Is Model 4 better than Model 2? (4 vs. 2) 

 Test 7a: Does Model 5 fit the data? (A3 vs 5) 

 Test 7b: Is Model 5 better than Model 3? (5 vs. 3) 

 Test 7c: Is Model 5 better than Model 4? (5 vs. 4) 

 Tests of Interest 

 Test -2*log(Likelihood Ratio) D. F. p-value 

 -------- ------------------------ ------ -------------- 

 Test 1 42.01 8 < 0.0001 

 Test 2 12.24 4 0.01567 

 Test 3 11.04 3 0.01149 

 Test 4 6.778 3 0.07933 

 Test 5a 228.2 3 < 0.0001 

 Test 5b -221.5 0 N/A 

 Test 6a 3.131 2 0.209 

 Test 6b 3.647 1 0.05617 

 Test 7a 1.371 1 0.2417 

 Test 7b 226.9 2 < 0.0001 

 Test 7c 1.76 1 0.1846 

 The p-value for Test 1 is less than .05. There appears to be a 

 difference between response and/or variances among the dose 

 levels, it seems appropriate to model the data. 

 The p-value for Test 2 is less than .1. A non-homogeneous 

 variance model appears to be appropriate. 

 The p-value for Test 3 is less than .1. You may want to 

 consider a different variance model. 

 The p-value for Test 4 is less than .1. Model 2 may not adequately 

 describe the data; you may want to consider another model. 

 The p-value for Test 5a is less than .1. Model 3 may not adequately 

 describe the data; you may want to consider another model. 

 Degrees of freedom for Test 5b are less than or equal to 0. 

 The Chi-Square test for fit is not valid. 

 The p-value for Test 6a is greater than .1. Model 4 seems 

 to adequately describe the data. 

 The p-value for Test 6b is greater than .05. Model 4 does 

 not seem to fit the data better than Model 2. 

 The p-value for Test 7a is greater than .1. Model 5 seems 

 to adequately describe the data. 

 The p-value for Test 7b is less than .05. Model 5 appears 

 to fit the data better than Model 3. 

 The p-value for Test 7c is greater than .05. Model 5 does 
Page 27 of 438 

 not seem to fit the data better than Model 4. 

 Benchmark Dose Computations: 

 Specified Effect = 0.100000 

 Risk Type = Relative deviation 

 Confidence Level = 0.950000 

 BMD and BMDL by Model 

 Model BMD BMDL 

 ------- ------------ ---------- 

 2 1.04023 0.817659 

 3 0.87389 Bad completion 

 4 0.104052 0.0512996 

 5 0.276846 0.0684109 

 1000

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dose

Exponential Model 2 with 0.95 Confidence Level

10:27 07/26 2012

BMDL BMD

 

Exponential
Page 28 of 438 

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-1 0 1 2 3 4 5

Mean Response

dose

Exponential Model 3 with 0.95 Confidence Level

10:27 07/26 2012

BMDL BMD

 

Exponential

 1000

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Mean Response

dose

Exponential Model 4 with 0.95 Confidence Level

10:27 07/26 2012

BMDL BMD

 

Exponential
Page 29 of 438 

Female PND8 RBC ChE Inhibition

 ==================================================================== 

 Exponential Model. (Version: 1.7; Date: 12/10/2009) 

 Input Data File: C:/Documents and Settings/jliccion/My 

Documents/BMDS220/Data/exp_UntitledData_Setting.(d) 

 Gnuplot Plotting File: 

 Thu Jul 26 10:10:05 2012 

 ==================================================================== 

 BMDS Model Run 

~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ 

 

 The form of the response function by Model: 

 Model 2: Y[dose] = a * exp{sign * b * dose} 

 Model 3: Y[dose] = a * exp{sign * (b * dose)^d} 

 Model 4: Y[dose] = a * [c-(c-1) * exp{-b * dose}] 

 Model 5: Y[dose] = a * [c-(c-1) * exp{-(b * dose)^d}] 

 Note: Y[dose] is the median response for exposure = dose; 

 sign = +1 for increasing trend in data; 

 sign = -1 for decreasing trend. 

 Model 2 is nested within Models 3 and 4. 

 Model 3 is nested within Model 5. 

 Model 4 is nested within Model 5. 

 1000

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Exponential Model 5 with 0.95 Confidence Level

10:27 07/26 2012

BMDL BMD

 

Exponential
Page 30 of 438 

 Dependent variable = Mean 

 Independent variable = Dose 

 Data are assumed to be distributed: normally 

 Variance Model: exp(lnalpha +rho *ln(Y[dose])) 

 rho is set to 0. 

 A constant variance model is fit. 

 Total number of dose groups = 5 

 Total number of records with missing values = 0 

 Maximum number of iterations = 250 

 Relative Function Convergence has been set to: 1e-008 

 Parameter Convergence has been set to: 1e-008 

 MLE solution provided: Exact 

 Initial Parameter Values 

 Variable Model 2 Model 3 Model 4 Model 5 

 -------- ------- ------- ------- ------- 

 lnalpha 12.5207 12.5207 12.5207 12.5207 

 rho(S) 0 0 0 0 

 a 2008.24 2008.24 3503.85 3503.85 

 b 0.124795 0.124795 0.714306 0.714306 

 c -- -- 0.42946 

0.42946 

 d -- 1 -- 1 

 (S) = Specified 

 Parameter Estimates by Model 

 Variable Model 2 Model 3 Model 4 Model 5 

 -------- ------- ------- ------- ------- 

 lnalpha 13.0488 13.0488 12.6189 12.546 

 rho 0 0 0 0 

 a 2892.16 2892.16 3339.12 3245 

 b 0.159702 0.159702 1.87191 3.25902 

 c -- -- 0.462783 0.516333 

 d -- 1 -- 16.726 

 Table of Stats From Input Data 

 Dose N Obs Mean Obs Std Dev 

 ----- --- ---------- ------------- 

 0 5 3153 486 

 0.1 5 3337 531 

 0.3 5 2466 722 

 1 5 1771 495 

 5 5 1580 654 

 Estimated Values of Interest 

 Model Dose Est Mean Est Std Scaled Residual 

 ------- ------ ---------- --------- ---------------- 

 2 0 2892 681.6 0.8557 

 0.1 2846 681.6 1.61 
Page 31 of 438 

 0.3 2757 681.6 -0.9543 

 1 2465 681.6 -2.278 

 5 1301 681.6 0.9138 

 3 0 2892 681.6 0.8557 

 0.1 2846 681.6 1.61 

 0.3 2757 681.6 -0.9543 

 1 2465 681.6 -2.278 

 5 1301 681.6 0.9138 

 4 0 3339 549.7 -0.757 

 0.1 3033 549.7 1.237 

 0.3 2568 549.7 -0.4162 

 1 1821 549.7 -0.2043 

 5 1545 549.7 0.1406 

 5 0 3245 530.1 -0.3881 

 0.1 3245 530.1 0.3881 

 0.3 2466 530.1 -1.255e-008 

 1 1676 530.1 0.4029 

 5 1676 530.1 -0.4029 

 Other models for which likelihoods are calculated: 

 Model A1: Yij = Mu(i) + e(ij) 

 Var{e(ij)} = Sigma^2 

 Model A2: Yij = Mu(i) + e(ij) 

 Var{e(ij)} = Sigma(i)^2 

 Model A3: Yij = Mu(i) + e(ij) 

 Var{e(ij)} = exp(lalpha + log(mean(i)) * rho) 

 Model R: Yij = Mu + e(i) 

 Var{e(ij)} = Sigma^2 

 Likelihoods of Interest 

 Model Log(likelihood) DF AIC 

 ------- ----------------- ---- ------------ 

 A1 -169.0087 6 350.0174 

 A2 -168.364 10 356.728 

 A3 -169.0087 6 350.0174 

 R -181.986 2 367.972 

 2 -175.6102 3 357.2203 

 3 -175.6102 3 357.2203 

 4 -170.2361 4 348.4721 

 5 -169.3256 5 348.6512 

 Additive constant for all log-likelihoods = -22.97. This constant added to the 

 above values gives the log-likelihood including the term that does not 

 depend on the model parameters. 

 Explanation of Tests 

 Test 1: Does response and/or variances differ among Dose levels? (A2 vs. R) 

 Test 2: Are Variances Homogeneous? (A2 vs. A1) 

 Test 3: Are variances adequately modeled? (A2 vs. A3) 

 Test 4: Does Model 2 fit the data? (A3 vs. 2) 

 Test 5a: Does Model 3 fit the data? (A3 vs 3) 
Page 32 of 438 

 Test 5b: Is Model 3 better than Model 2? (3 vs. 2) 

 Test 6a: Does Model 4 fit the data? (A3 vs 4) 

 Test 6b: Is Model 4 better than Model 2? (4 vs. 2) 

 Test 7a: Does Model 5 fit the data? (A3 vs 5) 

 Test 7b: Is Model 5 better than Model 3? (5 vs. 3) 

 Test 7c: Is Model 5 better than Model 4? (5 vs. 4) 

 Tests of Interest 

 Test -2*log(Likelihood Ratio) D. F. p-value 

 -------- ------------------------ ------ -------------- 

 Test 1 27.24 8 0.0006416 

 Test 2 1.289 4 0.8632 

 Test 3 1.289 4 0.8632 

 Test 4 13.2 3 0.004218 

 Test 5a 13.2 3 0.004218 

 Test 5b -5.19e-011 0 N/A 

 Test 6a 2.455 2 0.2931 

 Test 6b 10.75 1 0.001044 

 Test 7a 0.6338 1 0.426 

 Test 7b 12.57 2 0.001865 

 Test 7c 1.821 1 0.1772 

 The p-value for Test 1 is less than .05. There appears to be a 

 difference between response and/or variances among the dose 

 levels, it seems appropriate to model the data. 

 The p-value for Test 2 is greater than .1. A homogeneous 

 variance model appears to be appropriate here. 

 The p-value for Test 3 is greater than .1. The modeled 

 variance appears to be appropriate here. 

 The p-value for Test 4 is less than .1. Model 2 may not adequately 

 describe the data; you may want to consider another model. 

 The p-value for Test 5a is less than .1. Model 3 may not adequately 

 describe the data; you may want to consider another model. 

 Degrees of freedom for Test 5b are less than or equal to 0. 

 The Chi-Square test for fit is not valid. 

 The p-value for Test 6a is greater than .1. Model 4 seems 

 to adequately describe the data. 

 The p-value for Test 6b is less than .05. Model 4 appears 

 to fit the data better than Model 2. 

 The p-value for Test 7a is greater than .1. Model 5 seems 

 to adequately describe the data. 

 The p-value for Test 7b is less than .05. Model 5 appears 

 to fit the data better than Model 3. 

 The p-value for Test 7c is greater than .05. Model 5 does 

 not seem to fit the data better than Model 4. 

 Benchmark Dose Computations: 
Page 33 of 438 

 Specified Effect = 0.100000 

 Risk Type = Relative deviation 

 Confidence Level = 0.950000 

 BMD and BMDL by Model 

 Model BMD BMDL 

 ------- ------------ ---------- 

 2 0.659733 0.381932 

 3 0.659731 0.381932 

 4 0.110034 0.0586555 

 5 0.281148 0.076904 

 500

 1000

 1500

 2000

 2500

 3000

 3500

 4000

 0 1 2 3 4 5

Mean Response

dose

Exponential Model 2 with 0.95 Confidence Level

10:10 07/26 2012

BMDL BMD

 

Exponential
Page 34 of 438 

 500

 1000

 1500

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 2500

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 0 1 2 3 4 5

Mean Response

dose

Exponential Model 3 with 0.95 Confidence Level

10:10 07/26 2012

BMDL BMD

 

Exponential

 500

 1000

 1500

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 0 1 2 3 4 5

Mean Response

dose

Exponential Model 4 with 0.95 Confidence Level

10:10 07/26 2012

BMDL BMD

 

Exponential
Page 35 of 438 

Male PND15 Brain ChE Inhibition

==================================================================

== 

 Exponential Model. (Version: 1.7; Date: 12/10/2009) 

 Input Data File: C:/Usepa/BMDS212/Data/exp_Testrunscontinuous_Setting.(d) 

 Gnuplot Plotting File: 

 Fri Feb 04 13:02:01 2011 

==================================================================

== 

 BMDS Model Run 

~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

~~~ 

 

 The form of the response function by Model: 

 Model 2: Y[dose] = a * exp{sign * b * dose} 

 Model 3: Y[dose] = a * exp{sign * (b * dose)^d} 

 Model 4: Y[dose] = a * [c-(c-1) * exp{-b * dose}] 

 500

 1000

 1500

 2000

 2500

 3000

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 0 1 2 3 4 5

Mean Response

dose

Exponential Model 5 with 0.95 Confidence Level

10:10 07/26 2012

BMDL BMD

 

Exponential
Page 36 of 438 

 Model 5: Y[dose] = a * [c-(c-1) * exp{-(b * dose)^d}] 

 Note: Y[dose] is the median response for exposure = dose; 

 sign = +1 for increasing trend in data; 

 sign = -1 for decreasing trend. 

 Model 2 is nested within Models 3 and 4. 

 Model 3 is nested within Model 5. 

 Model 4 is nested within Model 5. 

 Dependent variable = Mean 

 Independent variable = Dose 

 Data are assumed to be distributed: normally 

 Variance Model: exp(lnalpha +rho *ln(Y[dose])) 

 The variance is to be modeled as Var(i) = exp(lalpha + log(mean(i)) * rho) 

 Total number of dose groups = 5 

 Total number of records with missing values = 0 

 Maximum number of iterations = 250 

 Relative Function Convergence has been set to: 1e-008 

 Parameter Convergence has been set to: 1e-008 

 MLE solution provided: Exact 

 Initial Parameter Values 

 Variable Model 2 Model 3 Model 4 Model 5 

 -------- ------- ------- ------- ------- 

 lnalpha -5.073 -5.073 -5.073 -5.073 

 rho 2.39751 2.39751 2.39751 2.39751 

 a 1.5058 1.36944 4.9245 4.9245 

 b 0.316827 0.04148 0.982532 0.982532 

 c -- -- 0.148915 0.148915 

 d -- 2 -- 1 

 Parameter Estimates by Model 

 Variable Model 2 Model 3 Model 4 Model 5 

 -------- ------- ------- ------- ------- 

 lnalpha -5.17757 -5.17758 -4.77837 -4.77837 

 rho 4.88475 4.88475 2.37197 2.37197 

 a 3.18867 3.18867 4.43386 4.43386 
Page 37 of 438 

 b 0.28475 0.28475 1.63515 1.63515 

 c -- -- 0.172746 0.172746 

 d -- 1 -- 1 

 Table of Stats From Input Data 

 Dose N Obs Mean Obs Std Dev 

 ----- --- ---------- ------------- 

 0 5 4.69 0.38 

 0.1 5 3.73 0.6 

 0.3 5 2.95 0.18 

 1 5 1.48 0.25 

 5 5 0.77 0.04 

 Estimated Values of Interest 

 Model Dose Est Mean Est Std Scaled Residual 

 ------- ------ ---------- --------- ---------------- 

 2 0 3.189 1.276 2.632 

 0.1 3.099 1.19 1.185 

 0.3 2.928 1.035 0.04839 

 1 2.399 0.6363 -3.228 

 5 0.7679 0.0394 0.1213 

 3 0 3.189 1.276 2.632 

 0.1 3.099 1.19 1.185 

 0.3 2.928 1.035 0.04839 

 1 2.399 0.6363 -3.228 

 5 0.7679 0.0394 0.1213 

 4 0 4.434 0.5364 1.068 

 0.1 3.881 0.4579 -0.7352 

 0.3 3.012 0.3391 -0.4074 

 1 1.481 0.1461 -0.01373 

 5 0.767 0.06695 0.1014 

 5 0 4.434 0.5364 1.068 

 0.1 3.881 0.4579 -0.7352 

 0.3 3.012 0.3391 -0.4074 

 1 1.481 0.1461 -0.01373 

 5 0.767 0.06695 0.1014 

 Other models for which likelihoods are calculated: 

 Model A1: Yij = Mu(i) + e(ij) 
Page 38 of 438 

 Var{e(ij)} = Sigma^2 

 Model A2: Yij = Mu(i) + e(ij) 

 Var{e(ij)} = Sigma(i)^2 

 Model A3: Yij = Mu(i) + e(ij) 

 Var{e(ij)} = exp(lalpha + log(mean(i)) * rho) 

 Model R: Yij = Mu + e(i) 

 Var{e(ij)} = Sigma^2 

 Likelihoods of Interest 

 Model Log(likelihood) DF AIC 

 ------- ----------------- ---- ------------ 

 A1 16.77385 6 -21.54771 

 A2 29.28119 10 -38.56237 

 A3 23.94041 7 -33.88081 

 R -22.09697 2 48.19393 

 2 3.669795 4 0.6604104 

 3 3.669795 4 0.6604104 

 4 23.06442 5 -36.12885 

 5 23.06442 5 -36.12885 

 Additive constant for all log-likelihoods = -22.97. This constant added to the 

 above values gives the log-likelihood including the term that does not 

 depend on the model parameters. 

 Explanation of Tests 

 Test 1: Does response and/or variances differ among Dose levels? (A2 vs. R) 

 Test 2: Are Variances Homogeneous? (A2 vs. A1) 

 Test 3: Are variances adequately modeled? (A2 vs. A3) 

 Test 4: Does Model 2 fit the data? (A3 vs. 2) 

 Test 5a: Does Model 3 fit the data? (A3 vs 3) 

 Test 5b: Is Model 3 better than Model 2? (3 vs. 2) 

 Test 6a: Does Model 4 fit the data? (A3 vs 4) 

 Test 6b: Is Model 4 better than Model 2? (4 vs. 2) 

 Test 7a: Does Model 5 fit the data? (A3 vs 5) 

 Test 7b: Is Model 5 better than Model 3? (5 vs. 3) 
Page 39 of 438 

 Test 7c: Is Model 5 better than Model 4? (5 vs. 4) 

 Tests of Interest 

 Test -2*log(Likelihood Ratio) D. F. p-value 

 -------- ------------------------ ------ -------------- 

 Test 1 102.8 8 < 0.0001 

 Test 2 25.01 4 < 0.0001 

 Test 3 10.68 3 0.01358 

 Test 4 40.54 3 < 0.0001 

 Test 5a 40.54 3 < 0.0001 

 Test 5b -1.863e-012 0 N/A 

 Test 6a 1.752 2 0.4165 

 Test 6b 38.79 1 < 0.0001 

 Test 7a 1.752 2 0.4165 

 Test 7b 38.79 1 < 0.0001 

 Test 7c 0 0 N/A 

 The p-value for Test 1 is less than .05. There appears to be a 

 difference between response and/or variances among the dose 

 levels, it seems appropriate to model the data. 

 The p-value for Test 2 is less than .1. A non-homogeneous 

 variance model appears to be appropriate. 

 The p-value for Test 3 is less than .1. You may want to 

 consider a different variance model. 

 The p-value for Test 4 is less than .1. Model 2 may not adequately 

 describe the data; you may want to consider another model. 

 The p-value for Test 5a is less than .1. Model 3 may not adequately 

 describe the data; you may want to consider another model. 

 Degrees of freedom for Test 5b are less than or equal to 0. 

 The Chi-Square test for fit is not valid. 

 The p-value for Test 6a is greater than .1. Model 4 seems 

 to adequately describe the data. 

 The p-value for Test 6b is less than .05. Model 4 appears 

 to fit the data better than Model 2. 

 The p-value for Test 7a is greater than .1. Model 5 seems 
Page 40 of 438 

 to adequately describe the data. 

 The p-value for Test 7b is less than .05. Model 5 appears 

 to fit the data better than Model 3. 

 Degrees of freedom for Test 7c are less than or equal to 0. 

 The Chi-Square test for fit is not valid. 

 Benchmark Dose Computations: 

 Specified Effect = 0.100000 

 Risk Type = Relative deviation 

 Confidence Level = 0.950000 

 BMD and BMDL by Model 

 Model BMD BMDL 

 ------- ------------ ---------- 

 2 0.370011 0.335325 

 3 0.370011 0.335325 

 4 0.0787916 0.0696334 

 5 0.0787916 0.0696334 
Page 41 of 438 

 1

 2

 3

 4

 5

 0 1 2 3 4 5

Mean Response

dose

Exponential Model 2 with 0.95 Confidence Level

13:02 02/04 2011

BMDL BMD

 

Exponential

 1

 2

 3

 4

 5

 0 1 2 3 4 5

Mean Response

dose

Exponential Model 3 with 0.95 Confidence Level

13:02 02/04 2011

BMDL BMD

 

Exponential
Page 42 of 438 

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Page 43 of 438 

Female PND15 Brain ChE Inhibition

 ==================================================================== 

 Exponential Model. (Version: 1.7; Date: 12/10/2009) 

 Input Data File: C:/Documents and Settings/jliccion/My 

Documents/BMDS220/Data/exp_UntitledData_Setting.(d) 

 Gnuplot Plotting File: 

 Thu Jul 26 12:45:32 2012 

 ==================================================================== 

 BMDS Model Run 

~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ 

 

 The form of the response function by Model: 

 Model 2: Y[dose] = a * exp{sign * b * dose} 

 Model 3: Y[dose] = a * exp{sign * (b * dose)^d} 

 Model 4: Y[dose] = a * [c-(c-1) * exp{-b * dose}] 

 Model 5: Y[dose] = a * [c-(c-1) * exp{-(b * dose)^d}] 

 Note: Y[dose] is the median response for exposure = dose; 

 sign = +1 for increasing trend in data; 

 sign = -1 for decreasing trend. 

 Model 2 is nested within Models 3 and 4. 

 Model 3 is nested within Model 5. 

 Model 4 is nested within Model 5. 

 Dependent variable = Mean 

 Independent variable = Dose 

 Data are assumed to be distributed: normally 

 Variance Model: exp(lnalpha +rho *ln(Y[dose])) 

 rho is set to 0. 

 A constant variance model is fit. 

 Total number of dose groups = 5 

 Total number of records with missing values = 0 

 Maximum number of iterations = 250 

 Relative Function Convergence has been set to: 1e-008 

 Parameter Convergence has been set to: 1e-008 

 MLE solution provided: Exact 

 Initial Parameter Values 

 Variable Model 2 Model 3 Model 4 Model 5 

 -------- ------- ------- ------- ------- 

 lnalpha -2.93219 -2.93219 -2.93219 -2.93219 

 rho(S) 0 0 0 0 

 a 1.45976 1.45976 5.145 5.145 

 b 0.306168 0.306168 1.00146 1.00146 

 c -- -- 0.142533 

0.142533 

 d -- 1 -- 1 

 (S) = Specified 
Page 44 of 438 

 Parameter Estimates by Model 

 Variable Model 2 Model 3 Model 4 Model 5 

 -------- ------- ------- ------- ------- 

 lnalpha -1.19866 -1.19866 -2.08331 -2.08331 

 rho 0 0 0 0 

 a 4.40562 4.40562 4.65255 4.65255 

 b 1.41217 1.41217 2.83215 2.83215 

 c -- -- 0.206291 0.206291 

 d -- 1 -- 1 

 Table of Stats From Input Data 

 Dose N Obs Mean Obs Std Dev 

 ----- --- ---------- ------------- 

 0 5 4.9 0.3 

 0.1 5 3.31 0.45 

 0.3 5 2.69 0.1 

 1 5 1.4 0.17 

 5 5 0.77 0.04 

 Estimated Values of Interest 

 Model Dose Est Mean Est Std Scaled Residual 

 ------- ------ ---------- --------- ---------------- 

 2 0 4.406 0.5492 2.013 

 0.1 3.825 0.5492 -2.099 

 0.3 2.884 0.5492 -0.7905 

 1 1.073 0.5492 1.33 

 5 0.00378 0.5492 3.12 

 3 0 4.406 0.5492 2.013 

 0.1 3.825 0.5492 -2.099 

 0.3 2.884 0.5492 -0.7905 

 1 1.073 0.5492 1.33 

 5 0.00378 0.5492 3.12 

 4 0 4.653 0.3529 1.568 

 0.1 3.742 0.3529 -2.736 

 0.3 2.539 0.3529 0.9589 

 1 1.177 0.3529 1.412 

 5 0.9598 0.3529 -1.203 

 5 0 4.653 0.3529 1.568 

 0.1 3.742 0.3529 -2.736 

 0.3 2.539 0.3529 0.9589 

 1 1.177 0.3529 1.412 

 5 0.9598 0.3529 -1.203 

 Other models for which likelihoods are calculated: 

 Model A1: Yij = Mu(i) + e(ij) 

 Var{e(ij)} = Sigma^2 

 Model A2: Yij = Mu(i) + e(ij) 

 Var{e(ij)} = Sigma(i)^2 

 Model A3: Yij = Mu(i) + e(ij) 

 Var{e(ij)} = exp(lalpha + log(mean(i)) * rho) 
Page 45 of 438 

 Model R: Yij = Mu + e(i) 

 Var{e(ij)} = Sigma^2 

 Likelihoods of Interest 

 Model Log(likelihood) DF AIC 

 ------- ----------------- ---- ------------ 

 A1 24.15243 6 -36.30486 

 A2 36.76879 10 -53.53757 

 A3 24.15243 6 -36.30486 

 R -22.1916 2 48.3832 

 2 2.483245 3 1.033511 

 3 2.483245 3 1.033511 

 4 13.54142 4 -19.08285 

 5 13.54142 4 -19.08285 

 Additive constant for all log-likelihoods = -22.97. This constant added to the 

 above values gives the log-likelihood including the term that does not 

 depend on the model parameters. 

 Explanation of Tests 

 Test 1: Does response and/or variances differ among Dose levels? (A2 vs. R) 

 Test 2: Are Variances Homogeneous? (A2 vs. A1) 

 Test 3: Are variances adequately modeled? (A2 vs. A3) 

 Test 4: Does Model 2 fit the data? (A3 vs. 2) 

 Test 5a: Does Model 3 fit the data? (A3 vs 3) 

 Test 5b: Is Model 3 better than Model 2? (3 vs. 2) 

 Test 6a: Does Model 4 fit the data? (A3 vs 4) 

 Test 6b: Is Model 4 better than Model 2? (4 vs. 2) 

 Test 7a: Does Model 5 fit the data? (A3 vs 5) 

 Test 7b: Is Model 5 better than Model 3? (5 vs. 3) 

 Test 7c: Is Model 5 better than Model 4? (5 vs. 4) 

 Tests of Interest 

 Test -2*log(Likelihood Ratio) D. F. p-value 

 -------- ------------------------ ------ -------------- 

 Test 1 117.9 8 < 0.0001 

 Test 2 25.23 4 < 0.0001 

 Test 3 25.23 4 < 0.0001 

 Test 4 43.34 3 < 0.0001 

 Test 5a 43.34 3 < 0.0001 

 Test 5b -7.461e-014 0 N/A 

 Test 6a 21.22 2 < 0.0001 

 Test 6b 22.12 1 < 0.0001 

 Test 7a 21.22 2 < 0.0001 

 Test 7b 22.12 1 < 0.0001 

 Test 7c -1.727e-012 0 N/A 

 The p-value for Test 1 is less than .05. There appears to be a 

 difference between response and/or variances among the dose 

 levels, it seems appropriate to model the data. 

 The p-value for Test 2 is less than .1. Consider running 
Page 46 of 438 

 a non-homogeneous variance model. 

 The p-value for Test 3 is less than .1. You may want to 

 consider a different variance model. 

 The p-value for Test 4 is less than .1. Model 2 may not adequately 

 describe the data; you may want to consider another model. 

 The p-value for Test 5a is less than .1. Model 3 may not adequately 

 describe the data; you may want to consider another model. 

 Degrees of freedom for Test 5b are less than or equal to 0. 

 The Chi-Square test for fit is not valid. 

 The p-value for Test 6a is less than .1. Model 4 may not adequately 

 describe the data; you may want to consider another model. 

 The p-value for Test 6b is less than .05. Model 4 appears 

 to fit the data better than Model 2. 

 The p-value for Test 7a is less than .1. Model 5 may not adequately 

 describe the data; you may want to consider another model. 

 The p-value for Test 7b is less than .05. Model 5 appears 

 to fit the data better than Model 3. 

 Degrees of freedom for Test 7c are less than or equal to 0. 

 The Chi-Square test for fit is not valid. 

 Benchmark Dose Computations: 

 Specified Effect = 0.100000 

 Risk Type = Relative deviation 

 Confidence Level = 0.950000 

 BMD and BMDL by Model 

 Model BMD BMDL 

 ------- ------------ ---------- 

 2 0.0746088 0.0565782 

 3 0.0746088 0.0565782 

 4 0.0475484 0.0368912 

 5 0.0475484 0.0368912 
Page 47 of 438 

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Exponential
Page 49 of 438 

Male PND15 RBC ChE Inhibition

 ==================================================================== 

 Exponential Model. (Version: 1.7; Date: 12/10/2009) 

 Input Data File: C:/Documents and Settings/jliccion/My 

Documents/BMDS220/Data/exp_UntitledData_Setting.(d) 

 Gnuplot Plotting File: 

 Thu Jul 26 12:36:59 2012 

 ==================================================================== 

 BMDS Model Run 

~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ 

 

 The form of the response function by Model: 

 Model 2: Y[dose] = a * exp{sign * b * dose} 

 Model 3: Y[dose] = a * exp{sign * (b * dose)^d} 

 Model 4: Y[dose] = a * [c-(c-1) * exp{-b * dose}] 

 Model 5: Y[dose] = a * [c-(c-1) * exp{-(b * dose)^d}] 

 Note: Y[dose] is the median response for exposure = dose; 

 sign = +1 for increasing trend in data; 

 sign = -1 for decreasing trend. 

 Model 2 is nested within Models 3 and 4. 

 Model 3 is nested within Model 5. 

 Model 4 is nested within Model 5. 

 Dependent variable = Mean 

 Independent variable = Dose 

 Data are assumed to be distributed: normally 

 Variance Model: exp(lnalpha +rho *ln(Y[dose])) 

 The variance is to be modeled as Var(i) = exp(lalpha + log(mean(i)) * rho) 

 Total number of dose groups = 5 

 Total number of records with missing values = 0 

 Maximum number of iterations = 250 

 Relative Function Convergence has been set to: 1e-008 

 Parameter Convergence has been set to: 1e-008 

 MLE solution provided: Exact 

 Initial Parameter Values 

 Variable Model 2 Model 3 Model 4 Model 5 

 -------- ------- ------- ------- ------- 

 lnalpha 1.44121 1.44121 1.44121 1.44121 

 rho 1.27633 1.27633 1.27633 1.27633 

 a 1624.28 1624.28 3307.5 3307.5 

 b 0.152367 0.152367 0.765486 0.765486 

 c -- -- 0.337185 

0.337185 

 d -- 1 -- 1 

 Parameter Estimates by Model 

 Variable Model 2 Model 3 Model 4 Model 5 
Page 50 of 438 

 -------- ------- ------- ------- ------- 

 lnalpha -14.0995 -14.0995 -9.83321 -9.83319 

 rho 3.44246 3.44245 2.81245 2.81245 

 a 2408.97 2408.97 2753.77 2753.77 

 b 0.147786 0.147786 1.27794 1.27794 

 c -- -- 0.429066 0.429066 

 d -- 1 -- 1 

 Table of Stats From Input Data 

 Dose N Obs Mean Obs Std Dev 

 ----- --- ---------- ------------- 

 0 5 3150 285 

 0.1 5 2361 304 

 0.3 5 2006 527 

 1 5 1716 143 

 5 5 1171 184 

 Estimated Values of Interest 

 Model Dose Est Mean Est Std Scaled Residual 

 ------- ------ ---------- --------- ---------------- 

 2 0 2409 574.4 2.885 

 0.1 2374 560 -0.05044 

 0.3 2305 532.2 -1.254 

 1 2078 445.4 -1.817 

 5 1151 161 0.2835 

 3 0 2409 574.4 2.885 

 0.1 2374 560 -0.05044 

 0.3 2305 532.2 -1.254 

 1 2078 445.4 -1.817 

 5 1151 161 0.2835 

 4 0 2754 503.6 1.759 

 0.1 2565 455.8 -1.002 

 0.3 2253 379.8 -1.455 

 1 1620 238.7 0.9031 

 5 1184 153.7 -0.1919 

 5 0 2754 503.6 1.759 

 0.1 2565 455.8 -1.002 

 0.3 2253 379.8 -1.455 

 1 1620 238.7 0.9031 

 5 1184 153.7 -0.1919 

 Other models for which likelihoods are calculated: 

 Model A1: Yij = Mu(i) + e(ij) 

 Var{e(ij)} = Sigma^2 

 Model A2: Yij = Mu(i) + e(ij) 

 Var{e(ij)} = Sigma(i)^2 

 Model A3: Yij = Mu(i) + e(ij) 

 Var{e(ij)} = exp(lalpha + log(mean(i)) * rho) 

 Model R: Yij = Mu + e(i) 

 Var{e(ij)} = Sigma^2 

 Likelihoods of Interest 
Page 51 of 438 

 Model Log(likelihood) DF AIC 

 ------- ----------------- ---- ------------ 

 A1 -153.7633 6 319.5267 

 A2 -148.7832 10 317.5664 

 A3 -153.0341 7 320.0682 

 R -176.9844 2 357.9688 

 2 -163.1947 4 334.3894 

 3 -163.1947 4 334.3894 

 4 -156.4676 5 322.9352 

 5 -156.4676 5 322.9352 

 Additive constant for all log-likelihoods = -22.97. This constant added to the 

 above values gives the log-likelihood including the term that does not 

 depend on the model parameters. 

 Explanation of Tests 

 Test 1: Does response and/or variances differ among Dose levels? (A2 vs. R) 

 Test 2: Are Variances Homogeneous? (A2 vs. A1) 

 Test 3: Are variances adequately modeled? (A2 vs. A3) 

 Test 4: Does Model 2 fit the data? (A3 vs. 2) 

 Test 5a: Does Model 3 fit the data? (A3 vs 3) 

 Test 5b: Is Model 3 better than Model 2? (3 vs. 2) 

 Test 6a: Does Model 4 fit the data? (A3 vs 4) 

 Test 6b: Is Model 4 better than Model 2? (4 vs. 2) 

 Test 7a: Does Model 5 fit the data? (A3 vs 5) 

 Test 7b: Is Model 5 better than Model 3? (5 vs. 3) 

 Test 7c: Is Model 5 better than Model 4? (5 vs. 4) 

 Tests of Interest 

 Test -2*log(Likelihood Ratio) D. F. p-value 

 -------- ------------------------ ------ -------------- 

 Test 1 56.4 8 < 0.0001 

 Test 2 9.96 4 0.0411 

 Test 3 8.502 3 0.0367 

 Test 4 20.32 3 0.0001456 

 Test 5a 20.32 3 0.0001456 

 Test 5b -3.428e-011 0 N/A 

 Test 6a 6.867 2 0.03227 

 Test 6b 13.45 1 0.0002445 

 Test 7a 6.867 2 0.03227 

 Test 7b 13.45 1 0.0002445 

 Test 7c -1.012e-011 0 N/A 

 The p-value for Test 1 is less than .05. There appears to be a 

 difference between response and/or variances among the dose 

 levels, it seems appropriate to model the data. 

 The p-value for Test 2 is less than .1. A non-homogeneous 

 variance model appears to be appropriate. 

 The p-value for Test 3 is less than .1. You may want to 

 consider a different variance model. 
Page 52 of 438 

 The p-value for Test 4 is less than .1. Model 2 may not adequately 

 describe the data; you may want to consider another model. 

 The p-value for Test 5a is less than .1. Model 3 may not adequately 

 describe the data; you may want to consider another model. 

 Degrees of freedom for Test 5b are less than or equal to 0. 

 The Chi-Square test for fit is not valid. 

 The p-value for Test 6a is less than .1. Model 4 may not adequately 

 describe the data; you may want to consider another model. 

 The p-value for Test 6b is less than .05. Model 4 appears 

 to fit the data better than Model 2. 

 The p-value for Test 7a is less than .1. Model 5 may not adequately 

 describe the data; you may want to consider another model. 

 The p-value for Test 7b is less than .05. Model 5 appears 

 to fit the data better than Model 3. 

 Degrees of freedom for Test 7c are less than or equal to 0. 

 The Chi-Square test for fit is not valid. 

 Benchmark Dose Computations: 

 Specified Effect = 0.100000 

 Risk Type = Relative deviation 

 Confidence Level = 0.950000 

 BMD and BMDL by Model 

 Model BMD BMDL 

 ------- ------------ ---------- 

 2 0.712927 0.573232 

 3 0.712926 0.573232 

 4 0.150676 0.0933513 

 5 0.150676 0.0933513 
Page 53 of 438 

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Exponential Model 2 with 0.95 Confidence Level

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Exponential

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Exponential
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Exponential Model 4 with 0.95 Confidence Level

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Exponential

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Exponential
Page 55 of 438 

Female PND15 RBC ChE Inhibition

 ==================================================================== 

 Exponential Model. (Version: 1.7; Date: 12/10/2009) 

 Input Data File: C:/Documents and Settings/jliccion/My 

Documents/BMDS220/Data/exp_UntitledData_Setting.(d) 

 Gnuplot Plotting File: 

 Thu Jul 26 12:24:12 2012 

 ==================================================================== 

 BMDS Model Run 

~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ 

 

 The form of the response function by Model: 

 Model 2: Y[dose] = a * exp{sign * b * dose} 

 Model 3: Y[dose] = a * exp{sign * (b * dose)^d} 

 Model 4: Y[dose] = a * [c-(c-1) * exp{-b * dose}] 

 Model 5: Y[dose] = a * [c-(c-1) * exp{-(b * dose)^d}] 

 Note: Y[dose] is the median response for exposure = dose; 

 sign = +1 for increasing trend in data; 

 sign = -1 for decreasing trend. 

 Model 2 is nested within Models 3 and 4. 

 Model 3 is nested within Model 5. 

 Model 4 is nested within Model 5. 

 Dependent variable = Mean 

 Independent variable = Dose 

 Data are assumed to be distributed: normally 

 Variance Model: exp(lnalpha +rho *ln(Y[dose])) 

 The variance is to be modeled as Var(i) = exp(lalpha + log(mean(i)) * rho) 

 Total number of dose groups = 5 

 Total number of records with missing values = 0 

 Maximum number of iterations = 250 

 Relative Function Convergence has been set to: 1e-008 

 Parameter Convergence has been set to: 1e-008 

 MLE solution provided: Exact 

 Initial Parameter Values 

 Variable Model 2 Model 3 Model 4 Model 5 

 -------- ------- ------- ------- ------- 

 lnalpha 11.8959 11.8959 11.8959 11.8959 

 rho -0.129436 -0.129436 -0.129436 -0.129436 

 a 1685.7 1685.7 3106.95 3106.95 

 b 0.125024 0.125024 0.732633 0.732633 

 c -- -- 0.404623 

0.404623 

 d -- 1 -- 1 

 Parameter Estimates by Model 
Page 56 of 438 

 Variable Model 2 Model 3 Model 4 Model 5 

 -------- ------- ------- ------- ------- 

 lnalpha 18.115 18.115 -3.45217 -3.45217 

 rho -0.834683 -0.834683 1.92669 1.92669 

 a 2716.6 2716.6 2809.3 2809.3 

 b 0.605165 0.605165 2.02783 2.02783 

 c -- -- 0.470593 0.470593 

 d -- 1 -- 1 

 Table of Stats From Input Data 

 Dose N Obs Mean Obs Std Dev 

 ----- --- ---------- ------------- 

 0 5 2959 143 

 0.1 5 2318 505 

 0.3 5 2203 205 

 1 5 1519 246 

 5 5 1320 194 

 Estimated Values of Interest 

 Model Dose Est Mean Est Std Scaled Residual 

 ------- ------ ---------- --------- ---------------- 

 2 0 2717 316.6 1.712 

 0.1 2557 324.7 -1.647 

 0.3 2266 341.5 -0.4098 

 1 1483 407.5 0.1963 

 5 131.8 1119 2.374 

 3 0 2717 316.6 1.712 

 0.1 2557 324.7 -1.647 

 0.3 2266 341.5 -0.4098 

 1 1483 407.5 0.1963 

 5 131.8 1119 2.374 

 4 0 2809 373.7 0.8957 

 0.1 2536 338.7 -1.441 

 0.3 2131 286.4 0.5583 

 1 1518 206.5 0.0131 

 5 1322 180.8 -0.02589 

 5 0 2809 373.7 0.8957 

 0.1 2536 338.7 -1.441 

 0.3 2131 286.4 0.5583 

 1 1518 206.5 0.0131 

 5 1322 180.8 -0.02589 

 Other models for which likelihoods are calculated: 

 Model A1: Yij = Mu(i) + e(ij) 

 Var{e(ij)} = Sigma^2 

 Model A2: Yij = Mu(i) + e(ij) 

 Var{e(ij)} = Sigma(i)^2 

 Model A3: Yij = Mu(i) + e(ij) 

 Var{e(ij)} = exp(lalpha + log(mean(i)) * rho) 

 Model R: Yij = Mu + e(i) 

 Var{e(ij)} = Sigma^2 
Page 57 of 438 

 Likelihoods of Interest 

 Model Log(likelihood) DF AIC 

 ------- ----------------- ---- ------------ 

 A1 -151.3127 6 314.6255 

 A2 -146.1287 10 312.2574 

 A3 -150.9284 7 315.8567 

 R -174.1578 2 352.3157 

 2 -164.5195 4 337.0391 

 3 -164.5195 4 337.0391 

 4 -152.1701 5 314.3402 

 5 -152.1701 5 314.3402 

 Additive constant for all log-likelihoods = -22.97. This constant added to the 

 above values gives the log-likelihood including the term that does not 

 depend on the model parameters. 

 Explanation of Tests 

 Test 1: Does response and/or variances differ among Dose levels? (A2 vs. R) 

 Test 2: Are Variances Homogeneous? (A2 vs. A1) 

 Test 3: Are variances adequately modeled? (A2 vs. A3) 

 Test 4: Does Model 2 fit the data? (A3 vs. 2) 

 Test 5a: Does Model 3 fit the data? (A3 vs 3) 

 Test 5b: Is Model 3 better than Model 2? (3 vs. 2) 

 Test 6a: Does Model 4 fit the data? (A3 vs 4) 

 Test 6b: Is Model 4 better than Model 2? (4 vs. 2) 

 Test 7a: Does Model 5 fit the data? (A3 vs 5) 

 Test 7b: Is Model 5 better than Model 3? (5 vs. 3) 

 Test 7c: Is Model 5 better than Model 4? (5 vs. 4) 

 Tests of Interest 

 Test -2*log(Likelihood Ratio) D. F. p-value 

 -------- ------------------------ ------ -------------- 

 Test 1 56.06 8 < 0.0001 

 Test 2 10.37 4 0.03466 

 Test 3 9.599 3 0.0223 

 Test 4 27.18 3 < 0.0001 

 Test 5a 27.18 3 < 0.0001 

 Test 5b -3.411e-013 0 N/A 

 Test 6a 2.483 2 0.2889 

 Test 6b 24.7 1 < 0.0001 

 Test 7a 2.483 2 0.2889 

 Test 7b 24.7 1 < 0.0001 

 Test 7c -5.684e-014 0 N/A 

 The p-value for Test 1 is less than .05. There appears to be a 

 difference between response and/or variances among the dose 

 levels, it seems appropriate to model the data. 

 The p-value for Test 2 is less than .1. A non-homogeneous 

 variance model appears to be appropriate. 

 The p-value for Test 3 is less than .1. You may want to 
Page 58 of 438 

 consider a different variance model. 

 The p-value for Test 4 is less than .1. Model 2 may not adequately 

 describe the data; you may want to consider another model. 

 The p-value for Test 5a is less than .1. Model 3 may not adequately 

 describe the data; you may want to consider another model. 

 Degrees of freedom for Test 5b are less than or equal to 0. 

 The Chi-Square test for fit is not valid. 

 The p-value for Test 6a is greater than .1. Model 4 seems 

 to adequately describe the data. 

 The p-value for Test 6b is less than .05. Model 4 appears 

 to fit the data better than Model 2. 

 The p-value for Test 7a is greater than .1. Model 5 seems 

 to adequately describe the data. 

 The p-value for Test 7b is less than .05. Model 5 appears 

 to fit the data better than Model 3. 

 Degrees of freedom for Test 7c are less than or equal to 0. 

 The Chi-Square test for fit is not valid. 

 Benchmark Dose Computations: 

 Specified Effect = 0.100000 

 Risk Type = Relative deviation 

 Confidence Level = 0.950000 

 BMD and BMDL by Model 

 Model BMD BMDL 

 ------- ------------ ---------- 

 2 0.174102 0.120854 

 3 0.174102 0.120854 

 4 0.10324 0.0667855 

 5 0.10324 0.0667855 

 
Page 59 of 438 

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Exponential Model 2 with 0.95 Confidence Level

12:24 07/26 2012

BMDL BMD

 

Exponential
Page 60 of 438 

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Exponential Model 3 with 0.95 Confidence Level

12:24 07/26 2012

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Exponential

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Exponential Model 4 with 0.95 Confidence Level

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Exponential
Page 61 of 438 

Male PND22 Brain ChE Inhibition

 ==================================================================== 

 Exponential Model. (Version: 1.7; Date: 12/10/2009) 

 Input Data File: C:/Documents and Settings/jliccion/My 

Documents/BMDS220/Data/exp_UntitledData_Setting.(d) 

 Gnuplot Plotting File: 

 Thu Jul 26 12:00:50 2012 

 ==================================================================== 

 BMDS Model Run 

~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

~ 

 

 The form of the response function by Model: 

 Model 2: Y[dose] = a * exp{sign * b * dose} 

 Model 3: Y[dose] = a * exp{sign * (b * dose)^d} 

 Model 4: Y[dose] = a * [c-(c-1) * exp{-b * dose}] 

 Model 5: Y[dose] = a * [c-(c-1) * exp{-(b * dose)^d}] 

 Note: Y[dose] is the median response for exposure = dose; 

 1000

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dose

Exponential Model 5 with 0.95 Confidence Level

12:24 07/26 2012

BMDL BMD

 

Exponential
Page 62 of 438 

 sign = +1 for increasing trend in data; 

 sign = -1 for decreasing trend. 

 Model 2 is nested within Models 3 and 4. 

 Model 3 is nested within Model 5. 

 Model 4 is nested within Model 5. 

 Dependent variable = Mean 

 Independent variable = Dose 

 Data are assumed to be distributed: normally 

 Variance Model: exp(lnalpha +rho *ln(Y[dose])) 

 The variance is to be modeled as Var(i) = exp(lalpha + log(mean(i)) * rho) 

 Total number of dose groups = 5 

 Total number of records with missing values = 0 

 Maximum number of iterations = 250 

 Relative Function Convergence has been set to: 1e-008 

 Parameter Convergence has been set to: 1e-008 

 MLE solution provided: Exact 

 Initial Parameter Values 

 Variable Model 2 Model 3 Model 4 Model 5 

 -------- ------- ------- ------- ------- 

 lnalpha -4.35434 -4.35434 -4.35434 -4.35434 

 rho 1.6561 1.6561 1.6561 1.6561 

 a 2.15781 2.15781 5.439 5.439 

 b 0.283673 0.283673 0.889601 0.889601 

 c -- -- 0.203119 0.203119 

 d -- 1 -- 1 

 Parameter Estimates by Model 

 Variable Model 2 Model 3 Model 4 Model 5 

 -------- ------- ------- ------- ------- 

 lnalpha -3.36896 -3.36896 -4.15291 -4.26284 

 rho 2.12957 2.12957 1.75732 1.69146 

 a 4.53515 4.53515 5.37254 5.08566 

 b 0.283097 0.283097 1.254 1.17339 

 c -- -- 0.213796 0.229139 

 d -- 1 -- 1.49786 
Page 63 of 438 

 Table of Stats From Input Data 

 Dose N Obs Mean Obs Std Dev 

 ----- --- ---------- ------------- 

 0 5 5.18 0.31 

 0.1 5 4.79 0.69 

 0.3 5 4.38 0.48 

 1 5 2.28 0.11 

 5 5 1.16 0.18 

 Estimated Values of Interest 

 Model Dose Est Mean Est Std Scaled Residual 

 ------- ------ ---------- --------- ---------------- 

 2 0 4.535 0.928 1.554 

 0.1 4.409 0.9005 0.9472 

 0.3 4.166 0.8478 0.5647 

 1 3.417 0.6865 -3.703 

 5 1.101 0.2056 0.6399 

 3 0 4.535 0.928 1.554 

 0.1 4.409 0.9005 0.9472 

 0.3 4.166 0.8478 0.5647 

 1 3.417 0.6865 -3.703 

 5 1.101 0.2056 0.6399 

 4 0 5.373 0.5493 -0.7838 

 0.1 4.875 0.5043 -0.3757 

 0.3 4.048 0.4283 1.732 

 1 2.354 0.266 -0.6218 

 5 1.157 0.1425 0.05304 

 5 0 5.086 0.4696 0.4492 

 0.1 4.931 0.4574 -0.6868 

 0.3 4.345 0.4111 0.1891 

 1 2.266 0.237 0.1359 

 5 1.165 0.1351 -0.08818 

 Other models for which likelihoods are calculated: 

 Model A1: Yij = Mu(i) + e(ij) 

 Var{e(ij)} = Sigma^2 

 Model A2: Yij = Mu(i) + e(ij) 
Page 64 of 438 

 Var{e(ij)} = Sigma(i)^2 

 Model A3: Yij = Mu(i) + e(ij) 

 Var{e(ij)} = exp(lalpha + log(mean(i)) * rho) 

 Model R: Yij = Mu + e(i) 

 Var{e(ij)} = Sigma^2 

 Likelihoods of Interest 

 Model Log(likelihood) DF AIC 

 ------- ----------------- ---- ------------ 

 A1 12.48147 6 -12.96295 

 A2 21.28074 10 -22.56148 

 A3 17.06903 7 -20.13805 

 R -24.33356 2 52.66712 

 2 -0.9869933 4 9.973987 

 3 -0.9869933 4 9.973987 

 4 14.52238 5 -19.04476 

 5 16.84384 6 -21.68767 

 Additive constant for all log-likelihoods = -22.97. This constant added to the 

 above values gives the log-likelihood including the term that does not 

 depend on the model parameters. 

 Explanation of Tests 

 Test 1: Does response and/or variances differ among Dose levels? (A2 vs. R) 

 Test 2: Are Variances Homogeneous? (A2 vs. A1) 

 Test 3: Are variances adequately modeled? (A2 vs. A3) 

 Test 4: Does Model 2 fit the data? (A3 vs. 2) 

 Test 5a: Does Model 3 fit the data? (A3 vs 3) 

 Test 5b: Is Model 3 better than Model 2? (3 vs. 2) 

 Test 6a: Does Model 4 fit the data? (A3 vs 4) 

 Test 6b: Is Model 4 better than Model 2? (4 vs. 2) 

 Test 7a: Does Model 5 fit the data? (A3 vs 5) 

 Test 7b: Is Model 5 better than Model 3? (5 vs. 3) 

 Test 7c: Is Model 5 better than Model 4? (5 vs. 4) 
Page 65 of 438 

 Tests of Interest 

 Test -2*log(Likelihood Ratio) D. F. p-value 

 -------- ------------------------ ------ -------------- 

 Test 1 91.23 8 < 0.0001 

 Test 2 17.6 4 0.001478 

 Test 3 8.423 3 0.03803 

 Test 4 36.11 3 < 0.0001 

 Test 5a 36.11 3 < 0.0001 

 Test 5b -4.103e-013 0 N/A 

 Test 6a 5.093 2 0.07834 

 Test 6b 31.02 1 < 0.0001 

 Test 7a 0.4504 1 0.5022 

 Test 7b 35.66 2 < 0.0001 

 Test 7c 4.643 1 0.03118 

 The p-value for Test 1 is less than .05. There appears to be a 

 difference between response and/or variances among the dose 

 levels, it seems appropriate to model the data. 

 The p-value for Test 2 is less than .1. A non-homogeneous 

 variance model appears to be appropriate. 

 The p-value for Test 3 is less than .1. You may want to 

 consider a different variance model. 

 The p-value for Test 4 is less than .1. Model 2 may not adequately 

 describe the data; you may want to consider another model. 

 The p-value for Test 5a is less than .1. Model 3 may not adequately 

 describe the data; you may want to consider another model. 

 Degrees of freedom for Test 5b are less than or equal to 0. 

 The Chi-Square test for fit is not valid. 

 The p-value for Test 6a is less than .1. Model 4 may not adequately 

 describe the data; you may want to consider another model. 

 The p-value for Test 6b is less than .05. Model 4 appears 

 to fit the data better than Model 2. 

 The p-value for Test 7a is greater than .1. Model 5 seems 

 to adequately describe the data. 

 The p-value for Test 7b is less than .05. Model 5 appears 
Page 66 of 438 

 to fit the data better than Model 3. 

 The p-value for Test 7c is less than .05. Model 5 appears 

 to fit the data better than Model 4. 

 Benchmark Dose Computations: 

 Specified Effect = 0.100000 

 Risk Type = Relative deviation 

 Confidence Level = 0.950000 

 BMD and BMDL by Model 

 Model BMD BMDL 

 ------- ------------ ---------- 

 2 0.372171 0.0900431 

 3 0.372171 0.0900431 

 4 0.108486 0.0924896 

 5 0.228194 0.130349 
Page 67 of 438 

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dose

Exponential Model 2 with 0.95 Confidence Level

12:00 07/26 2012

BMDL BMD

 

Exponential

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Mean Response

dose

Exponential Model 3 with 0.95 Confidence Level

12:00 07/26 2012

BMDL BMD

 

Exponential
Page 68 of 438 

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dose

Exponential Model 4 with 0.95 Confidence Level

12:00 07/26 2012

BMDLBMD

 

Exponential

 1

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Mean Response

dose

Exponential Model 5 with 0.95 Confidence Level

12:00 07/26 2012

BMDL BMD

 

Exponential
Page 69 of 438 

Female PND22 Brain ChE Inhibition

 ==================================================================== 

 Exponential Model. (Version: 1.7; Date: 12/10/2009) 

 Input Data File: C:/Documents and Settings/jliccion/My 

Documents/BMDS220/Data/exp_UntitledData_Setting.(d) 

 Gnuplot Plotting File: 

 Thu Jul 26 11:05:57 2012 

 ==================================================================== 

 BMDS Model Run 

~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ 

 

 The form of the response function by Model: 

 Model 2: Y[dose] = a * exp{sign * b * dose} 

 Model 3: Y[dose] = a * exp{sign * (b * dose)^d} 

 Model 4: Y[dose] = a * [c-(c-1) * exp{-b * dose}] 

 Model 5: Y[dose] = a * [c-(c-1) * exp{-(b * dose)^d}] 

 Note: Y[dose] is the median response for exposure = dose; 

 sign = +1 for increasing trend in data; 

 sign = -1 for decreasing trend. 

 Model 2 is nested within Models 3 and 4. 

 Model 3 is nested within Model 5. 

 Model 4 is nested within Model 5. 

 Dependent variable = Mean 

 Independent variable = Dose 

 Data are assumed to be distributed: normally 

 Variance Model: exp(lnalpha +rho *ln(Y[dose])) 

 rho is set to 0. 

 A constant variance model is fit. 

 Total number of dose groups = 5 

 Total number of records with missing values = 0 

 Maximum number of iterations = 250 

 Relative Function Convergence has been set to: 1e-008 

 Parameter Convergence has been set to: 1e-008 

 MLE solution provided: Exact 

 Initial Parameter Values 

 Variable Model 2 Model 3 Model 4 Model 5 

 -------- ------- ------- ------- ------- 

 lnalpha -2.49442 -2.49442 -2.49442 -2.49442 

 rho(S) 0 0 0 0 

 a 2.3037 2.3037 5.7015 5.7015 

 b 0.249996 0.249996 0.869475 0.869475 

 c -- -- 0.222164 

0.222164 

 d -- 1 -- 1 

 (S) = Specified 
Page 70 of 438 

 Parameter Estimates by Model 

 Variable Model 2 Model 3 Model 4 Model 5 

 -------- ------- ------- ------- ------- 

 lnalpha -0.992616 -0.992616 -2.14826 -2.14826 

 rho 0 0 0 0 

 a 4.8018 4.8018 5.19109 5.19109 

 b 0.44012 0.44012 1.2623 1.2623 

 c -- -- 0.259981 0.259981 

 d -- 1 -- 1 

 Table of Stats From Input Data 

 Dose N Obs Mean Obs Std Dev 

 ----- --- ---------- ------------- 

 0 5 5.43 0.44 

 0.1 5 4.41 0.26 

 0.3 5 4.02 0.31 

 1 5 2.51 0.35 

 5 5 1.33 0.19 

 Estimated Values of Interest 

 Model Dose Est Mean Est Std Scaled Residual 

 ------- ------ ---------- --------- ---------------- 

 2 0 4.802 0.6088 2.307 

 0.1 4.595 0.6088 -0.6797 

 0.3 4.208 0.6088 -0.69 

 1 3.092 0.6088 -2.138 

 5 0.5317 0.6088 2.932 

 3 0 4.802 0.6088 2.307 

 0.1 4.595 0.6088 -0.6797 

 0.3 4.208 0.6088 -0.69 

 1 3.092 0.6088 -2.138 

 5 0.5317 0.6088 2.932 

 4 0 5.191 0.3416 1.564 

 0.1 4.736 0.3416 -2.131 

 0.3 3.98 0.3416 0.2613 

 1 2.437 0.3416 0.4796 

 5 1.357 0.3416 -0.1738 

 5 0 5.191 0.3416 1.564 

 0.1 4.736 0.3416 -2.131 

 0.3 3.98 0.3416 0.2613 

 1 2.437 0.3416 0.4796 

 5 1.357 0.3416 -0.1738 

 Other models for which likelihoods are calculated: 

 Model A1: Yij = Mu(i) + e(ij) 

 Var{e(ij)} = Sigma^2 

 Model A2: Yij = Mu(i) + e(ij) 

 Var{e(ij)} = Sigma(i)^2 

 Model A3: Yij = Mu(i) + e(ij) 

 Var{e(ij)} = exp(lalpha + log(mean(i)) * rho) 
Page 71 of 438 

 Model R: Yij = Mu + e(i) 

 Var{e(ij)} = Sigma^2 

 Likelihoods of Interest 

 Model Log(likelihood) DF AIC 

 ------- ----------------- ---- ------------ 

 A1 18.6803 6 -25.36059 

 A2 20.53825 10 -21.07649 

 A3 18.6803 6 -25.36059 

 R -22.26118 2 48.52236 

 2 -0.09230161 3 6.184603 

 3 -0.09230161 3 6.184603 

 4 14.35323 4 -20.70647 

 5 14.35323 4 -20.70647 

 Additive constant for all log-likelihoods = -22.97. This constant added to the 

 above values gives the log-likelihood including the term that does not 

 depend on the model parameters. 

 Explanation of Tests 

 Test 1: Does response and/or variances differ among Dose levels? (A2 vs. R) 

 Test 2: Are Variances Homogeneous? (A2 vs. A1) 

 Test 3: Are variances adequately modeled? (A2 vs. A3) 

 Test 4: Does Model 2 fit the data? (A3 vs. 2) 

 Test 5a: Does Model 3 fit the data? (A3 vs 3) 

 Test 5b: Is Model 3 better than Model 2? (3 vs. 2) 

 Test 6a: Does Model 4 fit the data? (A3 vs 4) 

 Test 6b: Is Model 4 better than Model 2? (4 vs. 2) 

 Test 7a: Does Model 5 fit the data? (A3 vs 5) 

 Test 7b: Is Model 5 better than Model 3? (5 vs. 3) 

 Test 7c: Is Model 5 better than Model 4? (5 vs. 4) 

 Tests of Interest 

 Test -2*log(Likelihood Ratio) D. F. p-value 

 -------- ------------------------ ------ -------------- 

 Test 1 85.6 8 < 0.0001 

 Test 2 3.716 4 0.4458 

 Test 3 3.716 4 0.4458 

 Test 4 37.55 3 < 0.0001 

 Test 5a 37.55 3 < 0.0001 

 Test 5b -6.807e-013 0 N/A 

 Test 6a 8.654 2 0.01321 

 Test 6b 28.89 1 < 0.0001 

 Test 7a 8.654 2 0.01321 

 Test 7b 28.89 1 < 0.0001 

 Test 7c 1.066e-014 0 N/A 

 The p-value for Test 1 is less than .05. There appears to be a 

 difference between response and/or variances among the dose 

 levels, it seems appropriate to model the data. 
Page 72 of 438 

 The p-value for Test 2 is greater than .1. A homogeneous 

 variance model appears to be appropriate here. 

 The p-value for Test 3 is greater than .1. The modeled 

 variance appears to be appropriate here. 

 The p-value for Test 4 is less than .1. Model 2 may not adequately 

 describe the data; you may want to consider another model. 

 The p-value for Test 5a is less than .1. Model 3 may not adequately 

 describe the data; you may want to consider another model. 

 Degrees of freedom for Test 5b are less than or equal to 0. 

 The Chi-Square test for fit is not valid. 

 The p-value for Test 6a is less than .1. Model 4 may not adequately 

 describe the data; you may want to consider another model. 

 The p-value for Test 6b is less than .05. Model 4 appears 

 to fit the data better than Model 2. 

 The p-value for Test 7a is less than .1. Model 5 may not adequately 

 describe the data; you may want to consider another model. 

 The p-value for Test 7b is less than .05. Model 5 appears 

 to fit the data better than Model 3. 

 Degrees of freedom for Test 7c are less than or equal to 0. 

 The Chi-Square test for fit is not valid. 

 Benchmark Dose Computations: 

 Specified Effect = 0.100000 

 Risk Type = Relative deviation 

 Confidence Level = 0.950000 

 BMD and BMDL by Model 

 Model BMD BMDL 

 ------- ------------ ---------- 

 2 0.239391 0.145676 

 3 0.239391 0.145676 

 4 0.11501 0.0940254 

 5 0.11501 0.0940254 
Page 73 of 438 

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 3

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Mean Response

dose

Exponential Model 2 with 0.95 Confidence Level

11:05 07/26 2012

BMDL BMD

 

Exponential

 1

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 3

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 0 1 2 3 4 5

Mean Response

dose

Exponential Model 3 with 0.95 Confidence Level

11:05 07/26 2012

BMDL BMD

 

Exponential
Page 74 of 438 

 1

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Mean Response

dose

Exponential Model 4 with 0.95 Confidence Level

11:05 07/26 2012

BMDL BMD

 

Exponential

 1

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 3

 4

 5

 6

 0 1 2 3 4 5

Mean Response

dose

Exponential Model 5 with 0.95 Confidence Level

11:05 07/26 2012

BMDL BMD

 

Exponential
Page 75 of 438 

Male PND22 RBC ChE Inhibition

 ==================================================================== 

 Exponential Model. (Version: 1.7; Date: 12/10/2009) 

 Input Data File: C:/Documents and Settings/jliccion/My 

Documents/BMDS220/Data/exp_UntitledData_Setting.(d) 

 Gnuplot Plotting File: 

 Thu Jul 26 10:20:48 2012 

 ==================================================================== 

 BMDS Model Run 

~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ 

 

 The form of the response function by Model: 

 Model 2: Y[dose] = a * exp{sign * b * dose} 

 Model 3: Y[dose] = a * exp{sign * (b * dose)^d} 

 Model 4: Y[dose] = a * [c-(c-1) * exp{-b * dose}] 

 Model 5: Y[dose] = a * [c-(c-1) * exp{-(b * dose)^d}] 

 Note: Y[dose] is the median response for exposure = dose; 

 sign = +1 for increasing trend in data; 

 sign = -1 for decreasing trend. 

 Model 2 is nested within Models 3 and 4. 

 Model 3 is nested within Model 5. 

 Model 4 is nested within Model 5. 

 Dependent variable = Mean 

 Independent variable = Dose 

 Data are assumed to be distributed: normally 

 Variance Model: exp(lnalpha +rho *ln(Y[dose])) 

 The variance is to be modeled as Var(i) = exp(lalpha + log(mean(i)) * rho) 

 Total number of dose groups = 5 

 Total number of records with missing values = 0 

 Maximum number of iterations = 250 

 Relative Function Convergence has been set to: 1e-008 

 Parameter Convergence has been set to: 1e-008 

 MLE solution provided: Exact 

 Initial Parameter Values 

 Variable Model 2 Model 3 Model 4 Model 5 

 -------- ------- ------- ------- ------- 

 lnalpha -8.69805 -8.69805 -8.69805 -8.69805 

 rho 2.53353 2.53353 2.53353 2.53353 

 a 1747.61 1747.61 2991.45 2991.45 

 b 0.126561 0.126561 0.700467 0.700467 

 c -- -- 0.43298 

0.43298 

 d -- 1 -- 1 

 Parameter Estimates by Model 

 Variable Model 2 Model 3 Model 4 Model 5 

 -------- ------- ------- ------- ------- 
Page 76 of 438 

 lnalpha -17.1985 122.086 -8.3531 -8.63381 

 rho 3.79153 -14.2486 2.47439 2.50916 

 a 2412.33 2137.6 2873.87 2827.29 

 b 0.118937 0.0660624 1.81398 1.74328 

 c -- -- 0.472054 0.482053 

 d -- 18 -- 1.14866 

 Table of Stats From Input Data 

 Dose N Obs Mean Obs Std Dev 

 ----- --- ---------- ------------- 

 0 5 2849 384 

 0.1 5 2584 227 

 0.3 5 2312 231 

 1 5 1583 113 

 5 5 1360 152 

 Estimated Values of Interest 

 Model Dose Est Mean Est Std Scaled Residual 

 ------- ------ ---------- --------- ---------------- 

 2 0 2412 476.1 2.051 

 0.1 2384 465.5 0.9617 

 0.3 2328 445 -0.07927 

 1 2142 380 -3.288 

 5 1331 154.2 0.4209 

 3 0 2138 613 2.595 

 0.1 2138 613 1.628 

 0.3 2138 613 0.6362 

 1 2138 613 -2.023 

 5 2138 613 -2.837 

 4 0 2874 291.7 -0.1907 

 0.1 2622 260.4 -0.3277 

 0.3 2237 214 0.7828 

 1 1604 141.8 -0.3303 

 5 1357 115.3 0.06212 

 5 0 2827 285.2 0.1702 

 0.1 2643 262.1 -0.5037 

 0.3 2274 217 0.3953 

 1 1583 137.8 -0.00604 

 5 1363 114.2 -0.05698 

 Other models for which likelihoods are calculated: 

 Model A1: Yij = Mu(i) + e(ij) 

 Var{e(ij)} = Sigma^2 

 Model A2: Yij = Mu(i) + e(ij) 

 Var{e(ij)} = Sigma(i)^2 

 Model A3: Yij = Mu(i) + e(ij) 

 Var{e(ij)} = exp(lalpha + log(mean(i)) * rho) 

 Model R: Yij = Mu + e(i) 

 Var{e(ij)} = Sigma^2 

 Likelihoods of Interest 
Page 77 of 438 

 Model Log(likelihood) DF AIC 

 ------- ----------------- ---- ------------ 

 A1 -146.7362 6 305.4724 

 A2 -142.5571 10 305.1142 

 A3 -143.5228 7 301.0455 

 R -172.9575 2 349.9149 

 2 -159.4253 4 326.8506 

 3 -172.9575 5 355.9149 

 4 -144.0265 5 298.053 

 5 -143.8297 6 299.6594 

 Additive constant for all log-likelihoods = -22.97. This constant added to the 

 above values gives the log-likelihood including the term that does not 

 depend on the model parameters. 

 Explanation of Tests 

 Test 1: Does response and/or variances differ among Dose levels? (A2 vs. R) 

 Test 2: Are Variances Homogeneous? (A2 vs. A1) 

 Test 3: Are variances adequately modeled? (A2 vs. A3) 

 Test 4: Does Model 2 fit the data? (A3 vs. 2) 

 Test 5a: Does Model 3 fit the data? (A3 vs 3) 

 Test 5b: Is Model 3 better than Model 2? (3 vs. 2) 

 Test 6a: Does Model 4 fit the data? (A3 vs 4) 

 Test 6b: Is Model 4 better than Model 2? (4 vs. 2) 

 Test 7a: Does Model 5 fit the data? (A3 vs 5) 

 Test 7b: Is Model 5 better than Model 3? (5 vs. 3) 

 Test 7c: Is Model 5 better than Model 4? (5 vs. 4) 

 Tests of Interest 

 Test -2*log(Likelihood Ratio) D. F. p-value 

 -------- ------------------------ ------ -------------- 

 Test 1 60.8 8 < 0.0001 

 Test 2 8.358 4 0.07931 

 Test 3 1.931 3 0.5868 

 Test 4 31.81 3 < 0.0001 

 Test 5a 58.87 2 < 0.0001 

 Test 5b -27.06 1 N/A 

 Test 6a 1.007 2 0.6043 

 Test 6b 30.8 1 < 0.0001 

 Test 7a 0.6139 1 0.4333 

 Test 7b 58.26 1 < 0.0001 

 Test 7c 0.3936 1 0.5304 

 The p-value for Test 1 is less than .05. There appears to be a 

 difference between response and/or variances among the dose 

 levels, it seems appropriate to model the data. 

 The p-value for Test 2 is less than .1. A non-homogeneous 

 variance model appears to be appropriate. 

 The p-value for Test 3 is greater than .1. The modeled 

 variance appears to be appropriate here. 

 The p-value for Test 4 is less than .1. Model 2 may not adequately 
Page 78 of 438 

 describe the data; you may want to consider another model. 

 The p-value for Test 5a is less than .1. Model 3 may not adequately 

 describe the data; you may want to consider another model. 

 The p-value for Test 5b is less than .05. Model 3 appears 

 to fit the data better than Model 2. 

 The p-value for Test 6a is greater than .1. Model 4 seems 

 to adequately describe the data. 

 The p-value for Test 6b is less than .05. Model 4 appears 

 to fit the data better than Model 2. 

 The p-value for Test 7a is greater than .1. Model 5 seems 

 to adequately describe the data. 

 The p-value for Test 7b is less than .05. Model 5 appears 

 to fit the data better than Model 3. 

 The p-value for Test 7c is greater than .05. Model 5 does 

 not seem to fit the data better than Model 4. 

 Benchmark Dose Computations: 

 Specified Effect = 0.100000 

 Risk Type = Relative deviation 

 Confidence Level = 0.950000 

 BMD and BMDL by Model 

 Model BMD BMDL 

 ------- ------------ ---------- 

 2 0.885852 0.664886 

 3 13.3583 0.129834 

 4 0.115766 0.0880934 

 5 0.150183 0.0900362 
Page 79 of 438 

 1000

 1500

 2000

 2500

 3000

 3500

 0 1 2 3 4 5

Mean Response

dose

Exponential Model 2 with 0.95 Confidence Level

10:20 07/26 2012

BMDL BMD

 

Exponential

 1000

 1500

 2000

 2500

 3000

 3500

 0 2 4 6 8 10 12 14

Mean Response

dose

Exponential Model 3 with 0.95 Confidence Level

10:20 07/26 2012

BMDL BMD

 

Exponential
Page 80 of 438 

 1000

 1500

 2000

 2500

 3000

 3500

 0 1 2 3 4 5

Mean Response

dose

Exponential Model 4 with 0.95 Confidence Level

10:20 07/26 2012

BMDL BMD

 

Exponential

 1000

 1500

 2000

 2500

 3000

 3500

 0 1 2 3 4 5

Mean Response

dose

Exponential Model 5 with 0.95 Confidence Level

10:20 07/26 2012

BMDL BMD

 

Exponential
Page 81 of 438 

Female PND22 RBC ChE Inhibition

 ==================================================================== 

 Exponential Model. (Version: 1.7; Date: 12/10/2009) 

 Input Data File: C:/Documents and Settings/jliccion/My 

Documents/BMDS220/Data/exp_UntitledData_Setting.(d) 

 Gnuplot Plotting File: 

 Thu Jul 26 09:57:47 2012 

 ==================================================================== 

 BMDS Model Run 

~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ 

 

 The form of the response function by Model: 

 Model 2: Y[dose] = a * exp{sign * b * dose} 

 Model 3: Y[dose] = a * exp{sign * (b * dose)^d} 

 Model 4: Y[dose] = a * [c-(c-1) * exp{-b * dose}] 

 Model 5: Y[dose] = a * [c-(c-1) * exp{-(b * dose)^d}] 

 Note: Y[dose] is the median response for exposure = dose; 

 sign = +1 for increasing trend in data; 

 sign = -1 for decreasing trend. 

 Model 2 is nested within Models 3 and 4. 

 Model 3 is nested within Model 5. 

 Model 4 is nested within Model 5. 

 Dependent variable = Mean 

 Independent variable = Dose 

 Data are assumed to be distributed: normally 

 Variance Model: exp(lnalpha +rho *ln(Y[dose])) 

 The variance is to be modeled as Var(i) = exp(lalpha + log(mean(i)) * rho) 

 Total number of dose groups = 5 

 Total number of records with missing values = 0 

 Maximum number of iterations = 250 

 Relative Function Convergence has been set to: 1e-008 

 Parameter Convergence has been set to: 1e-008 

 MLE solution provided: Exact 

 Initial Parameter Values 

 Variable Model 2 Model 3 Model 4 Model 5 

 -------- ------- ------- ------- ------- 

 lnalpha -2.57572 -2.57572 -2.57572 -2.57572 

 rho 1.74944 1.74944 1.74944 1.74944 

 a 1742.99 1742.99 3179.4 3179.4 

 b 0.13348 0.13348 0.720441 0.720441 

 c -- -- 0.395702 

0.395702 

 d -- 1 -- 1 

 Parameter Estimates by Model 

 Variable Model 2 Model 3 Model 4 Model 5 
Page 82 of 438 

 -------- ------- ------- ------- ------- 

 lnalpha -17.9803 -17.9803 -7.3403 -7.3403 

 rho 3.88486 3.88486 2.37494 2.37494 

 a 2446.5 2446.5 2840.74 2840.74 

 b 0.126594 0.126594 1.49896 1.49896 

 c -- -- 0.467836 0.467836 

 d -- 1 -- 1 

 Table of Stats From Input Data 

 Dose N Obs Mean Obs Std Dev 

 ----- --- ---------- ------------- 

 0 5 3028 319 

 0.1 5 2438 302 

 0.3 5 2280 176 

 1 5 1700 203 

 5 5 1321 147 

 Estimated Values of Interest 

 Model Dose Est Mean Est Std Scaled Residual 

 ------- ------ ---------- --------- ---------------- 

 2 0 2447 476 2.731 

 0.1 2416 464.5 0.1072 

 0.3 2355 442.2 -0.3809 

 1 2156 372.3 -2.737 

 5 1299 139.2 0.3515 

 3 0 2447 476 2.731 

 0.1 2416 464.5 0.1072 

 0.3 2355 442.2 -0.3809 

 1 2156 372.3 -2.737 

 5 1299 139.2 0.3515 

 4 0 2841 321.3 1.303 

 0.1 2630 293.2 -1.466 

 0.3 2293 249.2 -0.1187 

 1 1667 170.6 0.4369 

 5 1330 130.5 -0.1516 

 5 0 2841 321.3 1.303 

 0.1 2630 293.2 -1.466 

 0.3 2293 249.2 -0.1187 

 1 1667 170.6 0.4369 

 5 1330 130.5 -0.1516 

 Other models for which likelihoods are calculated: 

 Model A1: Yij = Mu(i) + e(ij) 

 Var{e(ij)} = Sigma^2 

 Model A2: Yij = Mu(i) + e(ij) 

 Var{e(ij)} = Sigma(i)^2 

 Model A3: Yij = Mu(i) + e(ij) 

 Var{e(ij)} = exp(lalpha + log(mean(i)) * rho) 

 Model R: Yij = Mu + e(i) 

 Var{e(ij)} = Sigma^2 

 Likelihoods of Interest 
Page 83 of 438 

 Model Log(likelihood) DF AIC 

 ------- ----------------- ---- ------------ 

 A1 -146.6727 6 305.3454 

 A2 -144.4594 10 308.9188 

 A3 -145.0517 7 304.1035 

 R -173.6765 2 351.3529 

 2 -158.7688 4 325.5376 

 3 -158.7688 4 325.5376 

 4 -147.4096 5 304.8191 

 5 -147.4096 5 304.8191 

 Additive constant for all log-likelihoods = -22.97. This constant added to the 

 above values gives the log-likelihood including the term that does not 

 depend on the model parameters. 

 Explanation of Tests 

 Test 1: Does response and/or variances differ among Dose levels? (A2 vs. R) 

 Test 2: Are Variances Homogeneous? (A2 vs. A1) 

 Test 3: Are variances adequately modeled? (A2 vs. A3) 

 Test 4: Does Model 2 fit the data? (A3 vs. 2) 

 Test 5a: Does Model 3 fit the data? (A3 vs 3) 

 Test 5b: Is Model 3 better than Model 2? (3 vs. 2) 

 Test 6a: Does Model 4 fit the data? (A3 vs 4) 

 Test 6b: Is Model 4 better than Model 2? (4 vs. 2) 

 Test 7a: Does Model 5 fit the data? (A3 vs 5) 

 Test 7b: Is Model 5 better than Model 3? (5 vs. 3) 

 Test 7c: Is Model 5 better than Model 4? (5 vs. 4) 

 Tests of Interest 

 Test -2*log(Likelihood Ratio) D. F. p-value 

 -------- ------------------------ ------ -------------- 

 Test 1 58.43 8 < 0.0001 

 Test 2 4.427 4 0.3513 

 Test 3 1.185 3 0.7567 

 Test 4 27.43 3 < 0.0001 

 Test 5a 27.43 3 < 0.0001 

 Test 5b -5.684e-013 0 N/A 

 Test 6a 4.716 2 0.09462 

 Test 6b 22.72 1 < 0.0001 

 Test 7a 4.716 2 0.09462 

 Test 7b 22.72 1 < 0.0001 

 Test 7c -5.684e-014 0 N/A 

 The p-value for Test 1 is less than .05. There appears to be a 

 difference between response and/or variances among the dose 

 levels, it seems appropriate to model the data. 

 The p-value for Test 2 is greater than .1. Consider 

 running a homogeneous model. 

 The p-value for Test 3 is greater than .1. The modeled 

 variance appears to be appropriate here. 
Page 84 of 438 

 The p-value for Test 4 is less than .1. Model 2 may not adequately 

 describe the data; you may want to consider another model. 

 The p-value for Test 5a is less than .1. Model 3 may not adequately 

 describe the data; you may want to consider another model. 

 Degrees of freedom for Test 5b are less than or equal to 0. 

 The Chi-Square test for fit is not valid. 

 The p-value for Test 6a is less than .1. Model 4 may not adequately 

 describe the data; you may want to consider another model. 

 The p-value for Test 6b is less than .05. Model 4 appears 

 to fit the data better than Model 2. 

 The p-value for Test 7a is less than .1. Model 5 may not adequately 

 describe the data; you may want to consider another model. 

 The p-value for Test 7b is less than .05. Model 5 appears 

 to fit the data better than Model 3. 

 Degrees of freedom for Test 7c are less than or equal to 0. 

 The Chi-Square test for fit is not valid. 

 Benchmark Dose Computations: 

 Specified Effect = 0.100000 

 Risk Type = Relative deviation 

 Confidence Level = 0.950000 

 BMD and BMDL by Model 

 Model BMD BMDL 

 ------- ------------ ---------- 

 2 0.832268 0.669998 

 3 0.832268 0.669998 

 4 0.138861 0.0988325 

 5 0.138861 0.0988325 p = 0.095 
Page 85 of 438 

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09:57 07/26 2012

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Exponential

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Page 87 of 438 

Male PND42 Brain ChE Inhibition – MRID 46153206

 ==================================================================== 

 Exponential Model. (Version: 1.7; Date: 12/10/2009) 

 Input Data File: C:/Usepa/BMDS212/Data/exp_Testrunscontinuous_Setting.(d) 

 Gnuplot Plotting File: 

 Fri Feb 04 12:21:44 2011 

 ==================================================================== 

 BMDS Model Run 

~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ 

 

 The form of the response function by Model: 

 Model 2: Y[dose] = a * exp{sign * b * dose} 

 Model 3: Y[dose] = a * exp{sign * (b * dose)^d} 

 Model 4: Y[dose] = a * [c-(c-1) * exp{-b * dose}] 

 Model 5: Y[dose] = a * [c-(c-1) * exp{-(b * dose)^d}] 

 Note: Y[dose] is the median response for exposure = dose; 

 sign = +1 for increasing trend in data; 

 sign = -1 for decreasing trend. 

 Model 2 is nested within Models 3 and 4. 

 Model 3 is nested within Model 5. 

 Model 4 is nested within Model 5. 

 Dependent variable = Mean 

 Independent variable = Dose 

 Data are assumed to be distributed: normally 

 Variance Model: exp(lnalpha +rho *ln(Y[dose])) 

 The variance is to be modeled as Var(i) = exp(lalpha + log(mean(i)) * rho) 

 Total number of dose groups = 4 

 Total number of records with missing values = 0 

 Maximum number of iterations = 250 

 Relative Function Convergence has been set to: 1e-008 

 Parameter Convergence has been set to: 1e-008 

 MLE solution provided: Exact 

 Initial Parameter Values 

 Variable Model 2 Model 3 Model 4 Model 5 

 -------- ------- ------- ------- ------- 

 lnalpha -5.2133 -5.2133 -5.2133 -5.2133 

 rho 2.06307 2.06307 2.06307 2.06307 

 a 2.3455 2.3455 5.2395 5.2395 

 b 0.276743 0.276743 0.402157 0.402157 

 c -- -- 0.118332 

0.118332 

 d -- 1 -- 1 

 Parameter Estimates by Model 

 Variable Model 2 Model 3 Model 4 Model 5 

 -------- ------- ------- ------- ------- 

 lnalpha -5.50474 -5.52216 -5.50474 -5.49807 
Page 88 of 438 

 rho 2.38374 2.36844 2.38374 2.32664 

 a 4.95468 4.85725 4.95468 4.86095 

 b 0.276943 0.256636 0.276943 2.79331 

 c -- -- 0 0.255042 

 d -- 1.24955 -- 14.8291 

 Table of Stats From Input Data 

 Dose N Obs Mean Obs Std Dev 

 ----- --- ---------- ------------- 

 0 5 4.77 0.27 

 0.1 5 4.99 0.32 

 0.3 5 4.57 0.6 

 5 5 1.24 0.09 

 Estimated Values of Interest 

 Model Dose Est Mean Est Std Scaled Residual 

 ------- ------ ---------- --------- ---------------- 

 2 0 4.955 0.4296 -0.9613 

 0.1 4.819 0.4156 0.9182 

 0.3 4.56 0.3891 0.0594 

 5 1.241 0.08246 -0.01693 

 3 0 4.857 0.4109 -0.4749 

 0.1 4.808 0.4059 1.005 

 0.3 4.664 0.3916 -0.5367 

 5 1.24 0.08155 0.006601 

 4 0 4.955 0.4296 -0.9613 

 0.1 4.819 0.4156 0.9182 

 0.3 4.56 0.3891 0.0594 

 5 1.241 0.08246 -0.01693 

 5 0 4.861 0.4027 -0.505 

 0.1 4.861 0.4027 0.7166 

 0.3 4.607 0.3783 -0.2182 

 5 1.24 0.08216 0.006919 

 Other models for which likelihoods are calculated: 

 Model A1: Yij = Mu(i) + e(ij) 

 Var{e(ij)} = Sigma^2 

 Model A2: Yij = Mu(i) + e(ij) 

 Var{e(ij)} = Sigma(i)^2 

 Model A3: Yij = Mu(i) + e(ij) 

 Var{e(ij)} = exp(lalpha + log(mean(i)) * rho) 

 Model R: Yij = Mu + e(i) 

 Var{e(ij)} = Sigma^2 

 Likelihoods of Interest 

 Model Log(likelihood) DF AIC 

 ------- ----------------- ---- ------------ 

 A1 12.19347 5 -14.38695 

 A2 19.06913 8 -22.13826 

 A3 16.85227 6 -21.70453 

 R -19.06644 2 42.13288 
Page 89 of 438 

 2 15.81191 4 -23.62381 

 3 16.17594 5 -22.35187 

 4 15.81191 4 -23.62381 

 5 16.45069 6 -20.90139 

 Additive constant for all log-likelihoods = -18.38. This constant added to the 

 above values gives the log-likelihood including the term that does not 

 depend on the model parameters. 

 Explanation of Tests 

 Test 1: Does response and/or variances differ among Dose levels? (A2 vs. R) 

 Test 2: Are Variances Homogeneous? (A2 vs. A1) 

 Test 3: Are variances adequately modeled? (A2 vs. A3) 

 Test 4: Does Model 2 fit the data? (A3 vs. 2) 

 Test 5a: Does Model 3 fit the data? (A3 vs 3) 

 Test 5b: Is Model 3 better than Model 2? (3 vs. 2) 

 Test 6a: Does Model 4 fit the data? (A3 vs 4) 

 Test 6b: Is Model 4 better than Model 2? (4 vs. 2) 

 Test 7a: Does Model 5 fit the data? (A3 vs 5) 

 Test 7b: Is Model 5 better than Model 3? (5 vs. 3) 

 Test 7c: Is Model 5 better than Model 4? (5 vs. 4) 

 Tests of Interest 

 Test -2*log(Likelihood Ratio) D. F. p-value 

 -------- ------------------------ ------ -------------- 

 Test 1 76.27 6 < 0.0001 

 Test 2 13.75 3 0.003264 

 Test 3 4.434 2 0.109 

 Test 4 2.081 2 0.3533 

 Test 5a 1.353 1 0.2448 

 Test 5b 0.7281 1 0.3935 

 Test 6a 2.081 2 0.3533 

 Test 6b -2.132e-014 0 N/A 

 Test 7a 0.8031 0 N/A 

 Test 7b 0.5495 1 0.4585 

 Test 7c 1.278 2 0.5279 

 The p-value for Test 1 is less than .05. There appears to be a 

 difference between response and/or variances among the dose 

 levels, it seems appropriate to model the data. 

 The p-value for Test 2 is less than .1. A non-homogeneous 

 variance model appears to be appropriate. 

 The p-value for Test 3 is greater than .1. The modeled 

 variance appears to be appropriate here. 

 The p-value for Test 4 is greater than .1. Model 2 seems 

 to adequately describe the data. 

 The p-value for Test 5a is greater than .1. Model 3 seems 

 to adequately describe the data. 

 The p-value for Test 5b is greater than .05. Model 3 does 
Page 90 of 438 

 not seem to fit the data better than Model 2. 

 The p-value for Test 6a is greater than .1. Model 4 seems 

 to adequately describe the data. 

 Degrees of freedom for Test 6b are less than or equal to 0. 

 The Chi-Square test for fit is not valid. 

 Degrees of freedom for Test 7a are less than or equal to 0. 

 The Chi-Square test for fit is not valid. 

 The p-value for Test 7b is greater than .05. Model 5 does 

 not seem to fit the data better than Model 3. 

 The p-value for Test 7c is greater than .05. Model 5 does 

 not seem to fit the data better than Model 4. 

 Benchmark Dose Computations: 

 Specified Effect = 0.100000 

 Risk Type = Relative deviation 

 Confidence Level = 0.950000 

 BMD and BMDL by Model 

 Model BMD BMDL 

 ------- ------------ ---------- 

 2 0.380441 0.362614 

 3 0.643489 0.365455 

 4 0.380441 0.219588 

 5 0.314162 0.261081 
Page 91 of 438 

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Exponential
Page 93 of 438 

Female PND42 Brain ChE Inhibition – MRID 46153206

 ==================================================================== 

 Exponential Model. (Version: 1.7; Date: 12/10/2009) 

 Input Data File: C:/Documents and Settings/jliccion/My 

Documents/BMDS220/Data/exp_UntitledData_Setting.(d) 

 Gnuplot Plotting File: 

 Thu Jul 26 09:48:06 2012 

 ==================================================================== 

 BMDS Model Run 

~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ 

 

 The form of the response function by Model: 

 Model 2: Y[dose] = a * exp{sign * b * dose} 

 Model 3: Y[dose] = a * exp{sign * (b * dose)^d} 

 Model 4: Y[dose] = a * [c-(c-1) * exp{-b * dose}] 

 Model 5: Y[dose] = a * [c-(c-1) * exp{-(b * dose)^d}] 

 Note: Y[dose] is the median response for exposure = dose; 

 sign = +1 for increasing trend in data; 

 sign = -1 for decreasing trend. 

 Model 2 is nested within Models 3 and 4. 

 Model 3 is nested within Model 5. 

 Model 4 is nested within Model 5. 

 Dependent variable = Mean 

 Independent variable = Dose 

 Data are assumed to be distributed: normally 

 Variance Model: exp(lnalpha +rho *ln(Y[dose])) 

 The variance is to be modeled as Var(i) = exp(lalpha + log(mean(i)) * rho) 

 Total number of dose groups = 4 

 Total number of records with missing values = 0 

 Maximum number of iterations = 250 

 Relative Function Convergence has been set to: 1e-008 

 Parameter Convergence has been set to: 1e-008 

 MLE solution provided: Exact 

 Initial Parameter Values 

 Variable Model 2 Model 3 Model 4 Model 5 

 -------- ------- ------- ------- ------- 

 lnalpha -6.32337 -6.32337 -6.32337 -6.32337 

 rho 2.59245 2.59245 2.59245 2.59245 

 a 2.33303 2.33303 5.481 5.481 

 b 0.270154 0.270154 0.855633 0.855633 

 c -- -- 0.218938 

0.218938 

 d -- 1 -- 1 

 Parameter Estimates by Model 

 Variable Model 2 Model 3 Model 4 Model 5 

 -------- ------- ------- ------- ------- 

 lnalpha -6.63517 -6.63517 -6.62712 -6.62712 
Page 94 of 438 

 rho 3.34293 3.34293 2.88877 2.88877 

 a 4.83428 4.83428 5.2242 5.2242 

 b 0.269108 0.269108 1.17157 1.17157 

 c -- -- 0.239079 0.239079 

 d -- 1 -- 1 

 Table of Stats From Input Data 

 Dose N Obs Mean Obs Std Dev 

 ----- --- ---------- ------------- 

 0 5 5.22 0.63 

 0.1 5 4.76 0.19 

 0.3 5 4.07 0.24 

 5 5 1.26 0.06 

 Estimated Values of Interest 

 Model Dose Est Mean Est Std Scaled Residual 

 ------- ------ ---------- --------- ---------------- 

 2 0 4.834 0.5047 1.709 

 0.1 4.706 0.4825 0.2506 

 0.3 4.459 0.441 -1.974 

 5 1.259 0.05325 0.0486 

 3 0 4.834 0.5047 1.709 

 0.1 4.706 0.4825 0.2506 

 0.3 4.459 0.441 -1.974 

 5 1.259 0.05325 0.0486 

 4 0 5.224 0.3963 -0.02371 

 0.1 4.785 0.3491 -0.1584 

 0.3 4.046 0.274 0.1945 

 5 1.26 0.05083 -0.01568 

 5 0 5.224 0.3963 -0.02371 

 0.1 4.785 0.3491 -0.1584 

 0.3 4.046 0.274 0.1945 

 5 1.26 0.05083 -0.01568 

 Other models for which likelihoods are calculated: 

 Model A1: Yij = Mu(i) + e(ij) 

 Var{e(ij)} = Sigma^2 

 Model A2: Yij = Mu(i) + e(ij) 

 Var{e(ij)} = Sigma(i)^2 

 Model A3: Yij = Mu(i) + e(ij) 

 Var{e(ij)} = exp(lalpha + log(mean(i)) * rho) 

 Model R: Yij = Mu + e(i) 

 Var{e(ij)} = Sigma^2 

 Likelihoods of Interest 

 Model Log(likelihood) DF AIC 

 ------- ----------------- ---- ------------ 

 A1 13.14253 5 -16.28506 

 A2 24.0479 8 -32.09581 

 A3 21.34811 6 -30.69621 

 R -19.01668 2 42.03336 
Page 95 of 438 

 2 15.8208 4 -23.6416 

 3 15.8208 4 -23.6416 

 4 21.26005 5 -32.52009 

 5 21.26005 5 -32.52009 

 Additive constant for all log-likelihoods = -18.38. This constant added to the 

 above values gives the log-likelihood including the term that does not 

 depend on the model parameters. 

 Explanation of Tests 

 Test 1: Does response and/or variances differ among Dose levels? (A2 vs. R) 

 Test 2: Are Variances Homogeneous? (A2 vs. A1) 

 Test 3: Are variances adequately modeled? (A2 vs. A3) 

 Test 4: Does Model 2 fit the data? (A3 vs. 2) 

 Test 5a: Does Model 3 fit the data? (A3 vs 3) 

 Test 5b: Is Model 3 better than Model 2? (3 vs. 2) 

 Test 6a: Does Model 4 fit the data? (A3 vs 4) 

 Test 6b: Is Model 4 better than Model 2? (4 vs. 2) 

 Test 7a: Does Model 5 fit the data? (A3 vs 5) 

 Test 7b: Is Model 5 better than Model 3? (5 vs. 3) 

 Test 7c: Is Model 5 better than Model 4? (5 vs. 4) 

 Tests of Interest 

 Test -2*log(Likelihood Ratio) D. F. p-value 

 -------- ------------------------ ------ -------------- 

 Test 1 86.13 6 < 0.0001 

 Test 2 21.81 3 < 0.0001 

 Test 3 5.4 2 0.06722 

 Test 4 11.05 2 0.003977 

 Test 5a 11.05 2 0.003977 

 Test 5b -3.908e-014 0 N/A 

 Test 6a 0.1761 1 0.6747 

 Test 6b 10.88 1 0.0009729 

 Test 7a 0.1761 1 0.6747 

 Test 7b 10.88 1 0.0009729 

 Test 7c 0 0 N/A 

 The p-value for Test 1 is less than .05. There appears to be a 

 difference between response and/or variances among the dose 

 levels, it seems appropriate to model the data. 

 The p-value for Test 2 is less than .1. A non-homogeneous 

 variance model appears to be appropriate. 

 The p-value for Test 3 is less than .1. You may want to 

 consider a different variance model. 

 The p-value for Test 4 is less than .1. Model 2 may not adequately 

 describe the data; you may want to consider another model. 

 The p-value for Test 5a is less than .1. Model 3 may not adequately 

 describe the data; you may want to consider another model. 

 Degrees of freedom for Test 5b are less than or equal to 0. 
Page 96 of 438 

 The Chi-Square test for fit is not valid. 

 The p-value for Test 6a is greater than .1. Model 4 seems 

 to adequately describe the data. 

 The p-value for Test 6b is less than .05. Model 4 appears 

 to fit the data better than Model 2. 

 The p-value for Test 7a is greater than .1. Model 5 seems 

 to adequately describe the data. 

 The p-value for Test 7b is less than .05. Model 5 appears 

 to fit the data better than Model 3. 

 Degrees of freedom for Test 7c are less than or equal to 0. 

 The Chi-Square test for fit is not valid. 

 Benchmark Dose Computations: 

 Specified Effect = 0.100000 

 Risk Type = Relative deviation 

 Confidence Level = 0.950000 

 BMD and BMDL by Model 

 Model BMD BMDL 

 ------- ------------ ---------- 

 2 0.391518 0.375628 

 3 0.391518 0.375628 

 4 0.120262 0.0920115 

 5 0.120262 0.0920115 
Page 97 of 438 

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09:48 07/26 2012

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Page 99 of 438 

Male PND42 RBC ChE Inhibition – MRID 46153206

 ==================================================================== 

 Exponential Model. (Version: 1.7; Date: 12/10/2009) 

 Input Data File: C:/Documents and Settings/jliccion/My 

Documents/BMDS220/Data/exp_UntitledData_Setting.(d) 

 Gnuplot Plotting File: 

 Thu Jul 26 09:37:15 2012 

 ==================================================================== 

 BMDS Model Run 

~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ 

 

 The form of the response function by Model: 

 Model 2: Y[dose] = a * exp{sign * b * dose} 

 Model 3: Y[dose] = a * exp{sign * (b * dose)^d} 

 Model 4: Y[dose] = a * [c-(c-1) * exp{-b * dose}] 

 Model 5: Y[dose] = a * [c-(c-1) * exp{-(b * dose)^d}] 

 Note: Y[dose] is the median response for exposure = dose; 

 sign = +1 for increasing trend in data; 

 sign = -1 for decreasing trend. 

 Model 2 is nested within Models 3 and 4. 

 Model 3 is nested within Model 5. 

 Model 4 is nested within Model 5. 

 Dependent variable = Mean 

 Independent variable = Dose 

 Data are assumed to be distributed: normally 

 Variance Model: exp(lnalpha +rho *ln(Y[dose])) 

 rho is set to 0. 

 A constant variance model is fit. 

 Total number of dose groups = 4 

 Total number of records with missing values = 0 

 Maximum number of iterations = 250 

 Relative Function Convergence has been set to: 1e-008 

 Parameter Convergence has been set to: 1e-008 

 MLE solution provided: Exact 

 Initial Parameter Values 

 Variable Model 2 Model 3 Model 4 Model 5 

 -------- ------- ------- ------- ------- 

 lnalpha 9.40035 9.40035 9.40035 9.40035 

 rho(S) 0 0 0 0 

 a 1857.97 1857.97 2909.55 2909.55 

 b 0.122373 0.122373 0.634799 0.634799 

 c -- -- 0.461207 

0.461207 

 d -- 1 -- 1 

 (S) = Specified 

 Parameter Estimates by Model 
Page 100 of 438 

 Variable Model 2 Model 3 Model 4 Model 5 

 -------- ------- ------- ------- ------- 

 lnalpha 10.6164 10.6164 10.1309 9.55907 

 rho 0 0 0 0 

 a 2599.01 2599.01 2749.97 2706.5 

 b 0.125486 0.125486 1.29416 3.18191 

 c -- -- 0.510418 0.520599 

 d -- 1 -- 17.2149 

 Table of Stats From Input Data 

 Dose N Obs Mean Obs Std Dev 

 ----- --- ---------- ------------- 

 0 5 2642 102 

 0.1 5 2771 145 

 0.3 5 2237 75 

 5 5 1409 153 

 Estimated Values of Interest 

 Model Dose Est Mean Est Std Scaled Residual 

 ------- ------ ---------- --------- ---------------- 

 2 0 2599 202 0.4759 

 0.1 2567 202 2.263 

 0.3 2503 202 -2.945 

 5 1388 202 0.235 

 3 0 2599 202 0.4759 

 0.1 2567 202 2.263 

 0.3 2503 202 -2.945 

 5 1388 202 0.235 

 4 0 2750 158.5 -1.524 

 0.1 2587 158.5 2.603 

 0.3 2317 158.5 -1.126 

 5 1406 158.5 0.04631 

 5 0 2707 119 -1.211 

 0.1 2706 119 1.211 

 0.3 2237 119 -7.557e-009 

 5 1409 119 1.727e-009 

 Other models for which likelihoods are calculated: 

 Model A1: Yij = Mu(i) + e(ij) 

 Var{e(ij)} = Sigma^2 

 Model A2: Yij = Mu(i) + e(ij) 

 Var{e(ij)} = Sigma(i)^2 

 Model A3: Yij = Mu(i) + e(ij) 

 Var{e(ij)} = exp(lalpha + log(mean(i)) * rho) 

 Model R: Yij = Mu + e(i) 

 Var{e(ij)} = Sigma^2 

 Likelihoods of Interest 

 Model Log(likelihood) DF AIC 

 ------- ----------------- ---- ------------ 

 A1 -104.0035 5 218.007 
Page 101 of 438 

 A2 -102.5167 8 221.0335 

 A3 -104.0035 5 218.007 

 R -135.9478 2 275.8955 

 2 -116.1642 3 238.3285 

 3 -116.1642 3 238.3285 

 4 -111.3092 4 230.6185 

 5 -105.5907 5 221.1815 

 Additive constant for all log-likelihoods = -18.38. This constant added to the 

 above values gives the log-likelihood including the term that does not 

 depend on the model parameters. 

 Explanation of Tests 

 Test 1: Does response and/or variances differ among Dose levels? (A2 vs. R) 

 Test 2: Are Variances Homogeneous? (A2 vs. A1) 

 Test 3: Are variances adequately modeled? (A2 vs. A3) 

 Test 4: Does Model 2 fit the data? (A3 vs. 2) 

 Test 5a: Does Model 3 fit the data? (A3 vs 3) 

 Test 5b: Is Model 3 better than Model 2? (3 vs. 2) 

 Test 6a: Does Model 4 fit the data? (A3 vs 4) 

 Test 6b: Is Model 4 better than Model 2? (4 vs. 2) 

 Test 7a: Does Model 5 fit the data? (A3 vs 5) 

 Test 7b: Is Model 5 better than Model 3? (5 vs. 3) 

 Test 7c: Is Model 5 better than Model 4? (5 vs. 4) 

 Tests of Interest 

 Test -2*log(Likelihood Ratio) D. F. p-value 

 -------- ------------------------ ------ -------------- 

 Test 1 66.86 6 < 0.0001 

 Test 2 2.974 3 0.3957 

 Test 3 2.974 3 0.3957 

 Test 4 24.32 2 < 0.0001 

 Test 5a 24.32 2 < 0.0001 

 Test 5b -4.547e-013 0 N/A 

 Test 6a 14.61 1 0.0001321 

 Test 6b 9.71 1 0.001833 

 Test 7a 3.175 0 N/A 

 Test 7b 21.15 2 < 0.0001 

 Test 7c 11.44 1 0.00072 

 The p-value for Test 1 is less than .05. There appears to be a 

 difference between response and/or variances among the dose 

 levels, it seems appropriate to model the data. 

 The p-value for Test 2 is greater than .1. A homogeneous 

 variance model appears to be appropriate here. 

 The p-value for Test 3 is greater than .1. The modeled 

 variance appears to be appropriate here. 

 The p-value for Test 4 is less than .1. Model 2 may not adequately 

 describe the data; you may want to consider another model. 

 The p-value for Test 5a is less than .1. Model 3 may not adequately 
Page 102 of 438 

 describe the data; you may want to consider another model. 

 Degrees of freedom for Test 5b are less than or equal to 0. 

 The Chi-Square test for fit is not valid. 

 The p-value for Test 6a is less than .1. Model 4 may not adequately 

 describe the data; you may want to consider another model. 

 The p-value for Test 6b is less than .05. Model 4 appears 

 to fit the data better than Model 2. 

 Degrees of freedom for Test 7a are less than or equal to 0. 

 The Chi-Square test for fit is not valid. 

 The p-value for Test 7b is less than .05. Model 5 appears 

 to fit the data better than Model 3. 

 The p-value for Test 7c is less than .05. Model 5 appears 

 to fit the data better than Model 4. 

 Benchmark Dose Computations: 

 Specified Effect = 0.100000 

 Risk Type = Relative deviation 

 Confidence Level = 0.950000 

 BMD and BMDL by Model 

 Model BMD BMDL 

 ------- ------------ ---------- 

 2 0.839619 0.6992 

 3 0.839619 0.6992 

 4 0.176545 0.122108 

 5 0.288844 0.215638 
Page 103 of 438 

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Mean Response

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Exponential Model 2 with 0.95 Confidence Level

09:37 07/26 2012

BMDL BMD

 

Exponential

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Exponential Model 3 with 0.95 Confidence Level

09:37 07/26 2012

BMDL BMD

 

Exponential
Page 104 of 438 

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Exponential Model 4 with 0.95 Confidence Level

09:37 07/26 2012

BMDL BMD

 

Exponential

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Exponential Model 5 with 0.95 Confidence Level

09:37 07/26 2012

BMDL BMD

 

Exponential
Page 105 of 438 

Female PND42 RBC ChE Inhibition – MRID 46153206 – CCA Acute Study – Single 

Dose

 ==================================================================== 

 Exponential Model. (Version: 1.7; Date: 12/10/2009) 

 Input Data File: C:/Documents and Settings/jliccion/My 

Documents/BMDS220/Data/exp_UntitledData_Setting.(d) 

 Gnuplot Plotting File: 

 Thu Jul 26 09:28:44 2012 

 ==================================================================== 

 BMDS Model Run 

~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ 

 

 The form of the response function by Model: 

 Model 2: Y[dose] = a * exp{sign * b * dose} 

 Model 3: Y[dose] = a * exp{sign * (b * dose)^d} 

 Model 4: Y[dose] = a * [c-(c-1) * exp{-b * dose}] 

 Model 5: Y[dose] = a * [c-(c-1) * exp{-(b * dose)^d}] 

 Note: Y[dose] is the median response for exposure = dose; 

 sign = +1 for increasing trend in data; 

 sign = -1 for decreasing trend. 

 Model 2 is nested within Models 3 and 4. 

 Model 3 is nested within Model 5. 

 Model 4 is nested within Model 5. 

 Dependent variable = Mean 

 Independent variable = Dose 

 Data are assumed to be distributed: normally 

 Variance Model: exp(lnalpha +rho *ln(Y[dose])) 

 rho is set to 0. 

 A constant variance model is fit. 

 Total number of dose groups = 4 

 Total number of records with missing values = 0 

 Maximum number of iterations = 250 

 Relative Function Convergence has been set to: 1e-008 

 Parameter Convergence has been set to: 1e-008 

 MLE solution provided: Exact 

 Initial Parameter Values 

 Variable Model 2 Model 3 Model 4 Model 5 

 -------- ------- ------- ------- ------- 

 lnalpha 9.21771 9.21771 9.21771 9.21771 

 rho(S) 0 0 0 0 

 a 1852.25 1852.25 2571.45 2571.45 

 b 0.0933434 0.0933434 0.558034 0.558034 

 c -- -- 0.55481 

0.55481 

 d -- 1 -- 1 

 (S) = Specified 
Page 106 of 438 

 Parameter Estimates by Model 

 Variable Model 2 Model 3 Model 4 Model 5 

 -------- ------- ------- ------- ------- 

 lnalpha 9.55953 9.55953 9.23825 9.21771 

 rho 0 0 0 0 

 a 2386.35 2386.35 2463.15 2449 

 b 0.0943021 0.0943021 0.941449 1.44792 

 c -- -- 0.6045 0.611678 

 d -- 1 -- 1.51031 

 Table of Stats From Input Data 

 Dose N Obs Mean Obs Std Dev 

 ----- --- ---------- ------------- 

 0 5 2449 110 

 0.1 5 2399 138 

 0.3 5 2214 101 

 5 5 1498 95 

 Estimated Values of Interest 

 Model Dose Est Mean Est Std Scaled Residual 

 ------- ------ ---------- --------- ---------------- 

 2 0 2386 119.1 1.176 

 0.1 2364 119.1 0.6581 

 0.3 2320 119.1 -1.987 

 5 1489 119.1 0.1648 

 3 0 2386 119.1 1.176 

 0.1 2364 119.1 0.6581 

 0.3 2320 119.1 -1.987 

 5 1489 119.1 0.1648 

 4 0 2463 101.4 -0.3121 

 0.1 2376 101.4 0.5155 

 0.3 2223 101.4 -0.2084 

 5 1498 101.4 0.005014 

 5 0 2449 100.4 -1.101e-008 

 0.1 2399 100.4 -7.1e-009 

 0.3 2214 100.4 3.207e-009 

 5 1498 100.4 -1.132e-008 

 Other models for which likelihoods are calculated: 

 Model A1: Yij = Mu(i) + e(ij) 

 Var{e(ij)} = Sigma^2 

 Model A2: Yij = Mu(i) + e(ij) 

 Var{e(ij)} = Sigma(i)^2 

 Model A3: Yij = Mu(i) + e(ij) 

 Var{e(ij)} = exp(lalpha + log(mean(i)) * rho) 

 Model R: Yij = Mu + e(i) 

 Var{e(ij)} = Sigma^2 

 Likelihoods of Interest 
Page 107 of 438 

 Model Log(likelihood) DF AIC 

 ------- ----------------- ---- ------------ 

 A1 -102.1771 5 214.3543 

 A2 -101.7522 8 219.5044 

 A3 -102.1771 5 214.3543 

 R -129.5198 2 263.0397 

 2 -105.5953 3 217.1906 

 3 -105.5953 3 217.1906 

 4 -102.3825 4 212.765 

 5 -102.1771 5 214.3543 

 Additive constant for all log-likelihoods = -18.38. This constant added to the 

 above values gives the log-likelihood including the term that does not 

 depend on the model parameters. 

 Explanation of Tests 

 Test 1: Does response and/or variances differ among Dose levels? (A2 vs. R) 

 Test 2: Are Variances Homogeneous? (A2 vs. A1) 

 Test 3: Are variances adequately modeled? (A2 vs. A3) 

 Test 4: Does Model 2 fit the data? (A3 vs. 2) 

 Test 5a: Does Model 3 fit the data? (A3 vs 3) 

 Test 5b: Is Model 3 better than Model 2? (3 vs. 2) 

 Test 6a: Does Model 4 fit the data? (A3 vs 4) 

 Test 6b: Is Model 4 better than Model 2? (4 vs. 2) 

 Test 7a: Does Model 5 fit the data? (A3 vs 5) 

 Test 7b: Is Model 5 better than Model 3? (5 vs. 3) 

 Test 7c: Is Model 5 better than Model 4? (5 vs. 4) 

 Tests of Interest 

 Test -2*log(Likelihood Ratio) D. F. p-value 

 -------- ------------------------ ------ -------------- 

 Test 1 55.54 6 < 0.0001 

 Test 2 0.8498 3 0.8375 

 Test 3 0.8498 3 0.8375 

 Test 4 6.836 2 0.03277 

 Test 5a 6.836 2 0.03277 

 Test 5b -1.023e-012 0 N/A 

 Test 6a 0.4108 1 0.5216 

 Test 6b 6.426 1 0.01125 

 Test 7a 0 0 N/A 

 Test 7b 6.836 2 0.03277 

 Test 7c 0.4108 1 0.5216 

 The p-value for Test 1 is less than .05. There appears to be a 

 difference between response and/or variances among the dose 

 levels, it seems appropriate to model the data. 

 The p-value for Test 2 is greater than .1. A homogeneous 

 variance model appears to be appropriate here. 

 The p-value for Test 3 is greater than .1. The modeled 

 variance appears to be appropriate here. 

 The p-value for Test 4 is less than .1. Model 2 may not adequately 
Page 108 of 438 

 describe the data; you may want to consider another model. 

 The p-value for Test 5a is less than .1. Model 3 may not adequately 

 describe the data; you may want to consider another model. 

 Degrees of freedom for Test 5b are less than or equal to 0. 

 The Chi-Square test for fit is not valid. 

 The p-value for Test 6a is greater than .1. Model 4 seems 

 to adequately describe the data. 

 The p-value for Test 6b is less than .05. Model 4 appears 

 to fit the data better than Model 2. 

 Degrees of freedom for Test 7a are less than or equal to 0. 

 The Chi-Square test for fit is not valid. 

 The p-value for Test 7b is less than .05. Model 5 appears 

 to fit the data better than Model 3. 

 The p-value for Test 7c is greater than .05. Model 5 does 

 not seem to fit the data better than Model 4. 

 Benchmark Dose Computations: 

 Specified Effect = 0.100000 

 Risk Type = Relative deviation 

 Confidence Level = 0.950000 

 BMD and BMDL by Model 

 Model BMD BMDL 

 ------- ------------ ---------- 

 2 1.11727 0.975931 

 3 1.11727 0.975931 

 4 0.30961 0.203503 

 5 0.309665 0.209803 
Page 109 of 438 

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09:28 07/26 2012

BMDL BMD

 

Exponential

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BMDL BMD

 

Exponential
Page 110 of 438 

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Exponential Model 4 with 0.95 Confidence Level

09:28 07/26 2012

BMDL BMD

 

Exponential

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Exponential Model 5 with 0.95 Confidence Level

09:28 07/26 2012

BMDL BMD

 

Exponential
Page 111 of 438 

Male Adult PND42 Brain ChE Inhibition – MRID 46153204 – CCA Repeat Study – 7 

days

 ==================================================================== 

 Exponential Model. (Version: 1.7; Date: 12/10/2009) 

 Input Data File: C:/Documents and Settings/jliccion/My 

Documents/BMDS220/Data/exp_UntitledData_Setting.(d) 

 Gnuplot Plotting File: 

 Thu Jul 26 09:06:15 2012 

 ==================================================================== 

 BMDS Model Run 

~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ 

 

 The form of the response function by Model: 

 Model 2: Y[dose] = a * exp{sign * b * dose} 

 Model 3: Y[dose] = a * exp{sign * (b * dose)^d} 

 Model 4: Y[dose] = a * [c-(c-1) * exp{-b * dose}] 

 Model 5: Y[dose] = a * [c-(c-1) * exp{-(b * dose)^d}] 

 Note: Y[dose] is the median response for exposure = dose; 

 sign = +1 for increasing trend in data; 

 sign = -1 for decreasing trend. 

 Model 2 is nested within Models 3 and 4. 

 Model 3 is nested within Model 5. 

 Model 4 is nested within Model 5. 

 Dependent variable = Mean 

 Independent variable = Dose 

 Data are assumed to be distributed: normally 

 Variance Model: exp(lnalpha +rho *ln(Y[dose])) 

 The variance is to be modeled as Var(i) = exp(lalpha + log(mean(i)) * rho) 

 Total number of dose groups = 5 

 Total number of records with missing values = 0 

 Maximum number of iterations = 250 

 Relative Function Convergence has been set to: 1e-008 

 Parameter Convergence has been set to: 1e-008 

 MLE solution provided: Exact 

 Initial Parameter Values 

 Variable Model 2 Model 3 Model 4 Model 5 

 -------- ------- ------- ------- ------- 

 lnalpha -0.824905 -0.824905 -0.824905 -0.824905 

 rho -0.64772 -0.64772 -0.64772 -0.64772 

 a 4.08651 4.08651 5.502 5.502 

 b 0.9598 0.9598 6.62714 6.62714 

 c -- -- 0.579876 

0.579876 

 d -- 1 -- 1 

 Parameter Estimates by Model 

 Variable Model 2 Model 3 Model 4 Model 5 
Page 112 of 438 

 -------- ------- ------- ------- ------- 

 lnalpha -7.29471 -6.90537 -7.29471 -6.92184 

 rho 3.98835 3.71141 3.98835 3.71739 

 a 4.98676 4.89846 4.98676 4.90965 

 b 0.982501 1.35533 0.982501 10.6441 

 c -- -- 0 0.682783 

 d -- 1.5818 -- 12.0615 

 Table of Stats From Input Data 

 Dose N Obs Mean Obs Std Dev 

 ----- --- ---------- ------------- 

 0 5 4.57 0.54 

 0.008 5 4.87 0.97 

 0.02 5 5.24 0.13 

 0.08 5 4.75 0.48 

 0.4 5 3.35 0.34 

 Estimated Values of Interest 

 Model Dose Est Mean Est Std Scaled Residual 

 ------- ------ ---------- --------- ---------------- 

 2 0 4.987 0.642 -1.452 

 0.008 4.948 0.632 -0.275 

 0.02 4.89 0.6173 1.269 

 0.08 4.61 0.5489 0.5711 

 0.4 3.366 0.2932 -0.1236 

 3 0 4.898 0.604 -1.216 

 0.008 4.895 0.6032 -0.09134 

 0.02 4.882 0.6003 1.333 

 0.08 4.755 0.5716 -0.0187 

 0.4 3.351 0.2986 -0.007228 

 4 0 4.987 0.642 -1.452 

 0.008 4.948 0.632 -0.275 

 0.02 4.89 0.6173 1.269 

 0.08 4.61 0.5489 0.5711 

 0.4 3.366 0.2932 -0.1236 

 5 0 4.91 0.6045 -1.256 

 0.008 4.91 0.6045 -0.1467 

 0.02 4.91 0.6045 1.222 

 0.08 4.701 0.5576 0.1969 

 0.4 3.352 0.2974 -0.01677 

 Other models for which likelihoods are calculated: 

 Model A1: Yij = Mu(i) + e(ij) 

 Var{e(ij)} = Sigma^2 

 Model A2: Yij = Mu(i) + e(ij) 

 Var{e(ij)} = Sigma(i)^2 

 Model A3: Yij = Mu(i) + e(ij) 

 Var{e(ij)} = exp(lalpha + log(mean(i)) * rho) 

 Model R: Yij = Mu + e(i) 

 Var{e(ij)} = Sigma^2 

 Likelihoods of Interest 
Page 113 of 438 

 Model Log(likelihood) DF AIC 

 ------- ----------------- ---- ------------ 

 A1 4.568212 6 2.863576 

 A2 12.78752 10 -5.575039 

 A3 5.46186 7 3.07628 

 R -7.438085 2 18.87617 

 2 3.555166 4 0.8896673 

 3 3.940053 5 2.119894 

 4 3.555166 4 0.8896673 

 5 4.033916 6 3.932167 

 Additive constant for all log-likelihoods = -22.97. This constant added to the 

 above values gives the log-likelihood including the term that does not 

 depend on the model parameters. 

 Explanation of Tests 

 Test 1: Does response and/or variances differ among Dose levels? (A2 vs. R) 

 Test 2: Are Variances Homogeneous? (A2 vs. A1) 

 Test 3: Are variances adequately modeled? (A2 vs. A3) 

 Test 4: Does Model 2 fit the data? (A3 vs. 2) 

 Test 5a: Does Model 3 fit the data? (A3 vs 3) 

 Test 5b: Is Model 3 better than Model 2? (3 vs. 2) 

 Test 6a: Does Model 4 fit the data? (A3 vs 4) 

 Test 6b: Is Model 4 better than Model 2? (4 vs. 2) 

 Test 7a: Does Model 5 fit the data? (A3 vs 5) 

 Test 7b: Is Model 5 better than Model 3? (5 vs. 3) 

 Test 7c: Is Model 5 better than Model 4? (5 vs. 4) 

 Tests of Interest 

 Test -2*log(Likelihood Ratio) D. F. p-value 

 -------- ------------------------ ------ -------------- 

 Test 1 40.45 8 < 0.0001 

 Test 2 16.44 4 0.002484 

 Test 3 14.65 3 0.00214 

 Test 4 3.813 3 0.2823 

 Test 5a 3.044 2 0.2183 

 Test 5b 0.7698 1 0.3803 

 Test 6a 3.813 3 0.2823 

 Test 6b 0 0 N/A 

 Test 7a 2.856 1 0.09104 

 Test 7b 0.1877 1 0.6648 

 Test 7c 0.9575 2 0.6196 

 The p-value for Test 1 is less than .05. There appears to be a 

 difference between response and/or variances among the dose 

 levels, it seems appropriate to model the data. 

 The p-value for Test 2 is less than .1. A non-homogeneous 

 variance model appears to be appropriate. 

 The p-value for Test 3 is less than .1. You may want to 

 consider a different variance model. 
Page 114 of 438 

 The p-value for Test 4 is greater than .1. Model 2 seems 

 to adequately describe the data. 

 The p-value for Test 5a is greater than .1. Model 3 seems 

 to adequately describe the data. 

 The p-value for Test 5b is greater than .05. Model 3 does 

 not seem to fit the data better than Model 2. 

 The p-value for Test 6a is greater than .1. Model 4 seems 

 to adequately describe the data. 

 Degrees of freedom for Test 6b are less than or equal to 0. 

 The Chi-Square test for fit is not valid. 

 The p-value for Test 7a is less than .1. Model 5 may not adequately 

 describe the data; you may want to consider another model. 

 The p-value for Test 7b is greater than .05. Model 5 does 

 not seem to fit the data better than Model 3. 

 The p-value for Test 7c is greater than .05. Model 5 does 

 not seem to fit the data better than Model 4. 

 Benchmark Dose Computations: 

 Specified Effect = 0.100000 

 Risk Type = Relative deviation 

 Confidence Level = 0.950000 

 BMD and BMDL by Model 

 Model BMD BMDL 

 ------- ------------ ---------- 

 2 0.107237 0.087504 

 3 0.177869 0.0903093 

 4 0.107237 0.0538057 

 5 0.0866814 0.0634267 
Page 115 of 438 

 3

 3.5

 4

 4.5

 5

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 6

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Mean Response

dose

Exponential Model 2 with 0.95 Confidence Level

09:06 07/26 2012

BMDL BMD

 

Exponential

 3

 3.5

 4

 4.5

 5

 5.5

 6

 0 0.05 0.1 0.15 0.2 0.25 0.3 0.35 0.4

Mean Response

dose

Exponential Model 3 with 0.95 Confidence Level

09:06 07/26 2012

BMDL BMD

 

Exponential
Page 116 of 438 

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Mean Response

dose

Exponential Model 4 with 0.95 Confidence Level

09:06 07/26 2012

BMDL BMD

 

Exponential

 3

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Mean Response

dose

Exponential Model 5 with 0.95 Confidence Level

09:06 07/26 2012

BMDL BMD

 

Exponential
Page 117 of 438 

Female Adult PND42 Brain ChE Inhibition – MRID 46153204 – CCA Repeat Study – 7 

days

 ==================================================================== 

 Exponential Model. (Version: 1.7; Date: 12/10/2009) 

 Input Data File: C:/Documents and Settings/jliccion/My 

Documents/BMDS220/Data/exp_UntitledData_Setting.(d) 

 Gnuplot Plotting File: 

 Thu Jul 26 08:50:38 2012 

 ==================================================================== 

 BMDS Model Run 

~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ 

 

 The form of the response function by Model: 

 Model 2: Y[dose] = a * exp{sign * b * dose} 

 Model 3: Y[dose] = a * exp{sign * (b * dose)^d} 

 Model 4: Y[dose] = a * [c-(c-1) * exp{-b * dose}] 

 Model 5: Y[dose] = a * [c-(c-1) * exp{-(b * dose)^d}] 

 Note: Y[dose] is the median response for exposure = dose; 

 sign = +1 for increasing trend in data; 

 sign = -1 for decreasing trend. 

 Model 2 is nested within Models 3 and 4. 

 Model 3 is nested within Model 5. 

 Model 4 is nested within Model 5. 

 Dependent variable = Mean 

 Independent variable = Dose 

 Data are assumed to be distributed: normally 

 Variance Model: exp(lnalpha +rho *ln(Y[dose])) 

 rho is set to 0. 

 A constant variance model is fit. 

 Total number of dose groups = 5 

 Total number of records with missing values = 0 

 Maximum number of iterations = 250 

 Relative Function Convergence has been set to: 1e-008 

 Parameter Convergence has been set to: 1e-008 

 MLE solution provided: Exact 

 Initial Parameter Values 

 Variable Model 2 Model 3 Model 4 Model 5 

 -------- ------- ------- ------- ------- 

 lnalpha -1.76604 -1.76604 -1.76604 -1.76604 

 rho(S) 0 0 0 0 

 a 4.1018 4.1018 5.985 5.985 

 b 1.20825 1.20825 7.34047 7.34047 

 c -- -- 0.507618 

0.507618 

 d -- 1 -- 1 

 (S) = Specified 
Page 118 of 438 

 Parameter Estimates by Model 

 Variable Model 2 Model 3 Model 4 Model 5 

 -------- ------- ------- ------- ------- 

 lnalpha -1.16986 -1.28374 -1.16986 -1.288 

 rho 0 0 0 0 

 a 5.24123 5.12329 5.24123 5.13667 

 b 1.16573 1.78374 1.16573 10.1204 

 c -- -- 0 0.621025 

 d -- 2.2111 -- 12.7803 

 Table of Stats From Input Data 

 Dose N Obs Mean Obs Std Dev 

 ----- --- ---------- ------------- 

 0 5 5.01 0.45 

 0.008 5 4.7 0.53 

 0.02 5 5.7 0.65 

 0.08 5 5.01 0.3 

 0.4 5 3.19 0.27 

 Estimated Values of Interest 

 Model Dose Est Mean Est Std Scaled Residual 

 ------- ------ ---------- --------- ---------------- 

 2 0 5.241 0.5571 -0.928 

 0.008 5.193 0.5571 -1.977 

 0.02 5.12 0.5571 2.326 

 0.08 4.775 0.5571 0.945 

 0.4 3.288 0.5571 -0.3931 

 3 0 5.123 0.5263 -0.4813 

 0.008 5.123 0.5263 -1.797 

 0.02 5.12 0.5263 2.464 

 0.08 5.055 0.5263 -0.1895 

 0.4 3.189 0.5263 0.003803 

 4 0 5.241 0.5571 -0.928 

 0.008 5.193 0.5571 -1.977 

 0.02 5.12 0.5571 2.326 

 0.08 4.775 0.5571 0.945 

 0.4 3.288 0.5571 -0.3931 

 5 0 5.137 0.5252 -0.5393 

 0.008 5.137 0.5252 -1.859 

 0.02 5.137 0.5252 2.398 

 0.08 5.01 0.5252 -5.042e-008 

 0.4 3.19 0.5252 8.864e-011 

 Other models for which likelihoods are calculated: 

 Model A1: Yij = Mu(i) + e(ij) 

 Var{e(ij)} = Sigma^2 

 Model A2: Yij = Mu(i) + e(ij) 

 Var{e(ij)} = Sigma(i)^2 

 Model A3: Yij = Mu(i) + e(ij) 

 Var{e(ij)} = exp(lalpha + log(mean(i)) * rho) 

 Model R: Yij = Mu + e(i) 

 Var{e(ij)} = Sigma^2 
Page 119 of 438 

 Likelihoods of Interest 

 Model Log(likelihood) DF AIC 

 ------- ----------------- ---- ------------ 

 A1 9.575562 6 -7.151123 

 A2 12.17667 10 -4.353339 

 A3 9.575562 6 -7.151123 

 R -10.68695 2 25.37389 

 2 2.123198 3 1.753604 

 3 3.546697 4 0.9066065 

 4 2.123198 3 1.753604 

 5 3.600025 5 2.799951 

 Additive constant for all log-likelihoods = -22.97. This constant added to the 

 above values gives the log-likelihood including the term that does not 

 depend on the model parameters. 

 Explanation of Tests 

 Test 1: Does response and/or variances differ among Dose levels? (A2 vs. R) 

 Test 2: Are Variances Homogeneous? (A2 vs. A1) 

 Test 3: Are variances adequately modeled? (A2 vs. A3) 

 Test 4: Does Model 2 fit the data? (A3 vs. 2) 

 Test 5a: Does Model 3 fit the data? (A3 vs 3) 

 Test 5b: Is Model 3 better than Model 2? (3 vs. 2) 

 Test 6a: Does Model 4 fit the data? (A3 vs 4) 

 Test 6b: Is Model 4 better than Model 2? (4 vs. 2) 

 Test 7a: Does Model 5 fit the data? (A3 vs 5) 

 Test 7b: Is Model 5 better than Model 3? (5 vs. 3) 

 Test 7c: Is Model 5 better than Model 4? (5 vs. 4) 

 Tests of Interest 

 Test -2*log(Likelihood Ratio) D. F. p-value 

 -------- ------------------------ ------ -------------- 

 Test 1 45.73 8 < 0.0001 

 Test 2 5.202 4 0.2672 

 Test 3 5.202 4 0.2672 

 Test 4 14.9 3 0.0019 

 Test 5a 12.06 2 0.002408 

 Test 5b 2.847 1 0.09154 

 Test 6a 14.9 3 0.0019 

 Test 6b -2.309e-014 0 N/A 

 Test 7a 11.95 1 0.0005462 

 Test 7b 0.1067 1 0.744 

 Test 7c 2.954 2 0.2284 

 The p-value for Test 1 is less than .05. There appears to be a 

 difference between response and/or variances among the dose 

 levels, it seems appropriate to model the data. 

 The p-value for Test 2 is greater than .1. A homogeneous 

 variance model appears to be appropriate here. 
Page 120 of 438 

 The p-value for Test 3 is greater than .1. The modeled 

 variance appears to be appropriate here. 

 The p-value for Test 4 is less than .1. Model 2 may not adequately 

 describe the data; you may want to consider another model. 

 The p-value for Test 5a is less than .1. Model 3 may not adequately 

 describe the data; you may want to consider another model. 

 The p-value for Test 5b is greater than .05. Model 3 does 

 not seem to fit the data better than Model 2. 

 The p-value for Test 6a is less than .1. Model 4 may not adequately 

 describe the data; you may want to consider another model. 

 Degrees of freedom for Test 6b are less than or equal to 0. 

 The Chi-Square test for fit is not valid. 

 The p-value for Test 7a is less than .1. Model 5 may not adequately 

 describe the data; you may want to consider another model. 

 The p-value for Test 7b is greater than .05. Model 5 does 

 not seem to fit the data better than Model 3. 

 The p-value for Test 7c is greater than .05. Model 5 does 

 not seem to fit the data better than Model 4. 

 Benchmark Dose Computations: 

 Specified Effect = 0.100000 

 Risk Type = Relative deviation 

 Confidence Level = 0.950000 

 BMD and BMDL by Model 

 Model BMD BMDL 

 ------- ------------ ---------- 

 2 0.0903817 0.0690732 

 3 0.202611 0.0886113 

 4 0.0903817 0.058109 

 5 0.0900742 0.0773327 
Page 121 of 438 

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Exponential Model 2 with 0.95 Confidence Level

08:50 07/26 2012

BMDL BMD

 

Exponential

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Exponential Model 3 with 0.95 Confidence Level

08:50 07/26 2012

BMDL BMD

 

Exponential
Page 122 of 438 

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Exponential Model 4 with 0.95 Confidence Level

08:50 07/26 2012

BMDL BMD

 

Exponential

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Exponential Model 5 with 0.95 Confidence Level

08:50 07/26 2012

BMDL BMD

 

Exponential
Page 123 of 438 

Male Adult PND42 RBC ChE Inhibition – MRID 46153204 – CCA Repeat Study – 7 

days

 ==================================================================== 

 Exponential Model. (Version: 1.7; Date: 12/10/2009) 

 Input Data File: C:/Documents and Settings/jliccion/My 

Documents/BMDS220/Data/exp_UntitledData_Setting.(d) 

 Gnuplot Plotting File: 

 Thu Jul 26 09:19:37 2012 

 ==================================================================== 

 BMDS Model Run 

~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ 

 

 The form of the response function by Model: 

 Model 2: Y[dose] = a * exp{sign * b * dose} 

 Model 3: Y[dose] = a * exp{sign * (b * dose)^d} 

 Model 4: Y[dose] = a * [c-(c-1) * exp{-b * dose}] 

 Model 5: Y[dose] = a * [c-(c-1) * exp{-(b * dose)^d}] 

 Note: Y[dose] is the median response for exposure = dose; 

 sign = +1 for increasing trend in data; 

 sign = -1 for decreasing trend. 

 Model 2 is nested within Models 3 and 4. 

 Model 3 is nested within Model 5. 

 Model 4 is nested within Model 5. 

 Dependent variable = Mean 

 Independent variable = Dose 

 Data are assumed to be distributed: normally 

 Variance Model: exp(lnalpha +rho *ln(Y[dose])) 

 rho is set to 0. 

 A constant variance model is fit. 

 Total number of dose groups = 5 

 Total number of records with missing values = 0 

 Maximum number of iterations = 250 

 Relative Function Convergence has been set to: 1e-008 

 Parameter Convergence has been set to: 1e-008 

 MLE solution provided: Exact 

 Initial Parameter Values 

 Variable Model 2 Model 3 Model 4 Model 5 

 -------- ------- ------- ------- ------- 

 lnalpha 10.295 10.295 10.295 10.295 

 rho(S) 0 0 0 0 

 a 2078.86 2078.86 3036.6 3036.6 

 b 1.48194 1.48194 2.71408 2.71408 

 c -- -- 0.254232 

0.254232 

 d -- 1 -- 1 

 (S) = Specified 
Page 124 of 438 

 Parameter Estimates by Model 

 Variable Model 2 Model 3 Model 4 Model 5 

 -------- ------- ------- ------- ------- 

 lnalpha 10.5072 10.4841 10.5072 10.4841 

 rho 0 0 0 0 

 a 2805.98 2770.13 2805.98 2770.13 

 b 1.45783 1.61757 1.45783 1.61757 

 c -- -- 0 9.11846e-023 

 d -- 1.24183 -- 1.24183 

 Table of Stats From Input Data 

 Dose N Obs Mean Obs Std Dev 

 ----- --- ---------- ------------- 

 0 5 2892 120 

 0.008 5 2637 69 

 0.02 5 2706 170 

 0.08 5 2587 189 

 0.4 5 1544 318 

 Estimated Values of Interest 

 Model Dose Est Mean Est Std Scaled Residual 

 ------- ------ ---------- --------- ---------------- 

 2 0 2806 191.3 1.006 

 0.008 2773 191.3 -1.595 

 0.02 2725 191.3 -0.2262 

 0.08 2497 191.3 1.051 

 0.4 1566 191.3 -0.259 

 3 0 2770 189.1 1.441 

 0.008 2758 189.1 -1.427 

 0.02 2731 189.1 -0.2994 

 0.08 2560 189.1 0.3204 

 0.4 1547 189.1 -0.03928 

 4 0 2806 191.3 1.006 

 0.008 2773 191.3 -1.595 

 0.02 2725 191.3 -0.2262 

 0.08 2497 191.3 1.051 

 0.4 1566 191.3 -0.259 

 5 0 2770 189.1 1.441 

 0.008 2758 189.1 -1.427 

 0.02 2731 189.1 -0.2994 

 0.08 2560 189.1 0.3204 

 0.4 1547 189.1 -0.03928 

 Other models for which likelihoods are calculated: 

 Model A1: Yij = Mu(i) + e(ij) 

 Var{e(ij)} = Sigma^2 

 Model A2: Yij = Mu(i) + e(ij) 

 Var{e(ij)} = Sigma(i)^2 

 Model A3: Yij = Mu(i) + e(ij) 

 Var{e(ij)} = exp(lalpha + log(mean(i)) * rho) 

 Model R: Yij = Mu + e(i) 

 Var{e(ij)} = Sigma^2 
Page 125 of 438 

 Likelihoods of Interest 

 Model Log(likelihood) DF AIC 

 ------- ----------------- ---- ------------ 

 A1 -141.1878 6 294.3755 

 A2 -135.5167 10 291.0334 

 A3 -141.1878 6 294.3755 

 R -168.1686 2 340.3371 

 2 -143.84 3 293.6801 

 3 -143.5513 4 295.1027 

 4 -143.84 3 293.6801 

 5 -143.5513 5 297.1027 

 Additive constant for all log-likelihoods = -22.97. This constant added to the 

 above values gives the log-likelihood including the term that does not 

 depend on the model parameters. 

 Explanation of Tests 

 Test 1: Does response and/or variances differ among Dose levels? (A2 vs. R) 

 Test 2: Are Variances Homogeneous? (A2 vs. A1) 

 Test 3: Are variances adequately modeled? (A2 vs. A3) 

 Test 4: Does Model 2 fit the data? (A3 vs. 2) 

 Test 5a: Does Model 3 fit the data? (A3 vs 3) 

 Test 5b: Is Model 3 better than Model 2? (3 vs. 2) 

 Test 6a: Does Model 4 fit the data? (A3 vs 4) 

 Test 6b: Is Model 4 better than Model 2? (4 vs. 2) 

 Test 7a: Does Model 5 fit the data? (A3 vs 5) 

 Test 7b: Is Model 5 better than Model 3? (5 vs. 3) 

 Test 7c: Is Model 5 better than Model 4? (5 vs. 4) 

 Tests of Interest 

 Test -2*log(Likelihood Ratio) D. F. p-value 

 -------- ------------------------ ------ -------------- 

 Test 1 65.3 8 < 0.0001 

 Test 2 11.34 4 0.02298 

 Test 3 11.34 4 0.02298 

 Test 4 5.305 3 0.1508 

 Test 5a 4.727 2 0.09408 

 Test 5b 0.5774 1 0.4473 

 Test 6a 5.305 3 0.1508 

 Test 6b 5.684e-014 0 N/A 

 Test 7a 4.727 1 0.02969 

 Test 7b 0 1 1 

 Test 7c 0.5774 2 0.7492 

 The p-value for Test 1 is less than .05. There appears to be a 

 difference between response and/or variances among the dose 

 levels, it seems appropriate to model the data. 

 The p-value for Test 2 is less than .1. Consider running 

 a non-homogeneous variance model. 
Page 126 of 438 

 The p-value for Test 3 is less than .1. You may want to 

 consider a different variance model. 

 The p-value for Test 4 is greater than .1. Model 2 seems 

 to adequately describe the data. 

 The p-value for Test 5a is less than .1. Model 3 may not adequately 

 describe the data; you may want to consider another model. 

 The p-value for Test 5b is greater than .05. Model 3 does 

 not seem to fit the data better than Model 2. 

 The p-value for Test 6a is greater than .1. Model 4 seems 

 to adequately describe the data. 

 Degrees of freedom for Test 6b are less than or equal to 0. 

 The Chi-Square test for fit is not valid. 

 The p-value for Test 7a is less than .1. Model 5 may not adequately 

 describe the data; you may want to consider another model. 

 The p-value for Test 7b is greater than .05. Model 5 does 

 not seem to fit the data better than Model 3. 

 The p-value for Test 7c is greater than .05. Model 5 does 

 not seem to fit the data better than Model 4. 

 Benchmark Dose Computations: 

 Specified Effect = 0.100000 

 Risk Type = Relative deviation 

 Confidence Level = 0.950000 

 BMD and BMDL by Model 

 Model BMD BMDL 

 ------- ------------ ---------- 

 2 0.0722722 0.0615642 

 3 0.100957 0.0626997 

 4 0.0722722 0.0510405 

 5 0.100957 0.0626997 
Page 127 of 438 

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Exponential Model 2 with 0.95 Confidence Level

09:19 07/26 2012

BMDL BMD

 

Exponential

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BMDL BMD

 

Exponential
Page 128 of 438 

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Exponential Model 4 with 0.95 Confidence Level

09:19 07/26 2012

BMDL BMD

 

Exponential

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09:19 07/26 2012

BMDL BMD

 

Exponential
Page 129 of 438 

Female Adult PND42 RBC ChE Inhibition – MRID 46153204 – CCA Repeat Study – 7 

days

 ==================================================================== 

 Exponential Model. (Version: 1.7; Date: 12/10/2009) 

 Input Data File: C:/Documents and Settings/jliccion/My 

Documents/BMDS220/Data/exp_UntitledData_Setting.(d) 

 Gnuplot Plotting File: 

 Thu Jul 26 08:57:28 2012 

 ==================================================================== 

 BMDS Model Run 

~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ 

 

 The form of the response function by Model: 

 Model 2: Y[dose] = a * exp{sign * b * dose} 

 Model 3: Y[dose] = a * exp{sign * (b * dose)^d} 

 Model 4: Y[dose] = a * [c-(c-1) * exp{-b * dose}] 

 Model 5: Y[dose] = a * [c-(c-1) * exp{-(b * dose)^d}] 

 Note: Y[dose] is the median response for exposure = dose; 

 sign = +1 for increasing trend in data; 

 sign = -1 for decreasing trend. 

 Model 2 is nested within Models 3 and 4. 

 Model 3 is nested within Model 5. 

 Model 4 is nested within Model 5. 

 Dependent variable = Mean 

 Independent variable = Dose 

 Data are assumed to be distributed: normally 

 Variance Model: exp(lnalpha +rho *ln(Y[dose])) 

 The variance is to be modeled as Var(i) = exp(lalpha + log(mean(i)) * rho) 

 Total number of dose groups = 5 

 Total number of records with missing values = 0 

 Maximum number of iterations = 250 

 Relative Function Convergence has been set to: 1e-008 

 Parameter Convergence has been set to: 1e-008 

 MLE solution provided: Exact 

 Initial Parameter Values 

 Variable Model 2 Model 3 Model 4 Model 5 

 -------- ------- ------- ------- ------- 

 lnalpha 46.3616 46.3616 46.3616 46.3616 

 rho -4.68666 -4.68666 -4.68666 -4.68666 

 a 2159.07 2159.07 2869.65 2869.65 

 b 1.18628 1.18628 6.72111 6.72111 

 c -- -- 0.565193 

0.565193 

 d -- 1 -- 1 

 Parameter Estimates by Model 

 Variable Model 2 Model 3 Model 4 Model 5 

 -------- ------- ------- ------- ------- 
Page 130 of 438 

 lnalpha 46.089 42.7945 46.089 42.7945 

 rho -4.65647 -4.23916 -4.65647 -4.23916 

 a 2738.2 2721.59 2738.2 2721.59 

 b 1.10456 1.3007 1.10456 1.3007 

 c -- -- 0 9.78199e-023 

 d -- 1.17804 -- 1.17804 

 Table of Stats From Input Data 

 Dose N Obs Mean Obs Std Dev 

 ----- --- ---------- ------------- 

 0 5 2733 67 

 0.008 5 2724 129 

 0.02 5 2645 139 

 0.08 5 2556 123 

 0.4 5 1703 307 

 Estimated Values of Interest 

 Model Dose Est Mean Est Std Scaled Residual 

 ------- ------ ---------- --------- ---------------- 

 2 0 2738 101.1 -0.115 

 0.008 2714 103.2 0.2142 

 0.02 2678 106.5 -0.701 

 0.08 2507 124.2 0.8888 

 0.4 1760 282.9 -0.4528 

 3 0 2722 102.9 0.248 

 0.008 2709 103.9 0.3216 

 0.02 2685 105.9 -0.842 

 0.08 2539 119.2 0.3238 

 0.4 1713 274.6 -0.07902 

 4 0 2738 101.1 -0.115 

 0.008 2714 103.2 0.2142 

 0.02 2678 106.5 -0.701 

 0.08 2507 124.2 0.8888 

 0.4 1760 282.9 -0.4528 

 5 0 2722 102.9 0.248 

 0.008 2709 103.9 0.3216 

 0.02 2685 105.9 -0.842 

 0.08 2539 119.2 0.3238 

 0.4 1713 274.6 -0.07902 

 Other models for which likelihoods are calculated: 

 Model A1: Yij = Mu(i) + e(ij) 

 Var{e(ij)} = Sigma^2 

 Model A2: Yij = Mu(i) + e(ij) 

 Var{e(ij)} = Sigma(i)^2 

 Model A3: Yij = Mu(i) + e(ij) 

 Var{e(ij)} = exp(lalpha + log(mean(i)) * rho) 

 Model R: Yij = Mu + e(i) 

 Var{e(ij)} = Sigma^2 

 Likelihoods of Interest 
Page 131 of 438 

 Model Log(likelihood) DF AIC 

 ------- ----------------- ---- ------------ 

 A1 -138.5584 6 289.1167 

 A2 -132.4008 10 284.8015 

 A3 -133.578 7 281.156 

 R -163.4756 2 330.9513 

 2 -134.4407 4 276.8814 

 3 -134.1772 5 278.3544 

 4 -134.4407 4 276.8814 

 5 -134.1772 6 280.3544 

 Additive constant for all log-likelihoods = -22.97. This constant added to the 

 above values gives the log-likelihood including the term that does not 

 depend on the model parameters. 

 Explanation of Tests 

 Test 1: Does response and/or variances differ among Dose levels? (A2 vs. R) 

 Test 2: Are Variances Homogeneous? (A2 vs. A1) 

 Test 3: Are variances adequately modeled? (A2 vs. A3) 

 Test 4: Does Model 2 fit the data? (A3 vs. 2) 

 Test 5a: Does Model 3 fit the data? (A3 vs 3) 

 Test 5b: Is Model 3 better than Model 2? (3 vs. 2) 

 Test 6a: Does Model 4 fit the data? (A3 vs 4) 

 Test 6b: Is Model 4 better than Model 2? (4 vs. 2) 

 Test 7a: Does Model 5 fit the data? (A3 vs 5) 

 Test 7b: Is Model 5 better than Model 3? (5 vs. 3) 

 Test 7c: Is Model 5 better than Model 4? (5 vs. 4) 

 Tests of Interest 

 Test -2*log(Likelihood Ratio) D. F. p-value 

 -------- ------------------------ ------ -------------- 

 Test 1 62.15 8 < 0.0001 

 Test 2 12.32 4 0.01516 

 Test 3 2.354 3 0.5022 

 Test 4 1.725 3 0.6313 

 Test 5a 1.198 2 0.5492 

 Test 5b 0.527 1 0.4679 

 Test 6a 1.725 3 0.6313 

 Test 6b -5.684e-014 0 N/A 

 Test 7a 1.198 1 0.2736 

 Test 7b -5.684e-014 1 N/A 

 Test 7c 0.527 2 0.7684 

 The p-value for Test 1 is less than .05. There appears to be a 

 difference between response and/or variances among the dose 

 levels, it seems appropriate to model the data. 

 The p-value for Test 2 is less than .1. A non-homogeneous 

 variance model appears to be appropriate. 

 The p-value for Test 3 is greater than .1. The modeled 

 variance appears to be appropriate here. 

 The p-value for Test 4 is greater than .1. Model 2 seems 
Page 132 of 438 

 to adequately describe the data. 

 The p-value for Test 5a is greater than .1. Model 3 seems 

 to adequately describe the data. 

 The p-value for Test 5b is greater than .05. Model 3 does 

 not seem to fit the data better than Model 2. 

 The p-value for Test 6a is greater than .1. Model 4 seems 

 to adequately describe the data. 

 Degrees of freedom for Test 6b are less than or equal to 0. 

 The Chi-Square test for fit is not valid. 

 The p-value for Test 7a is greater than .1. Model 5 seems 

 to adequately describe the data. 

 The p-value for Test 7b is less than .05. Model 5 appears 

 to fit the data better than Model 3. 

 The p-value for Test 7c is greater than .05. Model 5 does 

 not seem to fit the data better than Model 4. 

 Benchmark Dose Computations: 

 Specified Effect = 0.100000 

 Risk Type = Relative deviation 

 Confidence Level = 0.950000 

 BMD and BMDL by Model 

 Model BMD BMDL 

 ------- ------------ ---------- 

 2 0.0953868 0.0756576 

 3 0.113817 0.0774704 

 4 0.0953868 0.0692102 

 5 0.113817 0.0774704 
Page 133 of 438 

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Mean Response

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Exponential Model 2 with 0.95 Confidence Level

08:57 07/26 2012

BMDL BMD

 

Exponential

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Exponential Model 3 with 0.95 Confidence Level

08:57 07/26 2012

BMDL BMD

 

Exponential
Page 134 of 438 

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Exponential Model 4 with 0.95 Confidence Level

08:57 07/26 2012

BMDL BMD

 

Exponential

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Exponential Model 5 with 0.95 Confidence Level

08:57 07/26 2012

BMDL BMD

 

Exponential
Page 135 of 438 

Male Pup PND12 Brain ChE Inhibition – MRID 46153204 – CCA Repeat Study – 7 

days

 ==================================================================== 

 Exponential Model. (Version: 1.7; Date: 12/10/2009) 

 Input Data File: C:/Documents and Settings/jliccion/My 

Documents/BMDS220/Data/exp_UntitledData_Setting.(d) 

 Gnuplot Plotting File: 

 Thu Jul 26 08:25:27 2012 

 ==================================================================== 

 BMDS Model Run 

~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ 

 

 The form of the response function by Model: 

 Model 2: Y[dose] = a * exp{sign * b * dose} 

 Model 3: Y[dose] = a * exp{sign * (b * dose)^d} 

 Model 4: Y[dose] = a * [c-(c-1) * exp{-b * dose}] 

 Model 5: Y[dose] = a * [c-(c-1) * exp{-(b * dose)^d}] 

 Note: Y[dose] is the median response for exposure = dose; 

 sign = +1 for increasing trend in data; 

 sign = -1 for decreasing trend. 

 Model 2 is nested within Models 3 and 4. 

 Model 3 is nested within Model 5. 

 Model 4 is nested within Model 5. 

 Dependent variable = Mean 

 Independent variable = Dose 

 Data are assumed to be distributed: normally 

 Variance Model: exp(lnalpha +rho *ln(Y[dose])) 

 The variance is to be modeled as Var(i) = exp(lalpha + log(mean(i)) * rho) 

 Total number of dose groups = 5 

 Total number of records with missing values = 0 

 Maximum number of iterations = 250 

 Relative Function Convergence has been set to: 1e-008 

 Parameter Convergence has been set to: 1e-008 

 MLE solution provided: Exact 

 Initial Parameter Values 

 Variable Model 2 Model 3 Model 4 Model 5 

 -------- ------- ------- ------- ------- 

 lnalpha -8.32332 -8.32332 -8.32332 -8.32332 

 rho 5.39693 5.39693 5.39693 5.39693 

 a 3.3307 3.3307 5.0925 5.0925 

 b 1.63255 1.63255 2.97327 2.97327 

 c -- -- 0.236622 

0.236622 

 d -- 1 -- 1 

 Parameter Estimates by Model 

 Variable Model 2 Model 3 Model 4 Model 5 

 -------- ------- ------- ------- ------- 
Page 136 of 438 

 lnalpha -8.61175 -8.8193 -8.61175 -8.70155 

 rho 5.70001 5.87358 5.70001 5.76615 

 a 4.66116 4.46 4.66116 4.58846 

 b 1.64862 2.42768 1.64862 1.72529 

 c -- -- 0 0 

 d -- 16.5321 -- 1.18577 

 Table of Stats From Input Data 

 Dose N Obs Mean Obs Std Dev 

 ----- --- ---------- ------------- 

 0 5 4.84 1.34 

 0.008 5 4.06 0.38 

 0.02 5 4.85 1.11 

 0.08 5 4.09 0.95 

 0.4 5 2.41 0.18 

 Estimated Values of Interest 

 Model Dose Est Mean Est Std Scaled Residual 

 ------- ------ ---------- --------- ---------------- 

 2 0 4.661 1.084 0.3688 

 0.008 4.6 1.044 -1.156 

 0.02 4.51 0.9871 0.7702 

 0.08 4.085 0.7446 0.01437 

 0.4 2.41 0.1656 -0.006172 

 3 0 4.46 0.9815 0.8658 

 0.008 4.46 0.9815 -0.9113 

 0.02 4.46 0.9815 0.8885 

 0.08 4.46 0.9815 -0.843 

 0.4 2.41 0.161 1.507e-008 

 4 0 4.661 1.084 0.3688 

 0.008 4.6 1.044 -1.156 

 0.02 4.51 0.9871 0.7702 

 0.08 4.085 0.7446 0.01437 

 0.4 2.41 0.1656 -0.006172 

 5 0 4.588 1.043 0.5395 

 0.008 4.56 1.024 -1.092 

 0.02 4.505 0.9886 0.7814 

 0.08 4.17 0.7916 -0.227 

 0.4 2.409 0.1628 0.008265 

 Other models for which likelihoods are calculated: 

 Model A1: Yij = Mu(i) + e(ij) 

 Var{e(ij)} = Sigma^2 

 Model A2: Yij = Mu(i) + e(ij) 

 Var{e(ij)} = Sigma(i)^2 

 Model A3: Yij = Mu(i) + e(ij) 

 Var{e(ij)} = exp(lalpha + log(mean(i)) * rho) 

 Model R: Yij = Mu + e(i) 

 Var{e(ij)} = Sigma^2 

 Likelihoods of Interest 
Page 137 of 438 

 Model Log(likelihood) DF AIC 

 ------- ----------------- ---- ------------ 

 A1 -7.251392 6 26.50278 

 A2 1.972525 10 16.05495 

 A3 0.4520246 7 13.09595 

 R -17.12902 2 38.25804 

 2 -2.591413 4 13.18283 

 3 -2.993934 5 15.98787 

 4 -2.591413 4 13.18283 

 5 -2.523869 5 15.04774 

 Additive constant for all log-likelihoods = -22.97. This constant added to the 

 above values gives the log-likelihood including the term that does not 

 depend on the model parameters. 

 Explanation of Tests 

 Test 1: Does response and/or variances differ among Dose levels? (A2 vs. R) 

 Test 2: Are Variances Homogeneous? (A2 vs. A1) 

 Test 3: Are variances adequately modeled? (A2 vs. A3) 

 Test 4: Does Model 2 fit the data? (A3 vs. 2) 

 Test 5a: Does Model 3 fit the data? (A3 vs 3) 

 Test 5b: Is Model 3 better than Model 2? (3 vs. 2) 

 Test 6a: Does Model 4 fit the data? (A3 vs 4) 

 Test 6b: Is Model 4 better than Model 2? (4 vs. 2) 

 Test 7a: Does Model 5 fit the data? (A3 vs 5) 

 Test 7b: Is Model 5 better than Model 3? (5 vs. 3) 

 Test 7c: Is Model 5 better than Model 4? (5 vs. 4) 

 Tests of Interest 

 Test -2*log(Likelihood Ratio) D. F. p-value 

 -------- ------------------------ ------ -------------- 

 Test 1 38.2 8 < 0.0001 

 Test 2 18.45 4 0.001009 

 Test 3 3.041 3 0.3853 

 Test 4 6.087 3 0.1075 

 Test 5a 6.892 2 0.03187 

 Test 5b -0.805 1 N/A 

 Test 6a 6.087 3 0.1075 

 Test 6b -1.332e-014 0 N/A 

 Test 7a 5.952 2 0.051 

 Test 7b 0.9401 0 N/A 

 Test 7c 0.1351 1 0.7132 

 The p-value for Test 1 is less than .05. There appears to be a 

 difference between response and/or variances among the dose 

 levels, it seems appropriate to model the data. 

 The p-value for Test 2 is less than .1. A non-homogeneous 

 variance model appears to be appropriate. 

 The p-value for Test 3 is greater than .1. The modeled 

 variance appears to be appropriate here. 

 The p-value for Test 4 is greater than .1. Model 2 seems 
Page 138 of 438 

 to adequately describe the data. 

 The p-value for Test 5a is less than .1. Model 3 may not adequately 

 describe the data; you may want to consider another model. 

 The p-value for Test 5b is less than .05. Model 3 appears 

 to fit the data better than Model 2. 

 The p-value for Test 6a is greater than .1. Model 4 seems 

 to adequately describe the data. 

 Degrees of freedom for Test 6b are less than or equal to 0. 

 The Chi-Square test for fit is not valid. 

 The p-value for Test 7a is less than .1. Model 5 may not adequately 

 describe the data; you may want to consider another model. 

 Degrees of freedom for Test 7b are less than or equal to 0. 

 The Chi-Square test for fit is not valid. 

 The p-value for Test 7c is greater than .05. Model 5 does 

 not seem to fit the data better than Model 4. 

 Benchmark Dose Computations: 

 Specified Effect = 0.100000 

 Risk Type = Relative deviation 

 Confidence Level = 0.950000 

 BMD and BMDL by Model 

 Model BMD BMDL 

 ------- ------------ ---------- 

 2 0.0639084 0.0553471 

 3 0.359494 0.0542734 

 4 0.0639084 0.0315121 

 5 0.0868813 0.032075 
Page 139 of 438 

 2

 3

 4

 5

 6

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08:25 07/26 2012

BMDL BMD

 

Exponential

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08:25 07/26 2012

BMDL BMD

 

Exponential
Page 140 of 438 

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08:25 07/26 2012

BMDL BMD

 

Exponential

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08:25 07/26 2012

BMDL BMD

 

Exponential
Page 141 of 438 

Female Pup PND12 Brain ChE Inhibition – MRID 46153204 – CCA Repeat Study – 7 

days

!!! Warning: optimum may not have been found for Model 3 !!! 

!!! Bad completion code in maximum likelihood optimization routine !!! 

!!! Try choosing different initial values !!! 

Warning: optimum may not have been found form model 2 (BMDL). Bad process completion 

in Optimization routine. 

 ==================================================================== 

 Exponential Model. (Version: 1.7; Date: 12/10/2009) 

 Input Data File: C:/Documents and Settings/jliccion/My 

Documents/BMDS220/Data/exp_UntitledData_Setting.(d) 

 Gnuplot Plotting File: 

 Thu Jul 26 08:10:18 2012 

 ==================================================================== 

 BMDS Model Run 

~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ 

 

 The form of the response function by Model: 

 Model 2: Y[dose] = a * exp{sign * b * dose} 

 Model 3: Y[dose] = a * exp{sign * (b * dose)^d} 

 Model 4: Y[dose] = a * [c-(c-1) * exp{-b * dose}] 

 Model 5: Y[dose] = a * [c-(c-1) * exp{-(b * dose)^d}] 

 Note: Y[dose] is the median response for exposure = dose; 

 sign = +1 for increasing trend in data; 

 sign = -1 for decreasing trend. 

 Model 2 is nested within Models 3 and 4. 

 Model 3 is nested within Model 5. 

 Model 4 is nested within Model 5. 

 Dependent variable = Mean 

 Independent variable = Dose 

 Data are assumed to be distributed: normally 

 Variance Model: exp(lnalpha +rho *ln(Y[dose])) 

 The variance is to be modeled as Var(i) = exp(lalpha + log(mean(i)) * rho) 

 Total number of dose groups = 5 

 Total number of records with missing values = 0 

 Maximum number of iterations = 250 

 Relative Function Convergence has been set to: 1e-008 

 Parameter Convergence has been set to: 1e-008 

 MLE solution provided: Exact 

 Initial Parameter Values 

 Variable Model 2 Model 3 Model 4 Model 5 

 -------- ------- ------- ------- ------- 

 lnalpha -4.96504 -4.96504 -4.96504 -4.96504 

 rho 2.50125 2.50125 2.50125 2.50125 

 a 2.88042 2.88042 4.683 4.683 

 b 2.124 2.124 3.44388 3.44388 

 c -- -- 0.201794 
Page 142 of 438 

0.201794 

 d -- 1 -- 1 

 Parameter Estimates by Model 

 Variable Model 2 Model 3 Model 4 Model 5 

 -------- ------- ------- ------- ------- 

 lnalpha -5.4287 -4.96504 -5.4287 -5.50603 

 rho 3.01672 2.50125 3.01672 3.04617 

 a 4.44123 2.88042 4.44123 4.3599 

 b 2.13199 2.124 2.13199 11.3907 

 c -- -- 0 0.434166 

 d -- 1 -- 13.0978 

 Table of Stats From Input Data 

 Dose N Obs Mean Obs Std Dev 

 ----- --- ---------- ------------- 

 0 5 4.46 0.41 

 0.008 5 4.14 0.46 

 0.02 5 4.41 1.07 

 0.08 5 3.79 0.29 

 0.4 5 1.89 0.2 

 Estimated Values of Interest 

 Model Dose Est Mean Est Std Scaled Residual 

 ------- ------ ---------- --------- ---------------- 

 2 0 4.441 0.6278 0.06684 

 0.008 4.366 0.6119 -0.8264 

 0.02 4.256 0.5887 0.5855 

 0.08 3.745 0.4854 0.2081 

 0.4 1.893 0.1735 -0.03801 

 3 0 0 0 1.#IO 

 0.008 0 0 1.#IO 

 0.02 0 0 1.#IO 

 0.08 0 0 1.#IO 

 0.4 0 0 1.#IO 

 4 0 4.441 0.6278 0.06684 

 0.008 4.366 0.6119 -0.8264 

 0.02 4.256 0.5887 0.5855 

 0.08 3.745 0.4854 0.2081 

 0.4 1.893 0.1735 -0.03801 

 5 0 4.36 0.6003 0.3729 

 0.008 4.36 0.6003 -0.8191 

 0.02 4.36 0.6003 0.1866 

 0.08 3.728 0.4729 0.2945 

 0.4 1.893 0.1685 -0.0388 

 Other models for which likelihoods are calculated: 

 Model A1: Yij = Mu(i) + e(ij) 

 Var{e(ij)} = Sigma^2 

 Model A2: Yij = Mu(i) + e(ij) 

 Var{e(ij)} = Sigma(i)^2 
Page 143 of 438 

 Model A3: Yij = Mu(i) + e(ij) 

 Var{e(ij)} = exp(lalpha + log(mean(i)) * rho) 

 Model R: Yij = Mu + e(i) 

 Var{e(ij)} = Sigma^2 

 Likelihoods of Interest 

 Model Log(likelihood) DF AIC 

 ------- ----------------- ---- ------------ 

 A1 4.15743 6 3.685141 

 A2 12.5282 10 -5.056394 

 A3 8.746326 7 -3.492651 

 R -14.50785 2 33.0157 

 2 7.305294 4 -6.610589 

 3 -357.2157 4 722.4313 

 4 7.305294 4 -6.610589 

 5 7.805507 6 -3.611014 

 Additive constant for all log-likelihoods = -22.97. This constant added to the 

 above values gives the log-likelihood including the term that does not 

 depend on the model parameters. 

 Explanation of Tests 

 Test 1: Does response and/or variances differ among Dose levels? (A2 vs. R) 

 Test 2: Are Variances Homogeneous? (A2 vs. A1) 

 Test 3: Are variances adequately modeled? (A2 vs. A3) 

 Test 4: Does Model 2 fit the data? (A3 vs. 2) 

 Test 5a: Does Model 3 fit the data? (A3 vs 3) 

 Test 5b: Is Model 3 better than Model 2? (3 vs. 2) 

 Test 6a: Does Model 4 fit the data? (A3 vs 4) 

 Test 6b: Is Model 4 better than Model 2? (4 vs. 2) 

 Test 7a: Does Model 5 fit the data? (A3 vs 5) 

 Test 7b: Is Model 5 better than Model 3? (5 vs. 3) 

 Test 7c: Is Model 5 better than Model 4? (5 vs. 4) 

 Tests of Interest 

 Test -2*log(Likelihood Ratio) D. F. p-value 

 -------- ------------------------ ------ -------------- 

 Test 1 54.07 8 < 0.0001 

 Test 2 16.74 4 0.00217 

 Test 3 7.564 3 0.05594 

 Test 4 2.882 3 0.4102 

 Test 5a 731.9 3 < 0.0001 

 Test 5b -729 0 N/A 

 Test 6a 2.882 3 0.4102 

 Test 6b 1.776e-014 0 N/A 

 Test 7a 1.882 1 0.1701 

 Test 7b 730 2 < 0.0001 

 Test 7c 1 2 0.6064 

 The p-value for Test 1 is less than .05. There appears to be a 

 difference between response and/or variances among the dose 
Page 144 of 438 

 levels, it seems appropriate to model the data. 

 The p-value for Test 2 is less than .1. A non-homogeneous 

 variance model appears to be appropriate. 

 The p-value for Test 3 is less than .1. You may want to 

 consider a different variance model. 

 The p-value for Test 4 is greater than .1. Model 2 seems 

 to adequately describe the data. 

 The p-value for Test 5a is less than .1. Model 3 may not adequately 

 describe the data; you may want to consider another model. 

 Degrees of freedom for Test 5b are less than or equal to 0. 

 The Chi-Square test for fit is not valid. 

 The p-value for Test 6a is greater than .1. Model 4 seems 

 to adequately describe the data. 

 Degrees of freedom for Test 6b are less than or equal to 0. 

 The Chi-Square test for fit is not valid. 

 The p-value for Test 7a is greater than .1. Model 5 seems 

 to adequately describe the data. 

 The p-value for Test 7b is less than .05. Model 5 appears 

 to fit the data better than Model 3. 

 The p-value for Test 7c is greater than .05. Model 5 does 

 not seem to fit the data better than Model 4. 

 Benchmark Dose Computations: 

 Specified Effect = 0.100000 

 Risk Type = Relative deviation 

 Confidence Level = 0.950000 

 BMD and BMDL by Model 

 Model BMD BMDL 

 ------- ------------ ---------- 

 2 0.0494189 0.0444033 

 3 0.0496049 Bad completion 

 4 0.0494189 0.0284767 

 5 0.0774737 0.0317548 
Page 145 of 438 

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Exponential Model 2 with 0.95 Confidence Level

08:10 07/26 2012

BMDL BMD

 

Exponential

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dose

Exponential Model 3 with 0.95 Confidence Level

08:10 07/26 2012

BMDL BMD

 

Exponential
Page 146 of 438 

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08:10 07/26 2012

BMDL BMD

 

Exponential

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08:10 07/26 2012

BMDL BMD

 

Exponential
Page 147 of 438 

Male Pup PND12 RBC ChE Inhibition – MRID 46153204 – CCA Repeat Study – 7 days

!!! Warning: optimum may not have been found for Model 3 !!! 

!!! Bad completion code in maximum likelihood optimization routine !!! 

!!! Try choosing different initial values !!! 

 ==================================================================== 

 Exponential Model. (Version: 1.7; Date: 12/10/2009) 

 Input Data File: C:/Documents and Settings/jliccion/My 

Documents/BMDS220/Data/exp_UntitledData_Setting.(d) 

 Gnuplot Plotting File: 

 Thu Jul 26 08:39:43 2012 

 ==================================================================== 

 BMDS Model Run 

~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ 

 

 The form of the response function by Model: 

 Model 2: Y[dose] = a * exp{sign * b * dose} 

 Model 3: Y[dose] = a * exp{sign * (b * dose)^d} 

 Model 4: Y[dose] = a * [c-(c-1) * exp{-b * dose}] 

 Model 5: Y[dose] = a * [c-(c-1) * exp{-(b * dose)^d}] 

 Note: Y[dose] is the median response for exposure = dose; 

 sign = +1 for increasing trend in data; 

 sign = -1 for decreasing trend. 

 Model 2 is nested within Models 3 and 4. 

 Model 3 is nested within Model 5. 

 Model 4 is nested within Model 5. 

 Dependent variable = Mean 

 Independent variable = Dose 

 Data are assumed to be distributed: normally 

 Variance Model: exp(lnalpha +rho *ln(Y[dose])) 

 The variance is to be modeled as Var(i) = exp(lalpha + log(mean(i)) * rho) 

 Total number of dose groups = 5 

 Total number of records with missing values = 0 

 Maximum number of iterations = 250 

 Relative Function Convergence has been set to: 1e-008 

 Parameter Convergence has been set to: 1e-008 

 MLE solution provided: Exact 

 Initial Parameter Values 

 Variable Model 2 Model 3 Model 4 Model 5 

 -------- ------- ------- ------- ------- 

 lnalpha -11.204 -11.204 -11.204 -11.204 

 rho 2.83836 2.83836 2.83836 2.83836 

 a 1966.78 1966.78 3319.05 3319.05 

 b 2.34368 2.34368 3.68739 3.68739 

 c -- -- 0.187554 

0.187554 
Page 148 of 438 

 d -- 1 -- 1 

 Parameter Estimates by Model 

 Variable Model 2 Model 3 Model 4 Model 5 

 -------- ------- ------- ------- ------- 

 lnalpha -13.0404 -11.204 -12.8334 -12.772 

 rho 3.06382 2.83836 3.0367 3.02866 

 a 3163.42 1966.78 3184.98 3167.55 

 b 2.3357 2.34368 3.02504 4.53849 

 c -- -- 0.131671 0.299528 

 d -- 1 -- 1.17643 

 Table of Stats From Input Data 

 Dose N Obs Mean Obs Std Dev 

 ----- --- ---------- ------------- 

 0 5 3161 315 

 0.008 5 3148 441 

 0.02 5 3046 206 

 0.08 5 2565 355 

 0.4 5 1245 86 

 Estimated Values of Interest 

 Model Dose Est Mean Est Std Scaled Residual 

 ------- ------ ---------- --------- ---------------- 

 2 0 3163 339 -0.01598 

 0.008 3105 329.5 0.2928 

 0.02 3019 315.6 0.191 

 0.08 2624 254.6 -0.5204 

 0.4 1243 81.03 0.06038 

 3 0 0 0 1.#IO 

 0.008 0 0 1.#IO 

 0.02 0 0 1.#IO 

 0.08 0 0 1.#IO 

 0.4 0 0 1.#IO 

 4 0 3185 340.6 -0.1574 

 0.008 3119 329.9 0.1976 

 0.02 3023 314.6 0.1662 

 0.08 2591 248.9 -0.2293 

 0.4 1244 81.72 0.02589 

 5 0 3168 337.2 -0.04347 

 0.008 3123 330 0.1685 

 0.02 3040 316.7 0.04582 

 0.08 2586 248.1 -0.1938 

 0.4 1244 81.88 0.02595 

 Other models for which likelihoods are calculated: 

 Model A1: Yij = Mu(i) + e(ij) 

 Var{e(ij)} = Sigma^2 

 Model A2: Yij = Mu(i) + e(ij) 

 Var{e(ij)} = Sigma(i)^2 

 Model A3: Yij = Mu(i) + e(ij) 
Page 149 of 438 

 Var{e(ij)} = exp(lalpha + log(mean(i)) * rho) 

 Model R: Yij = Mu + e(i) 

 Var{e(ij)} = Sigma^2 

 Likelihoods of Interest 

 Model Log(likelihood) DF AIC 

 ------- ----------------- ---- ------------ 

 A1 -152.8372 6 317.6744 

 A2 -147.1905 10 314.381 

 A3 -148.9075 7 311.8151 

 R -178.8974 2 361.7948 

 2 -149.0642 4 306.1284 

 3 -926.4701 4 1860.94 

 4 -149.0035 5 308.007 

 5 -148.9843 6 309.9687 

 Additive constant for all log-likelihoods = -22.97. This constant added to the 

 above values gives the log-likelihood including the term that does not 

 depend on the model parameters. 

 Explanation of Tests 

 Test 1: Does response and/or variances differ among Dose levels? (A2 vs. R) 

 Test 2: Are Variances Homogeneous? (A2 vs. A1) 

 Test 3: Are variances adequately modeled? (A2 vs. A3) 

 Test 4: Does Model 2 fit the data? (A3 vs. 2) 

 Test 5a: Does Model 3 fit the data? (A3 vs 3) 

 Test 5b: Is Model 3 better than Model 2? (3 vs. 2) 

 Test 6a: Does Model 4 fit the data? (A3 vs 4) 

 Test 6b: Is Model 4 better than Model 2? (4 vs. 2) 

 Test 7a: Does Model 5 fit the data? (A3 vs 5) 

 Test 7b: Is Model 5 better than Model 3? (5 vs. 3) 

 Test 7c: Is Model 5 better than Model 4? (5 vs. 4) 

 Tests of Interest 

 Test -2*log(Likelihood Ratio) D. F. p-value 

 -------- ------------------------ ------ -------------- 

 Test 1 63.41 8 < 0.0001 

 Test 2 11.29 4 0.02346 

 Test 3 3.434 3 0.3294 

 Test 4 0.3133 3 0.9575 

 Test 5a 1555 3 < 0.0001 

 Test 5b -1555 0 N/A 

 Test 6a 0.1919 2 0.9085 

 Test 6b 0.1214 1 0.7275 

 Test 7a 0.1536 1 0.6951 

 Test 7b 1555 2 < 0.0001 

 Test 7c 0.03831 1 0.8448 

 The p-value for Test 1 is less than .05. There appears to be a 

 difference between response and/or variances among the dose 

 levels, it seems appropriate to model the data. 
Page 150 of 438 

 The p-value for Test 2 is less than .1. A non-homogeneous 

 variance model appears to be appropriate. 

 The p-value for Test 3 is greater than .1. The modeled 

 variance appears to be appropriate here. 

 The p-value for Test 4 is greater than .1. Model 2 seems 

 to adequately describe the data. 

 The p-value for Test 5a is less than .1. Model 3 may not adequately 

 describe the data; you may want to consider another model. 

 Degrees of freedom for Test 5b are less than or equal to 0. 

 The Chi-Square test for fit is not valid. 

 The p-value for Test 6a is greater than .1. Model 4 seems 

 to adequately describe the data. 

 The p-value for Test 6b is greater than .05. Model 4 does 

 not seem to fit the data better than Model 2. 

 The p-value for Test 7a is greater than .1. Model 5 seems 

 to adequately describe the data. 

 The p-value for Test 7b is less than .05. Model 5 appears 

 to fit the data better than Model 3. 

 The p-value for Test 7c is greater than .05. Model 5 does 

 not seem to fit the data better than Model 4. 

 Benchmark Dose Computations: 

 Specified Effect = 0.100000 

 Risk Type = Relative deviation 

 Confidence Level = 0.950000 

 BMD and BMDL by Model 

 Model BMD BMDL 

 ------- ------------ ---------- 

 2 0.0451088 0.0418769 

 3 0.0449551 0.00137288 

 4 0.0404466 0.0266464 

 5 0.0449313 0.0267158 
Page 151 of 438 

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Exponential Model 2 with 0.95 Confidence Level

08:39 07/26 2012

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Exponential

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Exponential Model 3 with 0.95 Confidence Level

08:39 07/26 2012

BMDL BMD

 

Exponential
Page 152 of 438 

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Exponential Model 4 with 0.95 Confidence Level

08:39 07/26 2012

BMDL BMD

 

Exponential

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Exponential Model 5 with 0.95 Confidence Level

08:39 07/26 2012

BMDL BMD

 

Exponential
Page 153 of 438 

Female Pup PND12 RBC ChE Inhibition – MRID 46153204 – CCA Repeat Study – 7 

days

 ==================================================================== 

 Exponential Model. (Version: 1.7; Date: 12/10/2009) 

 Input Data File: C:/Documents and Settings/jliccion/My 

Documents/BMDS220/Data/exp_UntitledData_Setting.(d) 

 Gnuplot Plotting File: 

 Thu Jul 26 08:17:15 2012 

 ==================================================================== 

 BMDS Model Run 

~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ 

 

 The form of the response function by Model: 

 Model 2: Y[dose] = a * exp{sign * b * dose} 

 Model 3: Y[dose] = a * exp{sign * (b * dose)^d} 

 Model 4: Y[dose] = a * [c-(c-1) * exp{-b * dose}] 

 Model 5: Y[dose] = a * [c-(c-1) * exp{-(b * dose)^d}] 

 Note: Y[dose] is the median response for exposure = dose; 

 sign = +1 for increasing trend in data; 

 sign = -1 for decreasing trend. 

 Model 2 is nested within Models 3 and 4. 

 Model 3 is nested within Model 5. 

 Model 4 is nested within Model 5. 

 Dependent variable = Mean 

 Independent variable = Dose 

 Data are assumed to be distributed: normally 

 Variance Model: exp(lnalpha +rho *ln(Y[dose])) 

 rho is set to 0. 

 A constant variance model is fit. 

 Total number of dose groups = 5 

 Total number of records with missing values = 0 

 Maximum number of iterations = 250 

 Relative Function Convergence has been set to: 1e-008 

 Parameter Convergence has been set to: 1e-008 

 MLE solution provided: Exact 

 Initial Parameter Values 

 Variable Model 2 Model 3 Model 4 Model 5 

 -------- ------- ------- ------- ------- 

 lnalpha 11.1801 11.1801 11.1801 11.1801 

 rho(S) 0 0 0 0 

 a 1921.56 1921.56 3427.2 3427.2 

 b 2.2818 2.2818 9.0659 9.0659 

 c -- -- 0.347361 

0.347361 

 d -- 1 -- 1 

 (S) = Specified 
Page 154 of 438 

 Parameter Estimates by Model 

 Variable Model 2 Model 3 Model 4 Model 5 

 -------- ------- ------- ------- ------- 

 lnalpha 11.4766 11.4766 11.3104 11.2855 

 rho 0 0 0 0 

 a 3093.99 3093.99 3198.93 3152.37 

 b 2.47298 2.47298 6.86053 8.62251 

 c -- -- 0.347938 0.394135 

 d -- 1 -- 1.36772 

 Table of Stats From Input Data 

 Dose N Obs Mean Obs Std Dev 

 ----- --- ---------- ------------- 

 0 5 3066 307 

 0.008 5 3264 326 

 0.02 5 2906 429 

 0.08 5 2297 214 

 0.4 5 1250 133 

 Estimated Values of Interest 

 Model Dose Est Mean Est Std Scaled Residual 

 ------- ------ ---------- --------- ---------------- 

 2 0 3094 310.5 -0.2016 

 0.008 3033 310.5 1.661 

 0.02 2945 310.5 -0.2786 

 0.08 2539 310.5 -1.74 

 0.4 1151 310.5 0.7159 

 3 0 3094 310.5 -0.2016 

 0.008 3033 310.5 1.661 

 0.02 2945 310.5 -0.2786 

 0.08 2539 310.5 -1.74 

 0.4 1151 310.5 0.7159 

 4 0 3199 285.8 -1.04 

 0.008 3088 285.8 1.381 

 0.02 2931 285.8 -0.1995 

 0.08 2318 285.8 -0.1634 

 0.4 1247 285.8 0.02229 

 5 0 3152 282.2 -0.6843 

 0.008 3104 282.2 1.27 

 0.02 2987 282.2 -0.6446 

 0.08 2289 282.2 0.06497 

 0.4 1251 282.2 -0.006065 

 Other models for which likelihoods are calculated: 

 Model A1: Yij = Mu(i) + e(ij) 

 Var{e(ij)} = Sigma^2 

 Model A2: Yij = Mu(i) + e(ij) 

 Var{e(ij)} = Sigma(i)^2 

 Model A3: Yij = Mu(i) + e(ij) 

 Var{e(ij)} = exp(lalpha + log(mean(i)) * rho) 

 Model R: Yij = Mu + e(i) 

 Var{e(ij)} = Sigma^2 
Page 155 of 438 

 Likelihoods of Interest 

 Model Log(likelihood) DF AIC 

 ------- ----------------- ---- ------------ 

 A1 -152.251 6 316.502 

 A2 -148.8683 10 317.7367 

 A3 -152.251 6 316.502 

 R -178.871 2 361.742 

 2 -155.9578 3 317.9155 

 3 -155.9578 3 317.9155 

 4 -153.8804 4 315.7608 

 5 -153.5684 5 317.1367 

 Additive constant for all log-likelihoods = -22.97. This constant added to the 

 above values gives the log-likelihood including the term that does not 

 depend on the model parameters. 

 Explanation of Tests 

 Test 1: Does response and/or variances differ among Dose levels? (A2 vs. R) 

 Test 2: Are Variances Homogeneous? (A2 vs. A1) 

 Test 3: Are variances adequately modeled? (A2 vs. A3) 

 Test 4: Does Model 2 fit the data? (A3 vs. 2) 

 Test 5a: Does Model 3 fit the data? (A3 vs 3) 

 Test 5b: Is Model 3 better than Model 2? (3 vs. 2) 

 Test 6a: Does Model 4 fit the data? (A3 vs 4) 

 Test 6b: Is Model 4 better than Model 2? (4 vs. 2) 

 Test 7a: Does Model 5 fit the data? (A3 vs 5) 

 Test 7b: Is Model 5 better than Model 3? (5 vs. 3) 

 Test 7c: Is Model 5 better than Model 4? (5 vs. 4) 

 Tests of Interest 

 Test -2*log(Likelihood Ratio) D. F. p-value 

 -------- ------------------------ ------ -------------- 

 Test 1 60.01 8 < 0.0001 

 Test 2 6.765 4 0.1488 

 Test 3 6.765 4 0.1488 

 Test 4 7.413 3 0.05982 

 Test 5a 7.413 3 0.05982 

 Test 5b 0 0 N/A 

 Test 6a 3.259 2 0.196 

 Test 6b 4.155 1 0.04152 

 Test 7a 2.635 1 0.1046 

 Test 7b 4.779 2 0.09169 

 Test 7c 0.6241 1 0.4295 

 The p-value for Test 1 is less than .05. There appears to be a 

 difference between response and/or variances among the dose 

 levels, it seems appropriate to model the data. 

 The p-value for Test 2 is greater than .1. A homogeneous 

 variance model appears to be appropriate here. 
Page 156 of 438 

 The p-value for Test 3 is greater than .1. The modeled 

 variance appears to be appropriate here. 

 The p-value for Test 4 is less than .1. Model 2 may not adequately 

 describe the data; you may want to consider another model. 

 The p-value for Test 5a is less than .1. Model 3 may not adequately 

 describe the data; you may want to consider another model. 

 Degrees of freedom for Test 5b are less than or equal to 0. 

 The Chi-Square test for fit is not valid. 

 The p-value for Test 6a is greater than .1. Model 4 seems 

 to adequately describe the data. 

 The p-value for Test 6b is less than .05. Model 4 appears 

 to fit the data better than Model 2. 

 The p-value for Test 7a is greater than .1. Model 5 seems 

 to adequately describe the data. 

 The p-value for Test 7b is greater than .05. Model 5 does 

 not seem to fit the data better than Model 3. 

 The p-value for Test 7c is greater than .05. Model 5 does 

 not seem to fit the data better than Model 4. 

 Benchmark Dose Computations: 

 Specified Effect = 0.100000 

 Risk Type = Relative deviation 

 Confidence Level = 0.950000 

 BMD and BMDL by Model 

 Model BMD BMDL 

 ------- ------------ ---------- 

 2 0.0426047 0.0344945 

 3 0.0426047 0.0344945 

 4 0.0242662 0.0168301 

 5 0.0331546 0.0175827 
Page 157 of 438 

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Mean Response

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Exponential Model 2 with 0.95 Confidence Level

08:17 07/26 2012

BMDL BMD

 

Exponential

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Mean Response

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Exponential Model 3 with 0.95 Confidence Level

08:17 07/26 2012

BMDL BMD

 

Exponential
Page 158 of 438 

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Mean Response

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Exponential Model 4 with 0.95 Confidence Level

08:17 07/26 2012

BMDL BMD

 

Exponential

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08:17 07/26 2012

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Exponential
Page 159 of 438 

Male Adult Brain ChE Inhibition – MRID 46153208 – CCA Repeat Study – 4 weeks

 ==================================================================== 

 Exponential Model. (Version: 1.7; Date: 12/10/2009) 

 Input Data File: C:/Documents and Settings/jliccion/My 

Documents/BMDS220/Data/exp_UntitledData_Setting.(d) 

 Gnuplot Plotting File: 

 Thu Jul 26 14:06:38 2012 

 ==================================================================== 

 BMDS Model Run 

~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ 

 

 The form of the response function by Model: 

 Model 2: Y[dose] = a * exp{sign * b * dose} 

 Model 3: Y[dose] = a * exp{sign * (b * dose)^d} 

 Model 4: Y[dose] = a * [c-(c-1) * exp{-b * dose}] 

 Model 5: Y[dose] = a * [c-(c-1) * exp{-(b * dose)^d}] 

 Note: Y[dose] is the median response for exposure = dose; 

 sign = +1 for increasing trend in data; 

 sign = -1 for decreasing trend. 

 Model 2 is nested within Models 3 and 4. 

 Model 3 is nested within Model 5. 

 Model 4 is nested within Model 5. 

 Dependent variable = Mean 

 Independent variable = Dose 

 Data are assumed to be distributed: normally 

 Variance Model: exp(lnalpha +rho *ln(Y[dose])) 

 The variance is to be modeled as Var(i) = exp(lalpha + log(mean(i)) * rho) 

 Total number of dose groups = 4 

 Total number of records with missing values = 0 

 Maximum number of iterations = 250 

 Relative Function Convergence has been set to: 1e-008 

 Parameter Convergence has been set to: 1e-008 

 MLE solution provided: Exact 

 Initial Parameter Values 

 Variable Model 2 Model 3 Model 4 Model 5 

 -------- ------- ------- ------- ------- 

 lnalpha -3.94117 -3.94117 -3.94117 -3.94117 

 rho 1.98884 1.98884 1.98884 1.98884 

 a 3.41909 3.41909 5.334 5.334 

 b 1.74036 1.74036 8.08458 8.08458 

 c -- -- 0.441016 

0.441016 

 d -- 1 -- 1 

 Parameter Estimates by Model 

 Variable Model 2 Model 3 Model 4 Model 5 

 -------- ------- ------- ------- ------- 

 lnalpha -5.2548 -5.28939 -5.2548 -5.28939 

 rho 3.07692 3.08263 3.07692 3.08263 
Page 160 of 438 

 a 4.9674 4.88667 4.9674 4.88667 

 b 1.745 2.44432 1.745 3.14325 

 c -- -- 0 0.498516 

 d -- 16.9716 -- 6.35018 

 Table of Stats From Input Data 

 Dose N Obs Mean Obs Std Dev 

 ----- --- ---------- ------------- 

 0 5 4.72 0.87 

 0.008 5 5.08 0.3 

 0.02 5 4.86 1.26 

 0.4 5 2.47 0.32 

 Estimated Values of Interest 

 Model Dose Est Mean Est Std Scaled Residual 

 ------- ------ ---------- --------- ---------------- 

 2 0 4.967 0.8509 -0.6501 

 0.008 4.899 0.8329 0.4872 

 0.02 4.797 0.8065 0.1746 

 0.4 2.472 0.2908 -0.01285 

 3 0 4.887 0.8192 -0.4549 

 0.008 4.887 0.8192 0.5277 

 0.02 4.887 0.8192 -0.07279 

 0.4 2.47 0.2862 -2.551e-008 

 4 0 4.967 0.8509 -0.6501 

 0.008 4.899 0.8329 0.4872 

 0.02 4.797 0.8065 0.1746 

 0.4 2.472 0.2908 -0.01285 

 5 0 4.887 0.8192 -0.4549 

 0.008 4.887 0.8192 0.5277 

 0.02 4.887 0.8192 -0.07278 

 0.4 2.47 0.2862 -1.148e-008 

 Other models for which likelihoods are calculated: 

 Model A1: Yij = Mu(i) + e(ij) 

 Var{e(ij)} = Sigma^2 

 Model A2: Yij = Mu(i) + e(ij) 

 Var{e(ij)} = Sigma(i)^2 

 Model A3: Yij = Mu(i) + e(ij) 

 Var{e(ij)} = exp(lalpha + log(mean(i)) * rho) 

 Model R: Yij = Mu + e(i) 

 Var{e(ij)} = Sigma^2 

 Likelihoods of Interest 

 Model Log(likelihood) DF AIC 

 ------- ----------------- ---- ------------ 

 A1 -3.21505 5 16.4301 

 A2 3.489223 8 9.021555 

 A3 -0.550366 6 13.10073 

 R -14.81746 2 33.63492 

 2 -1.026699 4 10.0534 
Page 161 of 438 

 3 -0.7543121 5 11.50862 

 4 -1.026699 4 10.0534 

 5 -0.7543122 6 13.50862 

 Additive constant for all log-likelihoods = -18.38. This constant added to the 

 above values gives the log-likelihood including the term that does not 

 depend on the model parameters. 

 Explanation of Tests 

 Test 1: Does response and/or variances differ among Dose levels? (A2 vs. R) 

 Test 2: Are Variances Homogeneous? (A2 vs. A1) 

 Test 3: Are variances adequately modeled? (A2 vs. A3) 

 Test 4: Does Model 2 fit the data? (A3 vs. 2) 

 Test 5a: Does Model 3 fit the data? (A3 vs 3) 

 Test 5b: Is Model 3 better than Model 2? (3 vs. 2) 

 Test 6a: Does Model 4 fit the data? (A3 vs 4) 

 Test 6b: Is Model 4 better than Model 2? (4 vs. 2) 

 Test 7a: Does Model 5 fit the data? (A3 vs 5) 

 Test 7b: Is Model 5 better than Model 3? (5 vs. 3) 

 Test 7c: Is Model 5 better than Model 4? (5 vs. 4) 

 Tests of Interest 

 Test -2*log(Likelihood Ratio) D. F. p-value 

 -------- ------------------------ ------ -------------- 

 Test 1 36.61 6 < 0.0001 

 Test 2 13.41 3 0.003831 

 Test 3 8.079 2 0.0176 

 Test 4 0.9527 2 0.6211 

 Test 5a 0.4079 1 0.523 

 Test 5b 0.5448 1 0.4605 

 Test 6a 0.9527 2 0.6211 

 Test 6b 6.217e-015 0 N/A 

 Test 7a 0.4079 0 N/A 

 Test 7b -1.39e-007 1 N/A 

 Test 7c 0.5448 2 0.7616 

 The p-value for Test 1 is less than .05. There appears to be a 

 difference between response and/or variances among the dose 

 levels, it seems appropriate to model the data. 

 The p-value for Test 2 is less than .1. A non-homogeneous 

 variance model appears to be appropriate. 

 The p-value for Test 3 is less than .1. You may want to 

 consider a different variance model. 

 The p-value for Test 4 is greater than .1. Model 2 seems 

 to adequately describe the data. 

 The p-value for Test 5a is greater than .1. Model 3 seems 

 to adequately describe the data. 

 The p-value for Test 5b is greater than .05. Model 3 does 

 not seem to fit the data better than Model 2. 
Page 162 of 438 

 The p-value for Test 6a is greater than .1. Model 4 seems 

 to adequately describe the data. 

 Degrees of freedom for Test 6b are less than or equal to 0. 

 The Chi-Square test for fit is not valid. 

 Degrees of freedom for Test 7a are less than or equal to 0. 

 The Chi-Square test for fit is not valid. 

 The p-value for Test 7b is less than .05. Model 5 appears 

 to fit the data better than Model 3. 

 The p-value for Test 7c is greater than .05. Model 5 does 

 not seem to fit the data better than Model 4. 

 Benchmark Dose Computations: 

 Specified Effect = 0.100000 

 Risk Type = Relative deviation 

 Confidence Level = 0.950000 

 BMD and BMDL by Model 

 Model BMD BMDL 

 ------- ------------ ---------- 

 2 0.0603784 0.0511959 

 3 0.358308 0.0521715 

 4 0.0603784 0.0152893 

 5 0.25108 0.0175685 
Page 163 of 438 

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14:06 07/26 2012

BMDL BMD

 

Exponential

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14:06 07/26 2012

BMDL BMD

 

Exponential
Page 164 of 438 

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Exponential Model 4 with 0.95 Confidence Level

14:06 07/26 2012

BMDL BMD

 

Exponential

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14:06 07/26 2012

BMDL BMD

 

Exponential
Page 165 of 438 

Female Adult Brain ChE Inhibition – MRID 46153208 – CCA Repeat Study – 4 weeks

 ==================================================================== 

 Exponential Model. (Version: 1.7; Date: 12/10/2009) 

 Input Data File: C:/Documents and Settings/jliccion/My 

Documents/BMDS220/Data/exp_UntitledData_Setting.(d) 

 Gnuplot Plotting File: 

 Thu Jul 26 13:38:51 2012 

 ==================================================================== 

 BMDS Model Run 

~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ 

 

 The form of the response function by Model: 

 Model 2: Y[dose] = a * exp{sign * b * dose} 

 Model 3: Y[dose] = a * exp{sign * (b * dose)^d} 

 Model 4: Y[dose] = a * [c-(c-1) * exp{-b * dose}] 

 Model 5: Y[dose] = a * [c-(c-1) * exp{-(b * dose)^d}] 

 Note: Y[dose] is the median response for exposure = dose; 

 sign = +1 for increasing trend in data; 

 sign = -1 for decreasing trend. 

 Model 2 is nested within Models 3 and 4. 

 Model 3 is nested within Model 5. 

 Model 4 is nested within Model 5. 

 Dependent variable = Mean 

 Independent variable = Dose 

 Data are assumed to be distributed: normally 

 Variance Model: exp(lnalpha +rho *ln(Y[dose])) 

 The variance is to be modeled as Var(i) = exp(lalpha + log(mean(i)) * rho) 

 Total number of dose groups = 4 

 Total number of records with missing values = 0 

 Maximum number of iterations = 250 

 Relative Function Convergence has been set to: 1e-008 

 Parameter Convergence has been set to: 1e-008 

 MLE solution provided: Exact 

 Initial Parameter Values 

 Variable Model 2 Model 3 Model 4 Model 5 

 -------- ------- ------- ------- ------- 

 lnalpha -2.58909 -2.58909 -2.58909 -2.58909 

 rho 0.993649 0.993649 0.993649 0.993649 

 a 3.48202 3.48202 5.25 5.25 

 b 1.36299 1.36299 7.62073 7.62073 

 c -- -- 0.49161 

0.49161 

 d -- 1 -- 1 

 Parameter Estimates by Model 

 Variable Model 2 Model 3 Model 4 Model 5 

 -------- ------- ------- ------- ------- 
Page 166 of 438 

 lnalpha -2.93057 -2.93057 -3.23987 -3.23987 

 rho 1.36971 1.36971 1.50693 1.50693 

 a 4.66926 4.66926 4.96171 4.96171 

 b 1.3702 1.3702 20.16 20.16 

 c -- -- 0.548498 0.548498 

 d -- 1 -- 1 

 Table of Stats From Input Data 

 Dose N Obs Mean Obs Std Dev 

 ----- --- ---------- ------------- 

 0 5 5 0.95 

 0.008 5 4.49 0.65 

 0.02 5 4.33 0.28 

 0.4 5 2.71 0.52 

 Estimated Values of Interest 

 Model Dose Est Mean Est Std Scaled Residual 

 ------- ------ ---------- --------- ---------------- 

 2 0 4.669 0.6637 1.114 

 0.008 4.618 0.6587 -0.4357 

 0.02 4.543 0.6514 -0.7313 

 0.4 2.699 0.456 0.05341 

 3 0 4.669 0.6637 1.114 

 0.008 4.618 0.6587 -0.4357 

 0.02 4.543 0.6514 -0.7313 

 0.4 2.699 0.456 0.05341 

 4 0 4.962 0.6616 0.1294 

 0.008 4.628 0.6278 -0.4916 

 0.02 4.218 0.5855 0.4264 

 0.4 2.722 0.4209 -0.06476 

 5 0 4.962 0.6616 0.1294 

 0.008 4.628 0.6278 -0.4916 

 0.02 4.218 0.5855 0.4264 

 0.4 2.722 0.4209 -0.06476 

 Other models for which likelihoods are calculated: 

 Model A1: Yij = Mu(i) + e(ij) 

 Var{e(ij)} = Sigma^2 

 Model A2: Yij = Mu(i) + e(ij) 

 Var{e(ij)} = Sigma(i)^2 

 Model A3: Yij = Mu(i) + e(ij) 

 Var{e(ij)} = exp(lalpha + log(mean(i)) * rho) 

 Model R: Yij = Mu + e(i) 

 Var{e(ij)} = Sigma^2 

 Likelihoods of Interest 

 Model Log(likelihood) DF AIC 

 ------- ----------------- ---- ------------ 

 A1 0.9434142 5 8.113172 

 A2 4.276277 8 7.447445 

 A3 1.599232 6 8.801536 
Page 167 of 438 

 R -10.68106 2 25.36212 

 2 0.20663 4 7.58674 

 3 0.20663 4 7.58674 

 4 1.396782 5 7.206435 

 5 1.396782 5 7.206435 

 Additive constant for all log-likelihoods = -18.38. This constant added to the 

 above values gives the log-likelihood including the term that does not 

 depend on the model parameters. 

 Explanation of Tests 

 Test 1: Does response and/or variances differ among Dose levels? (A2 vs. R) 

 Test 2: Are Variances Homogeneous? (A2 vs. A1) 

 Test 3: Are variances adequately modeled? (A2 vs. A3) 

 Test 4: Does Model 2 fit the data? (A3 vs. 2) 

 Test 5a: Does Model 3 fit the data? (A3 vs 3) 

 Test 5b: Is Model 3 better than Model 2? (3 vs. 2) 

 Test 6a: Does Model 4 fit the data? (A3 vs 4) 

 Test 6b: Is Model 4 better than Model 2? (4 vs. 2) 

 Test 7a: Does Model 5 fit the data? (A3 vs 5) 

 Test 7b: Is Model 5 better than Model 3? (5 vs. 3) 

 Test 7c: Is Model 5 better than Model 4? (5 vs. 4) 

 Tests of Interest 

 Test -2*log(Likelihood Ratio) D. F. p-value 

 -------- ------------------------ ------ -------------- 

 Test 1 29.91 6 < 0.0001 

 Test 2 6.666 3 0.08335 

 Test 3 5.354 2 0.06877 

 Test 4 2.785 2 0.2484 

 Test 5a 2.785 2 0.2484 

 Test 5b -2.421e-013 0 N/A 

 Test 6a 0.4049 1 0.5246 

 Test 6b 2.38 1 0.1229 

 Test 7a 0.4049 1 0.5246 

 Test 7b 2.38 1 0.1229 

 Test 7c 1.776e-015 0 N/A 

 The p-value for Test 1 is less than .05. There appears to be a 

 difference between response and/or variances among the dose 

 levels, it seems appropriate to model the data. 

 The p-value for Test 2 is less than .1. A non-homogeneous 

 variance model appears to be appropriate. 

 The p-value for Test 3 is less than .1. You may want to 

 consider a different variance model. 

 The p-value for Test 4 is greater than .1. Model 2 seems 

 to adequately describe the data. 

 The p-value for Test 5a is greater than .1. Model 3 seems 

 to adequately describe the data. 
Page 168 of 438 

 Degrees of freedom for Test 5b are less than or equal to 0. 

 The Chi-Square test for fit is not valid. 

 The p-value for Test 6a is greater than .1. Model 4 seems 

 to adequately describe the data. 

 The p-value for Test 6b is greater than .05. Model 4 does 

 not seem to fit the data better than Model 2. 

 The p-value for Test 7a is greater than .1. Model 5 seems 

 to adequately describe the data. 

 The p-value for Test 7b is greater than .05. Model 5 does 

 not seem to fit the data better than Model 3. 

 Degrees of freedom for Test 7c are less than or equal to 0. 

 The Chi-Square test for fit is not valid. 

 Benchmark Dose Computations: 

 Specified Effect = 0.100000 

 Risk Type = Relative deviation 

 Confidence Level = 0.950000 

 BMD and BMDL by Model 

 Model BMD BMDL 

 ------- ------------ ---------- 

 2 0.0768945 0.0587043 Note: model 2 is in line with other 

data 

 3 0.0768945 0.0587043 

 4 0.0124188 0.0058289 

 5 0.0124188 0.0058289 
Page 169 of 438 

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Mean Response

dose

Exponential Model 2 with 0.95 Confidence Level

13:38 07/26 2012

BMDL BMD

 

Exponential

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Exponential Model 3 with 0.95 Confidence Level

13:38 07/26 2012

BMDL BMD

 

Exponential
Page 170 of 438 

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Mean Response

dose

Exponential Model 4 with 0.95 Confidence Level

13:38 07/26 2012

BMDL BMD

 

Exponential

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Exponential Model 5 with 0.95 Confidence Level

13:38 07/26 2012

BMDL BMD

 

Exponential
Page 171 of 438 

Male Adult RBC ChE Inhibition – MRID 46153208 – CCA Repeat Study – 4 weeks

 ==================================================================== 

 Exponential Model. (Version: 1.7; Date: 12/10/2009) 

 Input Data File: C:/Documents and Settings/jliccion/My 

Documents/BMDS220/Data/exp_UntitledData_Setting.(d) 

 Gnuplot Plotting File: 

 Thu Jul 26 14:24:32 2012 

 ==================================================================== 

 BMDS Model Run 

~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ 

 

 The form of the response function by Model: 

 Model 2: Y[dose] = a * exp{sign * b * dose} 

 Model 3: Y[dose] = a * exp{sign * (b * dose)^d} 

 Model 4: Y[dose] = a * [c-(c-1) * exp{-b * dose}] 

 Model 5: Y[dose] = a * [c-(c-1) * exp{-(b * dose)^d}] 

 Note: Y[dose] is the median response for exposure = dose; 

 sign = +1 for increasing trend in data; 

 sign = -1 for decreasing trend. 

 Model 2 is nested within Models 3 and 4. 

 Model 3 is nested within Model 5. 

 Model 4 is nested within Model 5. 

 Dependent variable = Mean 

 Independent variable = Dose 

 Data are assumed to be distributed: normally 

 Variance Model: exp(lnalpha +rho *ln(Y[dose])) 

 The variance is to be modeled as Var(i) = exp(lalpha + log(mean(i)) * rho) 

 Total number of dose groups = 4 

 Total number of records with missing values = 0 

 Maximum number of iterations = 250 

 Relative Function Convergence has been set to: 1e-008 

 Parameter Convergence has been set to: 1e-008 

 MLE solution provided: Exact 

 Initial Parameter Values 

 Variable Model 2 Model 3 Model 4 Model 5 

 -------- ------- ------- ------- ------- 

 lnalpha 25.9833 25.9833 25.9833 25.9833 

 rho -2.1644 -2.1644 -2.1644 -2.1644 

 a 1767.69 1767.69 2276.4 2276.4 

 b 0.866793 0.866793 6.19176 6.19176 

 c -- -- 0.630486 

0.630486 

 d -- 1 -- 1 

 Parameter Estimates by Model 

 Variable Model 2 Model 3 Model 4 Model 5 

 -------- ------- ------- ------- ------- 

 lnalpha 6.1897 6.1897 12.7833 17.5797 

 rho 0.485225 0.485225 -0.401355 -1.04105 
Page 172 of 438 

 a 2129.55 2129.55 2190.71 2167.98 

 b 0.871327 0.871327 13.9059 36.672 

 c -- -- 0.686638 0.695881 

 d -- 1 -- 3.96345 

 Table of Stats From Input Data 

 Dose N Obs Mean Obs Std Dev 

 ----- --- ---------- ------------- 

 0 5 2168 135 

 0.008 5 2160 56 

 0.02 5 2005 201 

 0.4 5 1507 143 

 Estimated Values of Interest 

 Model Dose Est Mean Est Std Scaled Residual 

 ------- ------ ---------- --------- ---------------- 

 2 0 2130 141.8 0.6065 

 0.008 2115 141.5 0.7148 

 0.02 2093 141.2 -1.39 

 0.4 1503 130.3 0.0708 

 3 0 2130 141.8 0.6065 

 0.008 2115 141.5 0.7148 

 0.02 2093 141.2 -1.39 

 0.4 1503 130.3 0.0708 

 4 0 2191 127.5 -0.3983 

 0.008 2118 128.4 0.7242 

 0.02 2024 129.5 -0.3286 

 0.4 1507 137.4 0.002292 

 5 0 2168 120.5 0.0002958 

 0.008 2163 120.6 -0.05368 

 0.02 2001 125.6 0.0767 

 0.4 1509 145.5 -0.02549 

 Other models for which likelihoods are calculated: 

 Model A1: Yij = Mu(i) + e(ij) 

 Var{e(ij)} = Sigma^2 

 Model A2: Yij = Mu(i) + e(ij) 

 Var{e(ij)} = Sigma(i)^2 

 Model A3: Yij = Mu(i) + e(ij) 

 Var{e(ij)} = exp(lalpha + log(mean(i)) * rho) 

 Model R: Yij = Mu + e(i) 

 Var{e(ij)} = Sigma^2 

 Likelihoods of Interest 

 Model Log(likelihood) DF AIC 

 ------- ----------------- ---- ------------ 

 A1 -107.0761 5 224.1521 

 A2 -103.7524 8 223.5049 

 A3 -106.9872 6 225.9744 

 R -123.9714 2 251.9428 

 2 -108.6306 4 225.2612 
Page 173 of 438 

 3 -108.6306 4 225.2612 

 4 -107.4493 5 224.8986 

 5 -106.9872 6 225.9744 

 Additive constant for all log-likelihoods = -18.38. This constant added to the 

 above values gives the log-likelihood including the term that does not 

 depend on the model parameters. 

 Explanation of Tests 

 Test 1: Does response and/or variances differ among Dose levels? (A2 vs. R) 

 Test 2: Are Variances Homogeneous? (A2 vs. A1) 

 Test 3: Are variances adequately modeled? (A2 vs. A3) 

 Test 4: Does Model 2 fit the data? (A3 vs. 2) 

 Test 5a: Does Model 3 fit the data? (A3 vs 3) 

 Test 5b: Is Model 3 better than Model 2? (3 vs. 2) 

 Test 6a: Does Model 4 fit the data? (A3 vs 4) 

 Test 6b: Is Model 4 better than Model 2? (4 vs. 2) 

 Test 7a: Does Model 5 fit the data? (A3 vs 5) 

 Test 7b: Is Model 5 better than Model 3? (5 vs. 3) 

 Test 7c: Is Model 5 better than Model 4? (5 vs. 4) 

 Tests of Interest 

 Test -2*log(Likelihood Ratio) D. F. p-value 

 -------- ------------------------ ------ -------------- 

 Test 1 40.44 6 < 0.0001 

 Test 2 6.647 3 0.08403 

 Test 3 6.47 2 0.03937 

 Test 4 3.287 2 0.1933 

 Test 5a 3.287 2 0.1933 

 Test 5b -2.842e-014 0 N/A 

 Test 6a 0.9242 1 0.3364 

 Test 6b 2.363 1 0.1243 

 Test 7a -1.481e-011 0 N/A 

 Test 7b 3.287 2 0.1933 

 Test 7c 0.9242 1 0.3364 

 The p-value for Test 1 is less than .05. There appears to be a 

 difference between response and/or variances among the dose 

 levels, it seems appropriate to model the data. 

 The p-value for Test 2 is less than .1. A non-homogeneous 

 variance model appears to be appropriate. 

 The p-value for Test 3 is less than .1. You may want to 

 consider a different variance model. 

 The p-value for Test 4 is greater than .1. Model 2 seems 

 to adequately describe the data. 

 The p-value for Test 5a is greater than .1. Model 3 seems 

 to adequately describe the data. 

 Degrees of freedom for Test 5b are less than or equal to 0. 

 The Chi-Square test for fit is not valid. 
Page 174 of 438 

 The p-value for Test 6a is greater than .1. Model 4 seems 

 to adequately describe the data. 

 The p-value for Test 6b is greater than .05. Model 4 does 

 not seem to fit the data better than Model 2. 

 Degrees of freedom for Test 7a are less than or equal to 0. 

 The Chi-Square test for fit is not valid. 

 The p-value for Test 7b is greater than .05. Model 5 does 

 not seem to fit the data better than Model 3. 

 The p-value for Test 7c is greater than .05. Model 5 does 

 not seem to fit the data better than Model 4. 

 Benchmark Dose Computations: 

 Specified Effect = 0.100000 

 Risk Type = Relative deviation 

 Confidence Level = 0.950000 

 BMD and BMDL by Model 

 Model BMD BMDL 

 ------- ------------ ---------- 

 2 0.12092 0.0972113 Note: model 2 is in line with other 

data 

 3 0.12092 0.0972113 

 4 0.0276407 0.0138321 

 5 0.0216227 0.0154075 
Page 175 of 438 

 1400

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 1800

 2000

 2200

 2400

 0 0.05 0.1 0.15 0.2 0.25 0.3 0.35 0.4

Mean Response

dose

Exponential Model 2 with 0.95 Confidence Level

14:24 07/26 2012

BMDL BMD

 

Exponential

 1400

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 0 0.05 0.1 0.15 0.2 0.25 0.3 0.35 0.4

Mean Response

dose

Exponential Model 3 with 0.95 Confidence Level

14:24 07/26 2012

BMDL BMD

 

Exponential
Page 176 of 438 

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Mean Response

dose

Exponential Model 4 with 0.95 Confidence Level

14:24 07/26 2012

BMDL BMD

 

Exponential

 1400

 1600

 1800

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 0 0.05 0.1 0.15 0.2 0.25 0.3 0.35 0.4

Mean Response

dose

Exponential Model 5 with 0.95 Confidence Level

14:24 07/26 2012

BMDL BMD

 

Exponential
Page 177 of 438 

Female Adult RBC ChE Inhibition – MRID 46153208 – CCA Repeat Study – 4 weeks

 ==================================================================== 

 Exponential Model. (Version: 1.7; Date: 12/10/2009) 

 Input Data File: C:/Documents and Settings/jliccion/My 

Documents/BMDS220/Data/exp_UntitledData_Setting.(d) 

 Gnuplot Plotting File: 

 Thu Jul 26 13:55:06 2012 

 ==================================================================== 

 BMDS Model Run 

~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ 

 

 The form of the response function by Model: 

 Model 2: Y[dose] = a * exp{sign * b * dose} 

 Model 3: Y[dose] = a * exp{sign * (b * dose)^d} 

 Model 4: Y[dose] = a * [c-(c-1) * exp{-b * dose}] 

 Model 5: Y[dose] = a * [c-(c-1) * exp{-(b * dose)^d}] 

 Note: Y[dose] is the median response for exposure = dose; 

 sign = +1 for increasing trend in data; 

 sign = -1 for decreasing trend. 

 Model 2 is nested within Models 3 and 4. 

 Model 3 is nested within Model 5. 

 Model 4 is nested within Model 5. 

 Dependent variable = Mean 

 Independent variable = Dose 

 Data are assumed to be distributed: normally 

 Variance Model: exp(lnalpha +rho *ln(Y[dose])) 

 rho is set to 0. 

 A constant variance model is fit. 

 Total number of dose groups = 4 

 Total number of records with missing values = 0 

 Maximum number of iterations = 250 

 Relative Function Convergence has been set to: 1e-008 

 Parameter Convergence has been set to: 1e-008 

 MLE solution provided: Exact 

 Initial Parameter Values 

 Variable Model 2 Model 3 Model 4 Model 5 

 -------- ------- ------- ------- ------- 

 lnalpha 10.4112 10.4112 10.4112 10.4112 

 rho(S) 0 0 0 0 

 a 2041.96 2041.96 2522.1 2522.1 

 b 0.617849 0.617849 5.55669 5.55669 

 c -- -- 0.686125 

0.686125 

 d -- 1 -- 1 

 (S) = Specified 

 Parameter Estimates by Model 
Page 178 of 438 

 Variable Model 2 Model 3 Model 4 Model 5 

 -------- ------- ------- ------- ------- 

 lnalpha 10.5153 10.4857 10.5153 10.4857 

 rho 0 0 0 0 

 a 2330.92 2319.33 2330.92 2319.33 

 b 0.615354 1.97119 0.615354 3.01794 

 c -- -- 0 0.779574 

 d -- 5.93393 -- 7.42855 

 Table of Stats From Input Data 

 Dose N Obs Mean Obs Std Dev 

 ----- --- ---------- ------------- 

 0 5 2274 321 

 0.008 5 2282 158 

 0.02 5 2402 143 

 0.4 5 1817 133 

 Estimated Values of Interest 

 Model Dose Est Mean Est Std Scaled Residual 

 ------- ------ ---------- --------- ---------------- 

 2 0 2331 192 -0.6628 

 0.008 2319 192 -0.4363 

 0.02 2302 192 1.16 

 0.4 1822 192 -0.06215 

 3 0 2319 189.2 -0.5358 

 0.008 2319 189.2 -0.4412 

 0.02 2319 189.2 0.977 

 0.4 1817 189.2 -5.485e-008 

 4 0 2331 192 -0.6628 

 0.008 2319 192 -0.4363 

 0.02 2302 192 1.16 

 0.4 1822 192 -0.06215 

 5 0 2319 189.2 -0.5358 

 0.008 2319 189.2 -0.4412 

 0.02 2319 189.2 0.977 

 0.4 1817 189.2 3.486e-007 

 Other models for which likelihoods are calculated: 

 Model A1: Yij = Mu(i) + e(ij) 

 Var{e(ij)} = Sigma^2 

 Model A2: Yij = Mu(i) + e(ij) 

 Var{e(ij)} = Sigma(i)^2 

 Model A3: Yij = Mu(i) + e(ij) 

 Var{e(ij)} = exp(lalpha + log(mean(i)) * rho) 

 Model R: Yij = Mu + e(i) 

 Var{e(ij)} = Sigma^2 

 Likelihoods of Interest 

 Model Log(likelihood) DF AIC 

 ------- ----------------- ---- ------------ 
Page 179 of 438 

 A1 -114.1117 5 238.2233 

 A2 -111.2047 8 238.4094 

 A3 -114.1117 5 238.2233 

 R -123.2795 2 250.5591 

 2 -115.1533 3 236.3065 

 3 -114.8568 4 237.7137 

 4 -115.1533 3 236.3065 

 5 -114.8568 5 239.7137 

 Additive constant for all log-likelihoods = -18.38. This constant added to the 

 above values gives the log-likelihood including the term that does not 

 depend on the model parameters. 

 Explanation of Tests 

 Test 1: Does response and/or variances differ among Dose levels? (A2 vs. R) 

 Test 2: Are Variances Homogeneous? (A2 vs. A1) 

 Test 3: Are variances adequately modeled? (A2 vs. A3) 

 Test 4: Does Model 2 fit the data? (A3 vs. 2) 

 Test 5a: Does Model 3 fit the data? (A3 vs 3) 

 Test 5b: Is Model 3 better than Model 2? (3 vs. 2) 

 Test 6a: Does Model 4 fit the data? (A3 vs 4) 

 Test 6b: Is Model 4 better than Model 2? (4 vs. 2) 

 Test 7a: Does Model 5 fit the data? (A3 vs 5) 

 Test 7b: Is Model 5 better than Model 3? (5 vs. 3) 

 Test 7c: Is Model 5 better than Model 4? (5 vs. 4) 

 Tests of Interest 

 Test -2*log(Likelihood Ratio) D. F. p-value 

 -------- ------------------------ ------ -------------- 

 Test 1 24.15 6 0.0004902 

 Test 2 5.814 3 0.121 

 Test 3 5.814 3 0.121 

 Test 4 2.083 2 0.3529 

 Test 5a 1.49 1 0.2222 

 Test 5b 0.5929 1 0.4413 

 Test 6a 2.083 2 0.3529 

 Test 6b 0 0 N/A 

 Test 7a 1.49 0 N/A 

 Test 7b 2.381e-007 1 0.9996 

 Test 7c 0.5929 2 0.7435 

 The p-value for Test 1 is less than .05. There appears to be a 

 difference between response and/or variances among the dose 

 levels, it seems appropriate to model the data. 

 The p-value for Test 2 is greater than .1. A homogeneous 

 variance model appears to be appropriate here. 

 The p-value for Test 3 is greater than .1. The modeled 

 variance appears to be appropriate here. 

 The p-value for Test 4 is greater than .1. Model 2 seems 

 to adequately describe the data. 
Page 180 of 438 

 The p-value for Test 5a is greater than .1. Model 3 seems 

 to adequately describe the data. 

 The p-value for Test 5b is greater than .05. Model 3 does 

 not seem to fit the data better than Model 2. 

 The p-value for Test 6a is greater than .1. Model 4 seems 

 to adequately describe the data. 

 Degrees of freedom for Test 6b are less than or equal to 0. 

 The Chi-Square test for fit is not valid. 

 Degrees of freedom for Test 7a are less than or equal to 0. 

 The Chi-Square test for fit is not valid. 

 The p-value for Test 7b is greater than .05. Model 5 does 

 not seem to fit the data better than Model 3. 

 The p-value for Test 7c is greater than .05. Model 5 does 

 not seem to fit the data better than Model 4. 

 Benchmark Dose Computations: 

 Specified Effect = 0.100000 

 Risk Type = Relative deviation 

 Confidence Level = 0.950000 

 BMD and BMDL by Model 

 Model BMD BMDL 

 ------- ------------ ---------- 

 2 0.171219 0.124387 

 3 0.347192 0.128842 

 4 0.171219 0.0419733 

 5 0.309645 0.0212892 
Page 181 of 438 

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Exponential Model 2 with 0.95 Confidence Level

13:55 07/26 2012

BMDL BMD

 

Exponential

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Exponential Model 3 with 0.95 Confidence Level

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BMDL BMD

 

Exponential
Page 182 of 438 

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Exponential Model 4 with 0.95 Confidence Level

13:55 07/26 2012

BMDL BMD

 

Exponential

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Exponential
Page 183 of 438 

 Developmental Neurotoxicity Study

MRID 46153201 - Developmental Neurotoxicity in Rats – Maternal Brain, Gestation Day 

22 

===============================================================

===== 

 Exponential Model. (Version: 1.7; Date: 12/10/2009) 

 Input Data File: F:/dicrotophos/exp_Dicrotophos DNT Mat Brain GD22 

NHV_Setting.(d) 

 Gnuplot Plotting File: 

 Tue Sep 25 15:00:58 2012 

===============================================================

===== 

 BMDS Model Run 

~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

~~~ 

 

 The form of the response function by Model: 

 Model 2: Y[dose] = a * exp{sign * b * dose} 

 Model 3: Y[dose] = a * exp{sign * (b * dose)^d} 

 Model 4: Y[dose] = a * [c-(c-1) * exp{-b * dose}] 

 Model 5: Y[dose] = a * [c-(c-1) * exp{-(b * dose)^d}] 

 Note: Y[dose] is the median response for exposure = dose; 

 sign = +1 for increasing trend in data; 

 sign = -1 for decreasing trend. 

 Model 2 is nested within Models 3 and 4. 

 Model 3 is nested within Model 5. 

 Model 4 is nested within Model 5. 

 Dependent variable = Mean 

 Independent variable = Dose 

 Data are assumed to be distributed: normally 

 Variance Model: exp(lnalpha +rho *ln(Y[dose])) 

 The variance is to be modeled as Var(i) = exp(lalpha + log(mean(i)) * rho) 

 Total number of dose groups = 4 

 Total number of records with missing values = 0 

 Maximum number of iterations = 250 

 Relative Function Convergence has been set to: 1e-008 
Page 184 of 438 

 Parameter Convergence has been set to: 1e-008 

 MLE solution provided: Exact 

 Initial Parameter Values 

 Variable Model 2 Model 3 Model 4 Model 5 

 -------- ------- ------- ------- ------- 

 lnalpha -3.20581 -3.20581 -3.20581 -3.20581 

 rho 2.13666 2.13666 2.13666 2.13666 

 a 1.70351 1.70351 5.019 5.019 

 b 1.45984 1.45984 4.42973 4.42973 

 c -- -- 0.195448 0.195448 

 d -- 1 -- 1 

 Parameter Estimates by Model 

 Variable Model 2 Model 3 Model 4 Model 5 

 -------- ------- ------- ------- ------- 

 lnalpha -3.30468 -3.30468 -3.42465 -3.43268 

 rho 2.40575 2.40575 2.16818 2.15031 

 a 4.25643 4.25643 4.92726 4.74926 

 b 1.44382 1.44382 4.74643 4.79315 

 c -- -- 0.201992 0.21699 

 d -- 1 -- 1.43371 

 Table of Stats From Input Data 

 Dose N Obs Mean Obs Std Dev 

 ----- --- ---------- ------------- 

 0 5 4.78 0.99 

 0.05 5 4.26 1.06 

 0.2 5 2.49 0.51 

 1 5 1.03 0.21 

 Estimated Values of Interest 

 Model Dose Est Mean Est Std Scaled Residual 

 ------- ------ ---------- --------- ---------------- 

 2 0 4.256 1.094 1.07 

 0.05 3.96 1.003 0.6688 
Page 185 of 438 

 0.2 3.189 0.7731 -2.021 

 1 1.005 0.1927 0.2945 

 3 0 4.256 1.094 1.07 

 0.05 3.96 1.003 0.6688 

 0.2 3.189 0.7731 -2.021 

 1 1.005 0.1927 0.2945 

 4 0 4.927 1.017 -0.3239 

 0.05 4.097 0.8323 0.4391 

 0.2 2.517 0.4908 -0.1231 

 1 1.029 0.1862 0.007129 

 5 0 4.749 0.9596 0.07163 

 0.05 4.299 0.8622 -0.1019 

 0.2 2.481 0.4775 0.04026 

 1 1.031 0.1857 -0.01006 

 Other models for which likelihoods are calculated: 

 Model A1: Yij = Mu(i) + e(ij) 

 Var{e(ij)} = Sigma^2 

 Model A2: Yij = Mu(i) + e(ij) 

 Var{e(ij)} = Sigma(i)^2 

 Model A3: Yij = Mu(i) + e(ij) 

 Var{e(ij)} = exp(lalpha + log(mean(i)) * rho) 

 Model R: Yij = Mu + e(i) 

 Var{e(ij)} = Sigma^2 

 Likelihoods of Interest 

 Model Log(likelihood) DF AIC 

 ------- ----------------- ---- ------------ 

 A1 -2.693171 5 15.38634 

 A2 3.160304 8 9.679392 

 A3 3.062163 6 5.875674 

 R -19.88139 2 43.76277 

 2 -0.9442328 4 9.888466 

 3 -0.9442328 4 9.888466 

 4 2.797887 5 4.404226 

 5 3.062163 6 5.875674 
Page 186 of 438 

 Additive constant for all log-likelihoods = -18.38. This constant added to the 

 above values gives the log-likelihood including the term that does not 

 depend on the model parameters. 

 Explanation of Tests 

 Test 1: Does response and/or variances differ among Dose levels? (A2 vs. R) 

 Test 2: Are Variances Homogeneous? (A2 vs. A1) 

 Test 3: Are variances adequately modeled? (A2 vs. A3) 

 Test 4: Does Model 2 fit the data? (A3 vs. 2) 

 Test 5a: Does Model 3 fit the data? (A3 vs 3) 

 Test 5b: Is Model 3 better than Model 2? (3 vs. 2) 

 Test 6a: Does Model 4 fit the data? (A3 vs 4) 

 Test 6b: Is Model 4 better than Model 2? (4 vs. 2) 

 Test 7a: Does Model 5 fit the data? (A3 vs 5) 

 Test 7b: Is Model 5 better than Model 3? (5 vs. 3) 

 Test 7c: Is Model 5 better than Model 4? (5 vs. 4) 

 Tests of Interest 

 Test -2*log(Likelihood Ratio) D. F. p-value 

 -------- ------------------------ ------ -------------- 

 Test 1 46.08 6 < 0.0001 

 Test 2 11.71 3 0.008458 

 Test 3 0.1963 2 0.9065 

 Test 4 8.013 2 0.0182 

 Test 5a 8.013 2 0.0182 

 Test 5b -1.329e-011 0 N/A 

 Test 6a 0.5286 1 0.4672 

 Test 6b 7.484 1 0.006224 

 Test 7a -2.961e-012 0 N/A 

 Test 7b 8.013 2 0.0182 

 Test 7c 0.5286 1 0.4672 

 The p-value for Test 1 is less than .05. There appears to be a 

 difference between response and/or variances among the dose 

 levels, it seems appropriate to model the data. 

 The p-value for Test 2 is less than .1. A non-homogeneous 

 variance model appears to be appropriate. 
Page 187 of 438 

 The p-value for Test 3 is greater than .1. The modeled 

 variance appears to be appropriate here. 

 The p-value for Test 4 is less than .1. Model 2 may not adequately 

 describe the data; you may want to consider another model. 

 The p-value for Test 5a is less than .1. Model 3 may not adequately 

 describe the data; you may want to consider another model. 

 Degrees of freedom for Test 5b are less than or equal to 0. 

 The Chi-Square test for fit is not valid. 

 The p-value for Test 6a is greater than .1. Model 4 seems 

 to adequately describe the data. 

 The p-value for Test 6b is less than .05. Model 4 appears 

 to fit the data better than Model 2. 

 Degrees of freedom for Test 7a are less than or equal to 0. 

 The Chi-Square test for fit is not valid. 

 The p-value for Test 7b is less than .05. Model 5 appears 

 to fit the data better than Model 3. 

 The p-value for Test 7c is greater than .05. Model 5 does 

 not seem to fit the data better than Model 4. 

 Benchmark Dose Computations: 

 Specified Effect = 0.100000 

 Risk Type = Relative deviation 

 Confidence Level = 0.950000 

 BMD and BMDL by Model 

 Model BMD BMDL 

 ------- ------------ ---------- 

 2 0.0729736 0.0618696 

 3 0.0729736 0.0618696 

 4 0.0282081 0.0207607 

 5 0.0520527 0.0213844 
Page 188 of 438 

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dose

Exponential Model 2 with 0.95 Confidence Level

15:01 09/25 2012

BMDL BMD

 

Exponential

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Exponential Model 3 with 0.95 Confidence Level

15:01 09/25 2012

BMDL BMD

 

Exponential
Page 189 of 438 

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dose

Exponential Model 4 with 0.95 Confidence Level

15:01 09/25 2012

BMDL BMD

 

Exponential

 1

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Mean Response

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Exponential Model 5 with 0.95 Confidence Level

15:01 09/25 2012

BMDL BMD

 

Exponential
Page 190 of 438 

MRID 46153201 - Developmental Neurotoxicity in Rats – Maternal Brain, Lactation Day 

22 

 ==================================================================== 

 Exponential Model. (Version: 1.7; Date: 12/10/2009) 

 Input Data File: F:/dicrotophos/exp_Dicrotophos DNT Mat Brain LD22 

NHV_Setting.(d) 

 Gnuplot Plotting File: 

 Tue Sep 25 15:12:12 2012 

 ==================================================================== 

 BMDS Model Run 

~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ 

 

 The form of the response function by Model: 

 Model 2: Y[dose] = a * exp{sign * b * dose} 

 Model 3: Y[dose] = a * exp{sign * (b * dose)^d} 

 Model 4: Y[dose] = a * [c-(c-1) * exp{-b * dose}] 

 Model 5: Y[dose] = a * [c-(c-1) * exp{-(b * dose)^d}] 

 Note: Y[dose] is the median response for exposure = dose; 

 sign = +1 for increasing trend in data; 

 sign = -1 for decreasing trend. 

 Model 2 is nested within Models 3 and 4. 

 Model 3 is nested within Model 5. 

 Model 4 is nested within Model 5. 

 Dependent variable = Mean 

 Independent variable = Dose 

 Data are assumed to be distributed: normally 

 Variance Model: exp(lnalpha +rho *ln(Y[dose])) 

 The variance is to be modeled as Var(i) = exp(lalpha + log(mean(i)) * rho) 

 Total number of dose groups = 4 

 Total number of records with missing values = 0 

 Maximum number of iterations = 250 

 Relative Function Convergence has been set to: 1e-008 

 Parameter Convergence has been set to: 1e-008 

 MLE solution provided: Exact 

 Initial Parameter Values 
Page 191 of 438 

 Variable Model 2 Model 3 Model 4 Model 5 

 -------- ------- ------- ------- ------- 

 lnalpha -2.78329 -2.78329 -2.78329 -2.78329 

 rho 1.82372 1.82372 1.82372 1.82372 

 a 1.5864 1.5864 5.565 5.565 

 b 1.49419 1.49419 4.69765 4.69765 

 c -- -- 0.164292 0.164292 

 d -- 1 -- 1 

 Parameter Estimates by Model 

 Variable Model 2 Model 3 Model 4 Model 5 

 -------- ------- ------- ------- ------- 

 lnalpha -2.97927 -3.04436 -3.02506 -3.02506 

 rho 2.46029 2.89388 1.94883 1.94883 

 a 4.08839 3.70667 4.9458 4.9458 

 b 1.47961 1.03448 5.78575 5.78575 

 c -- -- 0.191911 0.191911 

 d -- 8.87425 -- 1 

 Table of Stats From Input Data 

 Dose N Obs Mean Obs Std Dev 

 ----- --- ---------- ------------- 

 0 5 5.3 1.07 

 0.05 5 3.55 0.82 

 0.2 5 2.27 0.56 

 1 5 0.96 0.23 

 Estimated Values of Interest 

 Model Dose Est Mean Est Std Scaled Residual 

 ------- ------ ---------- --------- ---------------- 

 2 0 4.088 1.275 2.126 

 0.05 3.797 1.164 -0.4743 

 0.2 3.041 0.8856 -1.947 

 1 0.931 0.2065 0.3137 

 3 0 3.707 1.453 2.452 

 0.05 3.707 1.453 -0.2411 

 0.2 3.707 1.453 -2.211 

 1 0.96 0.2057 5.165e-007 

 4 0 4.946 1.046 0.7571 
Page 192 of 438 

 0.05 3.942 0.8386 -1.045 

 0.2 2.206 0.4763 0.3022 

 1 0.9614 0.2121 -0.01507 

 5 0 4.946 1.046 0.7571 

 0.05 3.942 0.8386 -1.045 

 0.2 2.206 0.4763 0.3022 

 1 0.9614 0.2121 -0.01507 

 Other models for which likelihoods are calculated: 

 Model A1: Yij = Mu(i) + e(ij) 

 Var{e(ij)} = Sigma^2 

 Model A2: Yij = Mu(i) + e(ij) 

 Var{e(ij)} = Sigma(i)^2 

 Model A3: Yij = Mu(i) + e(ij) 

 Var{e(ij)} = exp(lalpha + log(mean(i)) * rho) 

 Model R: Yij = Mu + e(i) 

 Var{e(ij)} = Sigma^2 

 Likelihoods of Interest 

 Model Log(likelihood) DF AIC 

 ------- ----------------- ---- ------------ 

 A1 -1.716286 5 13.43257 

 A2 3.132869 8 9.734261 

 A3 3.081416 6 5.837168 

 R -21.01312 2 46.02624 

 2 -3.475981 4 14.95196 

 3 -7.696264 5 25.39253 

 4 2.117408 5 5.765184 

 5 2.117408 5 5.765184 

 Additive constant for all log-likelihoods = -18.38. This constant added to the 

 above values gives the log-likelihood including the term that does not 

 depend on the model parameters. 

 Explanation of Tests 
Page 193 of 438 

 Test 1: Does response and/or variances differ among Dose levels? (A2 vs. R) 

 Test 2: Are Variances Homogeneous? (A2 vs. A1) 

 Test 3: Are variances adequately modeled? (A2 vs. A3) 

 Test 4: Does Model 2 fit the data? (A3 vs. 2) 

 Test 5a: Does Model 3 fit the data? (A3 vs 3) 

 Test 5b: Is Model 3 better than Model 2? (3 vs. 2) 

 Test 6a: Does Model 4 fit the data? (A3 vs 4) 

 Test 6b: Is Model 4 better than Model 2? (4 vs. 2) 

 Test 7a: Does Model 5 fit the data? (A3 vs 5) 

 Test 7b: Is Model 5 better than Model 3? (5 vs. 3) 

 Test 7c: Is Model 5 better than Model 4? (5 vs. 4) 

 Tests of Interest 

 Test -2*log(Likelihood Ratio) D. F. p-value 

 -------- ------------------------ ------ -------------- 

 Test 1 48.29 6 < 0.0001 

 Test 2 9.698 3 0.02131 

 Test 3 0.1029 2 0.9498 

 Test 4 13.11 2 0.00142 

 Test 5a 21.56 1 < 0.0001 

 Test 5b -8.441 1 N/A 

 Test 6a 1.928 1 0.165 

 Test 6b 11.19 1 0.0008238 

 Test 7a 1.928 1 0.165 

 Test 7b 19.63 0 N/A 

 Test 7c 0 0 N/A 

 The p-value for Test 1 is less than .05. There appears to be a 

 difference between response and/or variances among the dose 

 levels, it seems appropriate to model the data. 

 The p-value for Test 2 is less than .1. A non-homogeneous 

 variance model appears to be appropriate. 

 The p-value for Test 3 is greater than .1. The modeled 

 variance appears to be appropriate here. 

 The p-value for Test 4 is less than .1. Model 2 may not adequately 

 describe the data; you may want to consider another model. 
Page 194 of 438 

 The p-value for Test 5a is less than .1. Model 3 may not adequately 

 describe the data; you may want to consider another model. 

 The p-value for Test 5b is less than .05. Model 3 appears 

 to fit the data better than Model 2. 

 The p-value for Test 6a is greater than .1. Model 4 seems 

 to adequately describe the data. 

 The p-value for Test 6b is less than .05. Model 4 appears 

 to fit the data better than Model 2. 

 The p-value for Test 7a is greater than .1. Model 5 seems 

 to adequately describe the data. 

 Degrees of freedom for Test 7b are less than or equal to 0. 

 The Chi-Square test for fit is not valid. 

 Degrees of freedom for Test 7c are less than or equal to 0. 

 The Chi-Square test for fit is not valid. 

 Benchmark Dose Computations: 

 Specified Effect = 0.100000 

 Risk Type = Relative deviation 

 Confidence Level = 0.950000 

 BMD and BMDL by Model 

 Model BMD BMDL 

 ------- ------------ ---------- 

 2 0.0712083 0.0586767 

 3 0.750151 0.0149042 

 4 0.0228324 0.0172106 

 5 0.0228324 0.0172106 
Page 195 of 438 

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Mean Response

dose

Exponential Model 2 with 0.95 Confidence Level

15:12 09/25 2012

BMDL BMD

 

Exponential

 1

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Mean Response

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Exponential Model 3 with 0.95 Confidence Level

15:12 09/25 2012

BMDL BMD

 

Exponential
Page 196 of 438 

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Mean Response

dose

Exponential Model 4 with 0.95 Confidence Level

15:12 09/25 2012

BMDL BMD

 

Exponential

 1

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 7

 0 0.2 0.4 0.6 0.8 1

Mean Response

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Exponential Model 5 with 0.95 Confidence Level

15:12 09/25 2012

BMDL BMD

 

Exponential
Page 197 of 438 

MRID 46153201 - Developmental Neurotoxicity in Rats – Maternal RBC, Gestation Day 22 

==================================================================== 

 Exponential Model. (Version: 1.7; Date: 12/10/2009) 

 Input Data File: F:/dicrotophos/exp_Dicrotophos DNT Mat RBC GD22 

NHV_Setting.(d) 

 Gnuplot Plotting File: 

 Tue Sep 25 15:24:05 2012 

 ==================================================================== 

 BMDS Model Run 

~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ 

 

 The form of the response function by Model: 

 Model 2: Y[dose] = a * exp{sign * b * dose} 

 Model 3: Y[dose] = a * exp{sign * (b * dose)^d} 

 Model 4: Y[dose] = a * [c-(c-1) * exp{-b * dose}] 

 Model 5: Y[dose] = a * [c-(c-1) * exp{-(b * dose)^d}] 

 Note: Y[dose] is the median response for exposure = dose; 

 sign = +1 for increasing trend in data; 

 sign = -1 for decreasing trend. 

 Model 2 is nested within Models 3 and 4. 

 Model 3 is nested within Model 5. 

 Model 4 is nested within Model 5. 

 Dependent variable = Mean 

 Independent variable = Dose 

 Data are assumed to be distributed: normally 

 Variance Model: exp(lnalpha +rho *ln(Y[dose])) 

 The variance is to be modeled as Var(i) = exp(lalpha + log(mean(i)) * rho) 

 Total number of dose groups = 4 

 Total number of records with missing values = 0 

 Maximum number of iterations = 250 

 Relative Function Convergence has been set to: 1e-008 

 Parameter Convergence has been set to: 1e-008 

 MLE solution provided: Exact 

 Initial Parameter Values 

 Variable Model 2 Model 3 Model 4 Model 5 
Page 198 of 438 

 -------- ------- ------- ------- ------- 

 lnalpha 16.9485 16.9485 16.9485 16.9485 

 rho -0.923565 -0.923565 -0.923565 -0.923565 

 a 1555.6 69.289 2722.65 2722.65 

 b 0.62262 -2.0097 3.33194 3.33194 

 c -- -- 0.448443 0.448443 

 d -- 2 -- 1 

 Parameter Estimates by Model 

 Variable Model 2 Model 3 Model 4 Model 5 

 -------- ------- ------- ------- ------- 

 lnalpha 24.0537 16.9485 12.4658 13.3588 

 rho -1.81995 -0.923565 -0.307124 -0.436023 

 a 2591.98 69.289 2626.47 2591.53 

 b 2.15278 -2.0097 5.89745 6.08096 

 c -- -- 0.481125 0.49383 

 d -- 2 -- 1.31261 

 Table of Stats From Input Data 

 Dose N Obs Mean Obs Std Dev 

 ----- --- ---------- ------------- 

 0 5 2593 218 

 0.05 5 2342 79 

 0.2 5 1638 120 

 1 5 1282 226 

 Estimated Values of Interest 

 Model Dose Est Mean Est Std Scaled Residual 

 ------- ------ ---------- --------- ---------------- 

 2 0 2592 130.9 0.01741 

 0.05 2327 144.3 0.225 

 0.2 1685 193.7 -0.5447 

 1 301.1 928.2 2.363 

 3 0 0 1.176e+146 4.929e-143 

 0.05 0 1.176e+146 4.452e-143 

 0.2 0 1.176e+146 3.114e-143 

 1 0 1.176e+146 2.437e-143 

 4 0 2626 152 -0.4924 

 0.05 2278 155.3 0.9148 
Page 199 of 438 

 0.2 1683 162.8 -0.6133 

 1 1267 170 0.192 

 5 0 2592 143.4 0.02289 

 0.05 2344 146.6 -0.02341 

 0.2 1640 158.5 -0.0255 

 1 1280 167.3 0.02934 

 Other models for which likelihoods are calculated: 

 Model A1: Yij = Mu(i) + e(ij) 

 Var{e(ij)} = Sigma^2 

 Model A2: Yij = Mu(i) + e(ij) 

 Var{e(ij)} = Sigma(i)^2 

 Model A3: Yij = Mu(i) + e(ij) 

 Var{e(ij)} = exp(lalpha + log(mean(i)) * rho) 

 Model R: Yij = Mu + e(i) 

 Var{e(ij)} = Sigma^2 

 Likelihoods of Interest 

 Model Log(likelihood) DF AIC 

 ------- ----------------- ---- ------------ 

 A1 -110.7946 5 231.5893 

 A2 -107.5784 8 231.1568 

 A3 -110.6941 6 233.3881 

 R -136.1596 2 276.3192 

 2 -119.7293 4 247.4586 

 3 -215.0441 5 440.0882 

 4 -111.4873 5 232.9747 

 5 -110.6941 6 233.3881 

 Additive constant for all log-likelihoods = -18.38. This constant added to the 

 above values gives the log-likelihood including the term that does not 

 depend on the model parameters. 

 Explanation of Tests 

 Test 1: Does response and/or variances differ among Dose levels? (A2 vs. R) 
Page 200 of 438 

 Test 2: Are Variances Homogeneous? (A2 vs. A1) 

 Test 3: Are variances adequately modeled? (A2 vs. A3) 

 Test 4: Does Model 2 fit the data? (A3 vs. 2) 

 Test 5a: Does Model 3 fit the data? (A3 vs 3) 

 Test 5b: Is Model 3 better than Model 2? (3 vs. 2) 

 Test 6a: Does Model 4 fit the data? (A3 vs 4) 

 Test 6b: Is Model 4 better than Model 2? (4 vs. 2) 

 Test 7a: Does Model 5 fit the data? (A3 vs 5) 

 Test 7b: Is Model 5 better than Model 3? (5 vs. 3) 

 Test 7c: Is Model 5 better than Model 4? (5 vs. 4) 

 Tests of Interest 

 Test -2*log(Likelihood Ratio) D. F. p-value 

 -------- ------------------------ ------ -------------- 

 Test 1 57.16 6 < 0.0001 

 Test 2 6.432 3 0.09236 

 Test 3 6.231 2 0.04435 

 Test 4 18.07 2 0.0001191 

 Test 5a 208.7 1 < 0.0001 

 Test 5b -190.6 1 N/A 

 Test 6a 1.587 1 0.2078 

 Test 6b 16.48 1 < 0.0001 

 Test 7a -1.805e-007 0 N/A 

 Test 7b 208.7 1 < 0.0001 

 Test 7c 1.587 1 0.2078 

 The p-value for Test 1 is less than .05. There appears to be a 

 difference between response and/or variances among the dose 

 levels, it seems appropriate to model the data. 

 The p-value for Test 2 is less than .1. A non-homogeneous 

 variance model appears to be appropriate. 

 The p-value for Test 3 is less than .1. You may want to 

 consider a different variance model. 

 The p-value for Test 4 is less than .1. Model 2 may not adequately 

 describe the data; you may want to consider another model. 

 The p-value for Test 5a is less than .1. Model 3 may not adequately 
Page 201 of 438 

 describe the data; you may want to consider another model. 

 The p-value for Test 5b is less than .05. Model 3 appears 

 to fit the data better than Model 2. 

 The p-value for Test 6a is greater than .1. Model 4 seems 

 to adequately describe the data. 

 The p-value for Test 6b is less than .05. Model 4 appears 

 to fit the data better than Model 2. 

 Degrees of freedom for Test 7a are less than or equal to 0. 

 The Chi-Square test for fit is not valid. 

 The p-value for Test 7b is less than .05. Model 5 appears 

 to fit the data better than Model 3. 

 The p-value for Test 7c is greater than .05. Model 5 does 

 not seem to fit the data better than Model 4. 

 Benchmark Dose Computations: 

 Specified Effect = 0.100000 

 Risk Type = Relative deviation 

 Confidence Level = 0.950000 

 BMD and BMDL by Model 

 Model BMD BMDL 

 ------- ------------ ---------- 

 2 0.0489417 0.0396102 

 3 -0.161513 Bad completion 

 4 0.0363023 0.0276139 

 5 0.0519051 0.0305501 

 
Page 202 of 438 

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Exponential
Page 203 of 438 

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Exponential
Page 204 of 438 

MRID 46153201 - Developmental Neurotoxicity in Rats – Maternal RBC, Lactation Day 22

 ==================================================================== 

 Exponential Model. (Version: 1.7; Date: 12/10/2009) 

 Input Data File: F:/dicrotophos/exp_Dicrotophos DNT Mat RBC LD22_Setting.(d) 

 Gnuplot Plotting File: 

 Fri Aug 17 12:36:01 2012 

 ==================================================================== 

 BMDS Model Run 

~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ 

 

 The form of the response function by Model: 

 Model 2: Y[dose] = a * exp{sign * b * dose} 

 Model 3: Y[dose] = a * exp{sign * (b * dose)^d} 

 Model 4: Y[dose] = a * [c-(c-1) * exp{-b * dose}] 

 Model 5: Y[dose] = a * [c-(c-1) * exp{-(b * dose)^d}] 

 Note: Y[dose] is the median response for exposure = dose; 

 sign = +1 for increasing trend in data; 

 sign = -1 for decreasing trend. 

 Model 2 is nested within Models 3 and 4. 

 Model 3 is nested within Model 5. 

 Model 4 is nested within Model 5. 

 Dependent variable = Mean 

 Independent variable = Dose 

 Data are assumed to be distributed: normally 

 Variance Model: exp(lnalpha +rho *ln(Y[dose])) 

 rho is set to 0. 

 A constant variance model is fit. 

 Total number of dose groups = 4 

 Total number of records with missing values = 0 

 Maximum number of iterations = 250 

 Relative Function Convergence has been set to: 1e-008 

 Parameter Convergence has been set to: 1e-008 

 MLE solution provided: Exact 

 Initial Parameter Values 

 Variable Model 2 Model 3 Model 4 Model 5 
Page 205 of 438 

 -------- ------- ------- ------- ------- 

 lnalpha 9.87791 9.87791 9.87791 9.87791 

 rho(S) 0 0 0 0 

 a 1592.21 1592.21 2753.1 2753.1 

 b 0.592857 0.592857 3.28434 3.28434 

 c -- -- 0.456282 0.456282 

 d -- 1 -- 1 

 (S) = Specified 

 Parameter Estimates by Model 

 Variable Model 2 Model 3 Model 4 Model 5 

 -------- ------- ------- ------- ------- 

 lnalpha 11.1405 11.1405 9.88416 9.88416 

 rho 0 0 0 0 

 a 2363.74 2363.74 2612.64 2612.64 

 b 0.678249 0.678249 5.94524 5.94524 

 c -- -- 0.50467 0.50467 

 d -- 1 -- 1 

 Table of Stats From Input Data 

 Dose N Obs Mean Obs Std Dev 

 ----- --- ---------- ------------- 

 0 5 2622 185 

 0.05 5 2263 140 

 0.2 5 1723 195 

 1 5 1319 75 

 Estimated Values of Interest 

 Model Dose Est Mean Est Std Scaled Residual 

 ------- ------ ---------- --------- ---------------- 

 2 0 2364 262.5 2.2 

 0.05 2285 262.5 -0.1868 

 0.2 2064 262.5 -2.904 

 1 1200 262.5 1.017 

 3 0 2364 262.5 2.2 

 0.05 2285 262.5 -0.1868 

 0.2 2064 262.5 -2.904 

 1 1200 262.5 1.017 
Page 206 of 438 

 4 0 2613 140.1 0.1494 

 0.05 2280 140.1 -0.2691 

 0.2 1713 140.1 0.1661 

 1 1322 140.1 -0.04645 

 5 0 2613 140.1 0.1494 

 0.05 2280 140.1 -0.2691 

 0.2 1713 140.1 0.1661 

 1 1322 140.1 -0.04645 

 Other models for which likelihoods are calculated: 

 Model A1: Yij = Mu(i) + e(ij) 

 Var{e(ij)} = Sigma^2 

 Model A2: Yij = Mu(i) + e(ij) 

 Var{e(ij)} = Sigma(i)^2 

 Model A3: Yij = Mu(i) + e(ij) 

 Var{e(ij)} = exp(lalpha + log(mean(i)) * rho) 

 Model R: Yij = Mu + e(i) 

 Var{e(ij)} = Sigma^2 

 Likelihoods of Interest 

 Model Log(likelihood) DF AIC 

 ------- ----------------- ---- ------------ 

 A1 -108.7791 5 227.5583 

 A2 -106.531 8 229.062 

 A3 -108.7791 5 227.5583 

 R -134.9985 2 273.997 

 2 -121.4049 3 248.8099 

 3 -121.4049 3 248.8099 

 4 -108.8416 4 225.6832 

 5 -108.8416 4 225.6832 

 Additive constant for all log-likelihoods = -18.38. This constant added to the 

 above values gives the log-likelihood including the term that does not 

 depend on the model parameters. 

 Explanation of Tests 
Page 207 of 438 

 Test 1: Does response and/or variances differ among Dose levels? (A2 vs. R) 

 Test 2: Are Variances Homogeneous? (A2 vs. A1) 

 Test 3: Are variances adequately modeled? (A2 vs. A3) 

 Test 4: Does Model 2 fit the data? (A3 vs. 2) 

 Test 5a: Does Model 3 fit the data? (A3 vs 3) 

 Test 5b: Is Model 3 better than Model 2? (3 vs. 2) 

 Test 6a: Does Model 4 fit the data? (A3 vs 4) 

 Test 6b: Is Model 4 better than Model 2? (4 vs. 2) 

 Test 7a: Does Model 5 fit the data? (A3 vs 5) 

 Test 7b: Is Model 5 better than Model 3? (5 vs. 3) 

 Test 7c: Is Model 5 better than Model 4? (5 vs. 4) 

 Tests of Interest 

 Test -2*log(Likelihood Ratio) D. F. p-value 

 -------- ------------------------ ------ -------------- 

 Test 1 56.94 6 < 0.0001 

 Test 2 4.496 3 0.2126 

 Test 3 4.496 3 0.2126 

 Test 4 25.25 2 < 0.0001 

 Test 5a 25.25 2 < 0.0001 

 Test 5b -3.979e-013 0 N/A 

 Test 6a 0.1249 1 0.7238 

 Test 6b 25.13 1 < 0.0001 

 Test 7a 0.1249 1 0.7238 

 Test 7b 25.13 1 < 0.0001 

 Test 7c -1.137e-013 0 N/A 

 The p-value for Test 1 is less than .05. There appears to be a 

 difference between response and/or variances among the dose 

 levels, it seems appropriate to model the data. 

 The p-value for Test 2 is greater than .1. A homogeneous 

 variance model appears to be appropriate here. 

 The p-value for Test 3 is greater than .1. The modeled 

 variance appears to be appropriate here. 

 The p-value for Test 4 is less than .1. Model 2 may not adequately 

 describe the data; you may want to consider another model. 
Page 208 of 438 

 The p-value for Test 5a is less than .1. Model 3 may not adequately 

 describe the data; you may want to consider another model. 

 Degrees of freedom for Test 5b are less than or equal to 0. 

 The Chi-Square test for fit is not valid. 

 The p-value for Test 6a is greater than .1. Model 4 seems 

 to adequately describe the data. 

 The p-value for Test 6b is less than .05. Model 4 appears 

 to fit the data better than Model 2. 

 The p-value for Test 7a is greater than .1. Model 5 seems 

 to adequately describe the data. 

 The p-value for Test 7b is less than .05. Model 5 appears 

 to fit the data better than Model 3. 

 Degrees of freedom for Test 7c are less than or equal to 0. 

 The Chi-Square test for fit is not valid. 

 Benchmark Dose Computations: 

 Specified Effect = 0.100000 

 Risk Type = Relative deviation 

 Confidence Level = 0.950000 

 BMD and BMDL by Model 

 Model BMD BMDL 

 ------- ------------ ---------- 

 2 0.155342 0.119381 

 3 0.155342 0.119381 

 4 0.0379301 0.0292592 

 5 0.0379301 0.0292592 

 
Page 209 of 438 

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BMDL BMD

 

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Exponential
Page 210 of 438 

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BMDL BMD

 

Exponential
Page 211 of 438 

MRID 46153201 - Developmental Neurotoxicity in Rats – Male Fetus Gestation Day 22, 

Brain 

 ==================================================================== 

 Exponential Model. (Version: 1.7; Date: 12/10/2009) 

 Input Data File: F:/dicrotophos/exp_Dicrotophos DNT Fet Brain M GD22 

NHV_Setting.(d) 

 Gnuplot Plotting File: 

 Tue Sep 25 15:51:15 2012 

 ==================================================================== 

 BMDS Model Run 

~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ 

 

 The form of the response function by Model: 

 Model 2: Y[dose] = a * exp{sign * b * dose} 

 Model 3: Y[dose] = a * exp{sign * (b * dose)^d} 

 Model 4: Y[dose] = a * [c-(c-1) * exp{-b * dose}] 

 Model 5: Y[dose] = a * [c-(c-1) * exp{-(b * dose)^d}] 

 Note: Y[dose] is the median response for exposure = dose; 

 sign = +1 for increasing trend in data; 

 sign = -1 for decreasing trend. 

 Model 2 is nested within Models 3 and 4. 

 Model 3 is nested within Model 5. 

 Model 4 is nested within Model 5. 

 Dependent variable = Mean 

 Independent variable = Dose 

 Data are assumed to be distributed: normally 

 Variance Model: exp(lnalpha +rho *ln(Y[dose])) 

 The variance is to be modeled as Var(i) = exp(lalpha + log(mean(i)) * rho) 

 Total number of dose groups = 4 

 Total number of records with missing values = 0 

 Maximum number of iterations = 250 

 Relative Function Convergence has been set to: 1e-008 

 Parameter Convergence has been set to: 1e-008 

 MLE solution provided: Exact 

 Initial Parameter Values 
Page 212 of 438 

 Variable Model 2 Model 3 Model 4 Model 5 

 -------- ------- ------- ------- ------- 

 lnalpha -3.90785 -3.90785 -3.90785 -3.90785 

 rho 3.41373 3.41373 3.41373 3.41373 

 a 1.04679 1.06393 1.8375 1.8375 

 b 0.750205 0.602732 3.44021 3.44021 

 c -- -- 0.399093 0.399093 

 d -- 2 -- 1 

 Parameter Estimates by Model 

 Variable Model 2 Model 3 Model 4 Model 5 

 -------- ------- ------- ------- ------- 

 lnalpha -4.125 -4.08459 -4.05885 -4.05885 

 rho 4.25023 4.8 3.54227 3.54227 

 a 1.56895 1.49333 1.6798 1.6798 

 b 0.71878 0.965201 3.02726 3.02726 

 c -- -- 0.428293 0.428293 

 d -- 11.6299 -- 1 

 Table of Stats From Input Data 

 Dose N Obs Mean Obs Std Dev 

 ----- --- ---------- ------------- 

 0 5 1.75 0.34 

 0.05 5 1.51 0.25 

 0.2 5 1.22 0.28 

 1 4 0.77 0.08 

 Estimated Values of Interest 

 Model Dose Est Mean Est Std Scaled Residual 

 ------- ------ ---------- --------- ---------------- 

 2 0 1.569 0.3311 1.223 

 0.05 1.514 0.3068 -0.02595 

 0.2 1.359 0.2439 -1.273 

 1 0.7646 0.07187 0.1497 

 3 0 1.493 0.3396 1.69 

 0.05 1.493 0.3396 0.1097 

 0.2 1.493 0.3396 -1.8 

 1 0.77 0.06928 3.084e-008 

 4 0 1.68 0.3293 0.4767 
Page 213 of 438 

 0.05 1.545 0.2839 -0.2749 

 0.2 1.244 0.1933 -0.2733 

 1 0.766 0.08195 0.0983 

 5 0 1.68 0.3293 0.4767 

 0.05 1.545 0.2839 -0.2749 

 0.2 1.244 0.1933 -0.2733 

 1 0.766 0.08195 0.0983 

 Other models for which likelihoods are calculated: 

 Model A1: Yij = Mu(i) + e(ij) 

 Var{e(ij)} = Sigma^2 

 Model A2: Yij = Mu(i) + e(ij) 

 Var{e(ij)} = Sigma(i)^2 

 Model A3: Yij = Mu(i) + e(ij) 

 Var{e(ij)} = exp(lalpha + log(mean(i)) * rho) 

 Model R: Yij = Mu + e(i) 

 Var{e(ij)} = Sigma^2 

 Likelihoods of Interest 

 Model Log(likelihood) DF AIC 

 ------- ----------------- ---- ------------ 

 A1 18.05219 5 -26.10438 

 A2 21.5422 8 -27.08441 

 A3 20.77383 6 -29.54765 

 R 6.841334 2 -9.682669 

 2 19.52083 4 -31.04166 

 3 17.3763 5 -24.7526 

 4 20.57197 5 -31.14395 

 5 20.57197 5 -31.14395 

 Additive constant for all log-likelihoods = -17.46. This constant added to the 

 above values gives the log-likelihood including the term that does not 

 depend on the model parameters. 

 Explanation of Tests 
Page 214 of 438 

 Test 1: Does response and/or variances differ among Dose levels? (A2 vs. R) 

 Test 2: Are Variances Homogeneous? (A2 vs. A1) 

 Test 3: Are variances adequately modeled? (A2 vs. A3) 

 Test 4: Does Model 2 fit the data? (A3 vs. 2) 

 Test 5a: Does Model 3 fit the data? (A3 vs 3) 

 Test 5b: Is Model 3 better than Model 2? (3 vs. 2) 

 Test 6a: Does Model 4 fit the data? (A3 vs 4) 

 Test 6b: Is Model 4 better than Model 2? (4 vs. 2) 

 Test 7a: Does Model 5 fit the data? (A3 vs 5) 

 Test 7b: Is Model 5 better than Model 3? (5 vs. 3) 

 Test 7c: Is Model 5 better than Model 4? (5 vs. 4) 

 Tests of Interest 

 Test -2*log(Likelihood Ratio) D. F. p-value 

 -------- ------------------------ ------ -------------- 

 Test 1 29.4 6 < 0.0001 

 Test 2 6.98 3 0.07254 

 Test 3 1.537 2 0.4638 

 Test 4 2.506 2 0.2856 

 Test 5a 6.795 1 0.009141 

 Test 5b -4.289 1 N/A 

 Test 6a 0.4037 1 0.5252 

 Test 6b 2.102 1 0.1471 

 Test 7a 0.4037 1 0.5252 

 Test 7b 6.391 0 N/A 

 Test 7c 0 0 N/A 

 The p-value for Test 1 is less than .05. There appears to be a 

 difference between response and/or variances among the dose 

 levels, it seems appropriate to model the data. 

 The p-value for Test 2 is less than .1. A non-homogeneous 

 variance model appears to be appropriate. 

 The p-value for Test 3 is greater than .1. The modeled 

 variance appears to be appropriate here. 

 The p-value for Test 4 is greater than .1. Model 2 seems 

 to adequately describe the data. 
Page 215 of 438 

 The p-value for Test 5a is less than .1. Model 3 may not adequately 

 describe the data; you may want to consider another model. 

 The p-value for Test 5b is less than .05. Model 3 appears 

 to fit the data better than Model 2. 

 The p-value for Test 6a is greater than .1. Model 4 seems 

 to adequately describe the data. 

 The p-value for Test 6b is greater than .05. Model 4 does 

 not seem to fit the data better than Model 2. 

 The p-value for Test 7a is greater than .1. Model 5 seems 

 to adequately describe the data. 

 Degrees of freedom for Test 7b are less than or equal to 0. 

 The Chi-Square test for fit is not valid. 

 Degrees of freedom for Test 7c are less than or equal to 0. 

 The Chi-Square test for fit is not valid. 

 Benchmark Dose Computations: 

 Specified Effect = 0.100000 

 Risk Type = Relative deviation 

 Confidence Level = 0.950000 

 BMD and BMDL by Model 

 Model BMD BMDL 

 ------- ------------ ---------- 

 2 0.146582 0.122038 

 3 0.853782 0.0997103 

 4 0.0635125 0.0361406 

 5 0.0635125 0.0361406 

 
Page 216 of 438 

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dose

Exponential Model 2 with 0.95 Confidence Level

15:51 09/25 2012

BMDL BMD

 

Exponential

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Exponential Model 3 with 0.95 Confidence Level

15:51 09/25 2012

BMDL BMD

 

Exponential
Page 217 of 438 

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Exponential Model 4 with 0.95 Confidence Level

15:51 09/25 2012

BMDL BMD

 

Exponential

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Exponential Model 5 with 0.95 Confidence Level

15:51 09/25 2012

BMDL BMD

 

Exponential
Page 218 of 438 

MRID 46153201 - Developmental Neurotoxicity in Rats – Female Fetus Gestation Day 22, 

Brain

 ==================================================================== 

 Exponential Model. (Version: 1.7; Date: 12/10/2009) 

 Input Data File: F:/dicrotophos/exp_Dicrotophos DNT Fet Brain F GD22 

NHV_Setting.(d) 

 Gnuplot Plotting File: 

 Tue Sep 25 16:17:06 2012 

 ==================================================================== 

 BMDS Model Run 

~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ 

 

 The form of the response function by Model: 

 Model 2: Y[dose] = a * exp{sign * b * dose} 

 Model 3: Y[dose] = a * exp{sign * (b * dose)^d} 

 Model 4: Y[dose] = a * [c-(c-1) * exp{-b * dose}] 

 Model 5: Y[dose] = a * [c-(c-1) * exp{-(b * dose)^d}] 

 Note: Y[dose] is the median response for exposure = dose; 

 sign = +1 for increasing trend in data; 

 sign = -1 for decreasing trend. 

 Model 2 is nested within Models 3 and 4. 

 Model 3 is nested within Model 5. 

 Model 4 is nested within Model 5. 

 Dependent variable = Mean 

 Independent variable = Dose 

 Data are assumed to be distributed: normally 

 Variance Model: exp(lnalpha +rho *ln(Y[dose])) 

 The variance is to be modeled as Var(i) = exp(lalpha + log(mean(i)) * rho) 

 Total number of dose groups = 4 

 Total number of records with missing values = 0 

 Maximum number of iterations = 250 

 Relative Function Convergence has been set to: 1e-008 

 Parameter Convergence has been set to: 1e-008 

 MLE solution provided: Exact 

 Initial Parameter Values 
Page 219 of 438 

 Variable Model 2 Model 3 Model 4 Model 5 

 -------- ------- ------- ------- ------- 

 lnalpha -5.84249 -5.84249 -5.84249 -5.84249 

 rho 5.75815 5.75815 5.75815 5.75815 

 a 0.983047 1.02456 1.6485 1.6485 

 b 0.723412 0.609064 3.3292 3.3292 

 c -- -- 0.415963 0.415963 

 d -- 2 -- 1 

 Parameter Estimates by Model 

 Variable Model 2 Model 3 Model 4 Model 5 

 -------- ------- ------- ------- ------- 

 lnalpha -5.97293 -5.97293 -6.02656 -6.02656 

 rho 6.58288 6.58288 6.15482 6.15482 

 a 1.45092 1.45092 1.50478 1.50478 

 b 0.701409 0.701409 1.87726 1.87726 

 c -- -- 0.384227 0.384227 

 d -- 1 -- 1 

 Table of Stats From Input Data 

 Dose N Obs Mean Obs Std Dev 

 ----- --- ---------- ------------- 

 0 5 1.57 0.18 

 0.05 5 1.36 0.13 

 0.2 5 1.22 0.11 

 1 4 0.72 0.02 

 Estimated Values of Interest 

 Model Dose Est Mean Est Std Scaled Residual 

 ------- ------ ---------- --------- ---------------- 

 2 0 1.451 0.1718 1.55 

 0.05 1.401 0.1531 -0.5977 

 0.2 1.261 0.1083 -0.847 

 1 0.7195 0.01708 0.05961 

 3 0 1.451 0.1718 1.55 

 0.05 1.401 0.1531 -0.5977 

 0.2 1.261 0.1083 -0.847 

 1 0.7195 0.01708 0.05962 

 4 0 1.505 0.1728 0.8441 
Page 220 of 438 

 0.05 1.422 0.1451 -0.9518 

 0.2 1.215 0.0894 0.1318 

 1 0.72 0.01787 0.005117 

 5 0 1.505 0.1728 0.8441 

 0.05 1.422 0.1451 -0.9518 

 0.2 1.215 0.0894 0.1318 

 1 0.72 0.01787 0.005117 

 Other models for which likelihoods are calculated: 

 Model A1: Yij = Mu(i) + e(ij) 

 Var{e(ij)} = Sigma^2 

 Model A2: Yij = Mu(i) + e(ij) 

 Var{e(ij)} = Sigma(i)^2 

 Model A3: Yij = Mu(i) + e(ij) 

 Var{e(ij)} = exp(lalpha + log(mean(i)) * rho) 

 Model R: Yij = Mu + e(i) 

 Var{e(ij)} = Sigma^2 

 Likelihoods of Interest 

 Model Log(likelihood) DF AIC 

 ------- ----------------- ---- ------------ 

 A1 31.76435 5 -53.5287 

 A2 38.2085 8 -60.41701 

 A3 38.05597 6 -64.11194 

 R 12.14416 2 -20.28833 

 2 36.08699 4 -64.17398 

 3 36.08699 4 -64.17398 

 4 37.1013 5 -64.20259 

 5 37.1013 5 -64.20259 

 Additive constant for all log-likelihoods = -17.46. This constant added to the 

 above values gives the log-likelihood including the term that does not 

 depend on the model parameters. 

 Explanation of Tests 
Page 221 of 438 

 Test 1: Does response and/or variances differ among Dose levels? (A2 vs. R) 

 Test 2: Are Variances Homogeneous? (A2 vs. A1) 

 Test 3: Are variances adequately modeled? (A2 vs. A3) 

 Test 4: Does Model 2 fit the data? (A3 vs. 2) 

 Test 5a: Does Model 3 fit the data? (A3 vs 3) 

 Test 5b: Is Model 3 better than Model 2? (3 vs. 2) 

 Test 6a: Does Model 4 fit the data? (A3 vs 4) 

 Test 6b: Is Model 4 better than Model 2? (4 vs. 2) 

 Test 7a: Does Model 5 fit the data? (A3 vs 5) 

 Test 7b: Is Model 5 better than Model 3? (5 vs. 3) 

 Test 7c: Is Model 5 better than Model 4? (5 vs. 4) 

 Tests of Interest 

 Test -2*log(Likelihood Ratio) D. F. p-value 

 -------- ------------------------ ------ -------------- 

 Test 1 52.13 6 < 0.0001 

 Test 2 12.89 3 0.004884 

 Test 3 0.3051 2 0.8585 

 Test 4 3.938 2 0.1396 

 Test 5a 3.938 2 0.1396 

 Test 5b -1.14e-011 0 N/A 

 Test 6a 1.909 1 0.167 

 Test 6b 2.029 1 0.1544 

 Test 7a 1.909 1 0.167 

 Test 7b 2.029 1 0.1544 

 Test 7c 0 0 N/A 

 The p-value for Test 1 is less than .05. There appears to be a 

 difference between response and/or variances among the dose 

 levels, it seems appropriate to model the data. 

 The p-value for Test 2 is less than .1. A non-homogeneous 

 variance model appears to be appropriate. 

 The p-value for Test 3 is greater than .1. The modeled 

 variance appears to be appropriate here. 

 The p-value for Test 4 is greater than .1. Model 2 seems 

 to adequately describe the data. 
Page 222 of 438 

 The p-value for Test 5a is greater than .1. Model 3 seems 

 to adequately describe the data. 

 Degrees of freedom for Test 5b are less than or equal to 0. 

 The Chi-Square test for fit is not valid. 

 The p-value for Test 6a is greater than .1. Model 4 seems 

 to adequately describe the data. 

 The p-value for Test 6b is greater than .05. Model 4 does 

 not seem to fit the data better than Model 2. 

 The p-value for Test 7a is greater than .1. Model 5 seems 

 to adequately describe the data. 

 The p-value for Test 7b is greater than .05. Model 5 does 

 not seem to fit the data better than Model 3. 

 Degrees of freedom for Test 7c are less than or equal to 0. 

 The Chi-Square test for fit is not valid. 

 Benchmark Dose Computations: 

 Specified Effect = 0.100000 

 Risk Type = Relative deviation 

 Confidence Level = 0.950000 

 BMD and BMDL by Model 

 Model BMD BMDL 

 ------- ------------ ---------- 

 2 0.150213 0.139435 

 3 0.150213 0.139435 

 4 0.0943991 0.0643789 

 5 0.0943991 0.0643789 

 
Page 223 of 438 

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Page 224 of 438 

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Page 225 of 438 

MRID 46153201 - Developmental Neurotoxicity in Rats – Male Fetus Gestation Day 22, 

RBC 

 ==================================================================== 

 Exponential Model. (Version: 1.7; Date: 12/10/2009) 

 Input Data File: F:/dicrotophos/exp_Dicrotophos DNT Fet RBC M GD22 

NHV_Setting.(d) 

 Gnuplot Plotting File: 

 Tue Sep 25 16:53:37 2012 

 ==================================================================== 

 BMDS Model Run 

~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ 

 

 The form of the response function by Model: 

 Model 2: Y[dose] = a * exp{sign * b * dose} 

 Model 3: Y[dose] = a * exp{sign * (b * dose)^d} 

 Model 4: Y[dose] = a * [c-(c-1) * exp{-b * dose}] 

 Model 5: Y[dose] = a * [c-(c-1) * exp{-(b * dose)^d}] 

 Note: Y[dose] is the median response for exposure = dose; 

 sign = +1 for increasing trend in data; 

 sign = -1 for decreasing trend. 

 Model 2 is nested within Models 3 and 4. 

 Model 3 is nested within Model 5. 

 Model 4 is nested within Model 5. 

 Dependent variable = Mean 

 Independent variable = Dose 

 Data are assumed to be distributed: normally 

 Variance Model: exp(lnalpha +rho *ln(Y[dose])) 

 The variance is to be modeled as Var(i) = exp(lalpha + log(mean(i)) * rho) 

 Total number of dose groups = 4 

 Total number of records with missing values = 0 

 Maximum number of iterations = 250 

 Relative Function Convergence has been set to: 1e-008 

 Parameter Convergence has been set to: 1e-008 

 MLE solution provided: Exact 

 Initial Parameter Values 
Page 226 of 438 

 Variable Model 2 Model 3 Model 4 Model 5 

 -------- ------- ------- ------- ------- 

 lnalpha 1.03839 1.03839 1.03839 1.03839 

 rho 1.22269 1.22269 1.22269 1.22269 

 a 1702.34 1702.34 2673.3 2673.3 

 b 0.63661 0.63661 3.15295 3.15295 

 c -- -- 0.467052 0.467052 

 d -- 1 -- 1 

 Parameter Estimates by Model 

 Variable Model 2 Model 3 Model 4 Model 5 

 -------- ------- ------- ------- ------- 

 lnalpha -11.0332 -20.4514 -7.02355 -7.68872 

 rho 2.86511 4.15186 2.29724 2.37931 

 a 2416.4 2297.33 2576.99 2524.11 

 b 0.625986 0.962514 3.30124 3.98937 

 c -- -- 0.488945 0.519213 

 d -- 15.1312 -- 1.67697 

 Table of Stats From Input Data 

 Dose N Obs Mean Obs Std Dev 

 ----- --- ---------- ------------- 

 0 5 2546 112 

 0.05 5 2423 351 

 0.2 5 1923 190 

 1 4 1311 124 

 Estimated Values of Interest 

 Model Dose Est Mean Est Std Scaled Residual 

 ------- ------ ---------- --------- ---------------- 

 2 0 2416 282.3 1.026 

 0.05 2342 269.9 0.6714 

 0.2 2132 236 -1.981 

 1 1292 115.2 0.3277 

 3 0 2297 344.1 1.616 

 0.05 2297 344.1 0.8166 

 0.2 2297 344.1 -2.432 

 1 1311 107.4 -8.301e-009 

 4 0 2577 247.1 -0.2804 
Page 227 of 438 

 0.05 2377 225.2 0.4607 

 0.2 1941 178.4 -0.2196 

 1 1309 113.5 0.04367 

 5 0 2524 238.6 0.2051 

 0.05 2445 229.8 -0.2189 

 0.2 1922 172.6 0.007321 

 1 1311 109.4 0.007334 

 Other models for which likelihoods are calculated: 

 Model A1: Yij = Mu(i) + e(ij) 

 Var{e(ij)} = Sigma^2 

 Model A2: Yij = Mu(i) + e(ij) 

 Var{e(ij)} = Sigma(i)^2 

 Model A3: Yij = Mu(i) + e(ij) 

 Var{e(ij)} = exp(lalpha + log(mean(i)) * rho) 

 Model R: Yij = Mu + e(i) 

 Var{e(ij)} = Sigma^2 

 Likelihoods of Interest 

 Model Log(likelihood) DF AIC 

 ------- ----------------- ---- ------------ 

 A1 -109.831 5 229.6619 

 A2 -105.6637 8 227.3275 

 A3 -108.5976 6 229.1951 

 R -127.8621 2 259.7243 

 2 -112.01 4 232.0201 

 3 -115.82 5 241.6401 

 4 -108.9808 5 227.9616 

 5 -108.5976 6 229.1951 

 Additive constant for all log-likelihoods = -17.46. This constant added to the 

 above values gives the log-likelihood including the term that does not 

 depend on the model parameters. 

 Explanation of Tests 
Page 228 of 438 

 Test 1: Does response and/or variances differ among Dose levels? (A2 vs. R) 

 Test 2: Are Variances Homogeneous? (A2 vs. A1) 

 Test 3: Are variances adequately modeled? (A2 vs. A3) 

 Test 4: Does Model 2 fit the data? (A3 vs. 2) 

 Test 5a: Does Model 3 fit the data? (A3 vs 3) 

 Test 5b: Is Model 3 better than Model 2? (3 vs. 2) 

 Test 6a: Does Model 4 fit the data? (A3 vs 4) 

 Test 6b: Is Model 4 better than Model 2? (4 vs. 2) 

 Test 7a: Does Model 5 fit the data? (A3 vs 5) 

 Test 7b: Is Model 5 better than Model 3? (5 vs. 3) 

 Test 7c: Is Model 5 better than Model 4? (5 vs. 4) 

 Tests of Interest 

 Test -2*log(Likelihood Ratio) D. F. p-value 

 -------- ------------------------ ------ -------------- 

 Test 1 44.4 6 < 0.0001 

 Test 2 8.334 3 0.03958 

 Test 3 5.868 2 0.05319 

 Test 4 6.825 2 0.03296 

 Test 5a 14.44 1 0.0001443 

 Test 5b -7.62 1 N/A 

 Test 6a 0.7665 1 0.3813 

 Test 6b 6.058 1 0.01384 

 Test 7a -2.842e-014 0 N/A 

 Test 7b 14.44 1 0.0001443 

 Test 7c 0.7665 1 0.3813 

 The p-value for Test 1 is less than .05. There appears to be a 

 difference between response and/or variances among the dose 

 levels, it seems appropriate to model the data. 

 The p-value for Test 2 is less than .1. A non-homogeneous 

 variance model appears to be appropriate. 

 The p-value for Test 3 is less than .1. You may want to 

 consider a different variance model. 

 The p-value for Test 4 is less than .1. Model 2 may not adequately 

 describe the data; you may want to consider another model. 
Page 229 of 438 

 The p-value for Test 5a is less than .1. Model 3 may not adequately 

 describe the data; you may want to consider another model. 

 The p-value for Test 5b is less than .05. Model 3 appears 

 to fit the data better than Model 2. 

 The p-value for Test 6a is greater than .1. Model 4 seems 

 to adequately describe the data. 

 The p-value for Test 6b is less than .05. Model 4 appears 

 to fit the data better than Model 2. 

 Degrees of freedom for Test 7a are less than or equal to 0. 

 The Chi-Square test for fit is not valid. 

 The p-value for Test 7b is less than .05. Model 5 appears 

 to fit the data better than Model 3. 

 The p-value for Test 7c is greater than .05. Model 5 does 

 not seem to fit the data better than Model 4. 

 Benchmark Dose Computations: 

 Specified Effect = 0.100000 

 Risk Type = Relative deviation 

 Confidence Level = 0.950000 

 BMD and BMDL by Model 

 Model BMD BMDL 

 ------- ------------ ---------- 

 2 0.168311 0.136826 

 3 0.895371 0.0659445 

 4 0.0659603 0.0451428 

 5 0.105207 0.0477208 
Page 230 of 438 

 

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Page 232 of 438 

MRID 46153201 - Developmental Neurotoxicity in Rats – Female Fetus Gestation Day 22, 

RBC

 ==================================================================== 

 Exponential Model. (Version: 1.7; Date: 12/10/2009) 

 Input Data File: F:/dicrotophos/exp_Dicrotophos DNT Fet RBC F GD22 

NHV_Setting.(d) 

 Gnuplot Plotting File: 

 Tue Sep 25 16:37:34 2012 

 ==================================================================== 

 BMDS Model Run 

~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ 

 

 The form of the response function by Model: 

 Model 2: Y[dose] = a * exp{sign * b * dose} 

 Model 3: Y[dose] = a * exp{sign * (b * dose)^d} 

 Model 4: Y[dose] = a * [c-(c-1) * exp{-b * dose}] 

 Model 5: Y[dose] = a * [c-(c-1) * exp{-(b * dose)^d}] 

 Note: Y[dose] is the median response for exposure = dose; 

 sign = +1 for increasing trend in data; 

 sign = -1 for decreasing trend. 

 Model 2 is nested within Models 3 and 4. 

 Model 3 is nested within Model 5. 

 Model 4 is nested within Model 5. 

 Dependent variable = Mean 

 Independent variable = Dose 

 Data are assumed to be distributed: normally 

 Variance Model: exp(lnalpha +rho *ln(Y[dose])) 

 The variance is to be modeled as Var(i) = exp(lalpha + log(mean(i)) * rho) 

 Total number of dose groups = 4 

 Total number of records with missing values = 0 

 Maximum number of iterations = 250 

 Relative Function Convergence has been set to: 1e-008 

 Parameter Convergence has been set to: 1e-008 

 MLE solution provided: Exact 

 Initial Parameter Values 
Page 233 of 438 

 Variable Model 2 Model 3 Model 4 Model 5 

 -------- ------- ------- ------- ------- 

 lnalpha 5.29309 5.29309 5.29309 5.29309 

 rho 0.641199 0.641199 0.641199 0.641199 

 a 1738.64 1738.64 2649.15 2649.15 

 b 0.535125 0.535125 3.04573 3.04573 

 c -- -- 0.508339 0.508339 

 d -- 1 -- 1 

 Parameter Estimates by Model 

 Variable Model 2 Model 3 Model 4 Model 5 

 -------- ------- ------- ------- ------- 

 lnalpha -20.0566 -20.0566 -14.7374 -14.6371 

 rho 4.10424 4.10424 3.33968 3.3265 

 a 2323.16 2323.16 2572.87 2568.95 

 b 0.509266 0.509266 5.09487 5.10144 

 c -- -- 0.547597 0.549011 

 d -- 1 -- 1.02477 

 Table of Stats From Input Data 

 Dose N Obs Mean Obs Std Dev 

 ----- --- ---------- ------------- 

 0 5 2523 455 

 0.05 5 2362 50 

 0.2 5 1825 207 

 1 4 1414 142 

 Estimated Values of Interest 

 Model Dose Est Mean Est Std Scaled Residual 

 ------- ------ ---------- --------- ---------------- 

 2 0 2323 356.7 1.253 

 0.05 2265 338.6 0.6422 

 0.2 2098 289.5 -2.11 

 1 1396 125.5 0.2858 

 3 0 2323 356.7 1.253 

 0.05 2265 338.6 0.6422 

 0.2 2098 289.5 -2.11 

 1 1396 125.5 0.2858 

 4 0 2573 312.4 -0.357 
Page 234 of 438 

 0.05 2311 261.1 0.4358 

 0.2 1829 176.7 -0.05122 

 1 1416 115.2 -0.03518 

 5 0 2569 311.1 -0.3303 

 0.05 2316 261.8 0.395 

 0.2 1828 176.6 -0.0357 

 1 1416 115.5 -0.03627 

 Other models for which likelihoods are calculated: 

 Model A1: Yij = Mu(i) + e(ij) 

 Var{e(ij)} = Sigma^2 

 Model A2: Yij = Mu(i) + e(ij) 

 Var{e(ij)} = Sigma(i)^2 

 Model A3: Yij = Mu(i) + e(ij) 

 Var{e(ij)} = exp(lalpha + log(mean(i)) * rho) 

 Model R: Yij = Mu + e(i) 

 Var{e(ij)} = Sigma^2 

 Likelihoods of Interest 

 Model Log(likelihood) DF AIC 

 ------- ----------------- ---- ------------ 

 A1 -113.4178 5 236.8357 

 A2 -103.8996 8 223.7991 

 A3 -110.9046 6 233.8092 

 R -127.1543 2 258.3086 

 2 -115.6768 4 239.3536 

 3 -115.6768 4 239.3536 

 4 -110.906 5 231.812 

 5 -110.9046 6 233.8092 

 Additive constant for all log-likelihoods = -17.46. This constant added to the 

 above values gives the log-likelihood including the term that does not 

 depend on the model parameters. 

 Explanation of Tests 
Page 235 of 438 

 Test 1: Does response and/or variances differ among Dose levels? (A2 vs. R) 

 Test 2: Are Variances Homogeneous? (A2 vs. A1) 

 Test 3: Are variances adequately modeled? (A2 vs. A3) 

 Test 4: Does Model 2 fit the data? (A3 vs. 2) 

 Test 5a: Does Model 3 fit the data? (A3 vs 3) 

 Test 5b: Is Model 3 better than Model 2? (3 vs. 2) 

 Test 6a: Does Model 4 fit the data? (A3 vs 4) 

 Test 6b: Is Model 4 better than Model 2? (4 vs. 2) 

 Test 7a: Does Model 5 fit the data? (A3 vs 5) 

 Test 7b: Is Model 5 better than Model 3? (5 vs. 3) 

 Test 7c: Is Model 5 better than Model 4? (5 vs. 4) 

 Tests of Interest 

 Test -2*log(Likelihood Ratio) D. F. p-value 

 -------- ------------------------ ------ -------------- 

 Test 1 46.51 6 < 0.0001 

 Test 2 19.04 3 0.0002687 

 Test 3 14.01 2 0.0009073 

 Test 4 9.544 2 0.008462 

 Test 5a 9.544 2 0.008462 

 Test 5b -7.105e-013 0 N/A 

 Test 6a 0.002752 1 0.9582 

 Test 6b 9.542 1 0.002009 

 Test 7a -1.137e-013 0 N/A 

 Test 7b 9.544 2 0.008462 

 Test 7c 0.002752 1 0.9582 

 The p-value for Test 1 is less than .05. There appears to be a 

 difference between response and/or variances among the dose 

 levels, it seems appropriate to model the data. 

 The p-value for Test 2 is less than .1. A non-homogeneous 

 variance model appears to be appropriate. 

 The p-value for Test 3 is less than .1. You may want to 

 consider a different variance model. 

 The p-value for Test 4 is less than .1. Model 2 may not adequately 

 describe the data; you may want to consider another model. 
Page 236 of 438 

 The p-value for Test 5a is less than .1. Model 3 may not adequately 

 describe the data; you may want to consider another model. 

 Degrees of freedom for Test 5b are less than or equal to 0. 

 The Chi-Square test for fit is not valid. 

 The p-value for Test 6a is greater than .1. Model 4 seems 

 to adequately describe the data. 

 The p-value for Test 6b is less than .05. Model 4 appears 

 to fit the data better than Model 2. 

 Degrees of freedom for Test 7a are less than or equal to 0. 

 The Chi-Square test for fit is not valid. 

 The p-value for Test 7b is less than .05. Model 5 appears 

 to fit the data better than Model 3. 

 The p-value for Test 7c is greater than .05. Model 5 does 

 not seem to fit the data better than Model 4. 

 Benchmark Dose Computations: 

 Specified Effect = 0.100000 

 Risk Type = Relative deviation 

 Confidence Level = 0.950000 

 BMD and BMDL by Model 

 Model BMD BMDL 

 ------- ------------ ---------- 

 2 0.206887 0.162064 

 3 0.206887 0.162064 

 4 0.0490294 0.0326672 

 5 0.0508117 0.0326739 
Page 237 of 438 

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Page 238 of 438 

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Page 239 of 438 

MRID 43980201 Subchronic Neurotoxicity in Rats – Male Adult 35 D of dosing – Brain 

 ==================================================================== 

 Exponential Model. (Version: 1.7; Date: 12/10/2009) 

 Input Data File: F:/dicrotophos/exp_Dicrotophos Brain M 35D NHV_Setting.(d) 

 Gnuplot Plotting File: 

 Fri Sep 28 09:52:07 2012 

 ==================================================================== 

 BMDS Model Run 

~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ 

 

 The form of the response function by Model: 

 Model 2: Y[dose] = a * exp{sign * b * dose} 

 Model 3: Y[dose] = a * exp{sign * (b * dose)^d} 

 Model 4: Y[dose] = a * [c-(c-1) * exp{-b * dose}] 

 Model 5: Y[dose] = a * [c-(c-1) * exp{-(b * dose)^d}] 

 Note: Y[dose] is the median response for exposure = dose; 

 sign = +1 for increasing trend in data; 

 sign = -1 for decreasing trend. 

 Model 2 is nested within Models 3 and 4. 

 Model 3 is nested within Model 5. 

 Model 4 is nested within Model 5. 

 Dependent variable = Mean 

 Independent variable = Dose 

 Data are assumed to be distributed: normally 

 Variance Model: exp(lnalpha +rho *ln(Y[dose])) 

 The variance is to be modeled as Var(i) = exp(lalpha + log(mean(i)) * rho) 

 Total number of dose groups = 4 

 Total number of records with missing values = 0 

 Maximum number of iterations = 250 

 Relative Function Convergence has been set to: 1e-008 

 Parameter Convergence has been set to: 1e-008 

 MLE solution provided: Exact 

 Initial Parameter Values 

 Variable Model 2 Model 3 Model 4 Model 5 

 -------- ------- ------- ------- ------- 
Page 240 of 438 

 lnalpha -5.35004 -5.35004 -5.35004 -5.35004 

 rho 2.13496 2.13496 2.13496 2.13496 

 a 2.56227 2.56227 11.55 11.55 

 b 1.02749 1.02749 2.58311 2.58311 

 c -- -- 0.0997732 0.0997732 

 d -- 1 -- 1 

 Parameter Estimates by Model 

 Variable Model 2 Model 3 Model 4 Model 5 

 -------- ------- ------- ------- ------- 

 lnalpha -5.7115 -5.7115 -5.52084 -5.52084 

 rho 3.53687 3.53687 2.1434 2.1434 

 a 8.70498 8.70498 10.972 10.972 

 b 0.974451 0.974451 3.07872 3.07872 

 c -- -- 0.108508 0.108508 

 d -- 1 -- 1 

 Table of Stats From Input Data 

 Dose N Obs Mean Obs Std Dev 

 ----- --- ---------- ------------- 

 0 6 11 0.95 

 0.04 6 9.82 0.68 

 0.39 6 4.13 0.36 

 2.03 6 1.21 0.08 

 Estimated Values of Interest 

 Model Dose Est Mean Est Std Scaled Residual 

 ------- ------ ---------- --------- ---------------- 

 2 0 8.705 2.64 2.129 

 0.04 8.372 2.465 1.439 

 0.39 5.953 1.348 -3.311 

 2.03 1.204 0.07988 0.1799 

 3 0 8.705 2.64 2.129 

 0.04 8.372 2.465 1.439 

 0.39 5.953 1.348 -3.311 

 2.03 1.204 0.07988 0.1799 

 4 0 10.97 0.8242 0.08333 

 0.04 9.839 0.7333 -0.06216 

 0.39 4.135 0.2896 -0.03883 
Page 241 of 438 

 2.03 1.209 0.07757 0.0179 

 5 0 10.97 0.8242 0.08333 

 0.04 9.839 0.7333 -0.06216 

 0.39 4.135 0.2896 -0.03883 

 2.03 1.209 0.07757 0.0179 

 Other models for which likelihoods are calculated: 

 Model A1: Yij = Mu(i) + e(ij) 

 Var{e(ij)} = Sigma^2 

 Model A2: Yij = Mu(i) + e(ij) 

 Var{e(ij)} = Sigma(i)^2 

 Model A3: Yij = Mu(i) + e(ij) 

 Var{e(ij)} = exp(lalpha + log(mean(i)) * rho) 

 Model R: Yij = Mu + e(i) 

 Var{e(ij)} = Sigma^2 

 Likelihoods of Interest 

 Model Log(likelihood) DF AIC 

 ------- ----------------- ---- ------------ 

 A1 1.950612 5 6.098776 

 A2 14.09387 8 -12.18775 

 A3 13.80876 6 -15.61753 

 R -45.66171 2 95.32343 

 2 -9.867837 4 27.73567 

 3 -9.867837 4 27.73567 

 4 13.79643 5 -17.59285 

 5 13.79643 5 -17.59285 

 Additive constant for all log-likelihoods = -22.05. This constant added to the 

 above values gives the log-likelihood including the term that does not 

 depend on the model parameters. 

 Explanation of Tests 

 Test 1: Does response and/or variances differ among Dose levels? (A2 vs. R) 

 Test 2: Are Variances Homogeneous? (A2 vs. A1) 
Page 242 of 438 

 Test 3: Are variances adequately modeled? (A2 vs. A3) 

 Test 4: Does Model 2 fit the data? (A3 vs. 2) 

 Test 5a: Does Model 3 fit the data? (A3 vs 3) 

 Test 5b: Is Model 3 better than Model 2? (3 vs. 2) 

 Test 6a: Does Model 4 fit the data? (A3 vs 4) 

 Test 6b: Is Model 4 better than Model 2? (4 vs. 2) 

 Test 7a: Does Model 5 fit the data? (A3 vs 5) 

 Test 7b: Is Model 5 better than Model 3? (5 vs. 3) 

 Test 7c: Is Model 5 better than Model 4? (5 vs. 4) 

 Tests of Interest 

 Test -2*log(Likelihood Ratio) D. F. p-value 

 -------- ------------------------ ------ -------------- 

 Test 1 119.5 6 < 0.0001 

 Test 2 24.29 3 < 0.0001 

 Test 3 0.5702 2 0.7519 

 Test 4 47.35 2 < 0.0001 

 Test 5a 47.35 2 < 0.0001 

 Test 5b 7.105e-015 0 N/A 

 Test 6a 0.02468 1 0.8752 

 Test 6b 47.33 1 < 0.0001 

 Test 7a 0.02468 1 0.8752 

 Test 7b 47.33 1 < 0.0001 

 Test 7c -7.105e-015 0 N/A 

 The p-value for Test 1 is less than .05. There appears to be a 

 difference between response and/or variances among the dose 

 levels, it seems appropriate to model the data. 

 The p-value for Test 2 is less than .1. A non-homogeneous 

 variance model appears to be appropriate. 

 The p-value for Test 3 is greater than .1. The modeled 

 variance appears to be appropriate here. 

 The p-value for Test 4 is less than .1. Model 2 may not adequately 

 describe the data; you may want to consider another model. 

 The p-value for Test 5a is less than .1. Model 3 may not adequately 

 describe the data; you may want to consider another model. 
Page 243 of 438 

 Degrees of freedom for Test 5b are less than or equal to 0. 

 The Chi-Square test for fit is not valid. 

 The p-value for Test 6a is greater than .1. Model 4 seems 

 to adequately describe the data. 

 The p-value for Test 6b is less than .05. Model 4 appears 

 to fit the data better than Model 2. 

 The p-value for Test 7a is greater than .1. Model 5 seems 

 to adequately describe the data. 

 The p-value for Test 7b is less than .05. Model 5 appears 

 to fit the data better than Model 3. 

 Degrees of freedom for Test 7c are less than or equal to 0. 

 The Chi-Square test for fit is not valid. 

 Benchmark Dose Computations: 

 Specified Effect = 0.100000 

 Risk Type = Relative deviation 

 Confidence Level = 0.950000 

 BMD and BMDL by Model 

 Model BMD BMDL 

 ------- ------------ ---------- 

 2 0.108123 0.101367 

 3 0.108123 0.101367 

 4 0.0386449 0.0360282 

 5 0.0386449 0.0360282 
Page 244 of 438 

Neurotoxicity – Male Adult Brain – 35D – Assuming Non-Homogeneous Variance 

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Page 245 of 438 

 

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Page 246 of 438 

MRID 43980201 Subchronic Neurotoxicity in Rats – Female Adult 35 D of dosing - Brain 

 ==================================================================== 

 Exponential Model. (Version: 1.7; Date: 12/10/2009) 

 Input Data File: F:/dicrotophos/exp_Dicrotophos Brain F 35D NHV_Setting.(d) 

 Gnuplot Plotting File: 

 Fri Sep 28 10:21:01 2012 

 ==================================================================== 

 BMDS Model Run 

~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ 

 

 The form of the response function by Model: 

 Model 2: Y[dose] = a * exp{sign * b * dose} 

 Model 3: Y[dose] = a * exp{sign * (b * dose)^d} 

 Model 4: Y[dose] = a * [c-(c-1) * exp{-b * dose}] 

 Model 5: Y[dose] = a * [c-(c-1) * exp{-(b * dose)^d}] 

 Note: Y[dose] is the median response for exposure = dose; 

 sign = +1 for increasing trend in data; 

 sign = -1 for decreasing trend. 

 Model 2 is nested within Models 3 and 4. 

 Model 3 is nested within Model 5. 

 Model 4 is nested within Model 5. 

 Dependent variable = Mean 

 Independent variable = Dose 

 Data are assumed to be distributed: normally 

 Variance Model: exp(lnalpha +rho *ln(Y[dose])) 

 The variance is to be modeled as Var(i) = exp(lalpha + log(mean(i)) * rho) 

 Total number of dose groups = 4 

 Total number of records with missing values = 0 

 Maximum number of iterations = 250 

 Relative Function Convergence has been set to: 1e-008 

 Parameter Convergence has been set to: 1e-008 

 MLE solution provided: Exact 

 Initial Parameter Values 

 Variable Model 2 Model 3 Model 4 Model 5 

 -------- ------- ------- ------- ------- 
Page 247 of 438 

 lnalpha -2.55932 -2.55932 -2.55932 -2.55932 

 rho 1.07 1.07 1.07 1.07 

 a 2.31083 2.31083 10.143 10.143 

 b 0.863059 0.863059 2.1879 2.1879 

 c -- -- 0.102346 0.102346 

 d -- 1 -- 1 

 Parameter Estimates by Model 

 Variable Model 2 Model 3 Model 4 Model 5 

 -------- ------- ------- ------- ------- 

 lnalpha -0.25934 -2.11997 -2.75269 -2.75269 

 rho -0.0920517 1.50676 1.11283 1.11283 

 a 9.41977 8.34087 9.46199 9.46199 

 b 1.99174 0.909034 2.5039 2.5039 

 c -- -- 0.113337 0.113337 

 d -- 1 -- 1 

 Table of Stats From Input Data 

 Dose N Obs Mean Obs Std Dev 

 ----- --- ---------- ------------- 

 0 6 9.66 1.07 

 0.04 6 8.44 0.78 

 0.45 6 3.82 0.54 

 2.38 6 1.09 0.3 

 Estimated Values of Interest 

 Model Dose Est Mean Est Std Scaled Residual 

 ------- ------ ---------- --------- ---------------- 

 2 0 9.42 0.7922 0.7428 

 0.04 8.698 0.7951 -0.7961 

 0.45 3.844 0.8256 -0.07136 

 2.38 0.08229 0.9854 2.505 

 3 0 8.341 1.713 1.887 

 0.04 8.043 1.666 0.5835 

 0.45 5.541 1.258 -3.349 

 2.38 0.9586 0.3356 0.9595 

 4 0 9.462 0.8817 0.5501 

 0.04 8.662 0.8394 -0.6491 

 0.45 3.791 0.53 0.1326 
Page 248 of 438 

 2.38 1.094 0.2654 -0.03736 

 5 0 9.462 0.8817 0.5501 

 0.04 8.662 0.8394 -0.6491 

 0.45 3.791 0.53 0.1326 

 2.38 1.094 0.2654 -0.03736 

 Other models for which likelihoods are calculated: 

 Model A1: Yij = Mu(i) + e(ij) 

 Var{e(ij)} = Sigma^2 

 Model A2: Yij = Mu(i) + e(ij) 

 Var{e(ij)} = Sigma(i)^2 

 Model A3: Yij = Mu(i) + e(ij) 

 Var{e(ij)} = exp(lalpha + log(mean(i)) * rho) 

 Model R: Yij = Mu + e(i) 

 Var{e(ij)} = Sigma^2 

 Likelihoods of Interest 

 Model Log(likelihood) DF AIC 

 ------- ----------------- ---- ------------ 

 A1 -2.277647 5 14.55529 

 A2 2.193629 8 11.61274 

 A3 1.990205 6 8.019589 

 R -42.24709 2 88.49418 

 2 -7.989047 4 23.97809 

 3 -13.12035 4 34.2407 

 4 1.572602 5 6.854796 

 5 1.572602 5 6.854796 

 Additive constant for all log-likelihoods = -22.05. This constant added to the 

 above values gives the log-likelihood including the term that does not 

 depend on the model parameters. 

 Explanation of Tests 

 Test 1: Does response and/or variances differ among Dose levels? (A2 vs. R) 

 Test 2: Are Variances Homogeneous? (A2 vs. A1) 
Page 249 of 438 

 Test 3: Are variances adequately modeled? (A2 vs. A3) 

 Test 4: Does Model 2 fit the data? (A3 vs. 2) 

 Test 5a: Does Model 3 fit the data? (A3 vs 3) 

 Test 5b: Is Model 3 better than Model 2? (3 vs. 2) 

 Test 6a: Does Model 4 fit the data? (A3 vs 4) 

 Test 6b: Is Model 4 better than Model 2? (4 vs. 2) 

 Test 7a: Does Model 5 fit the data? (A3 vs 5) 

 Test 7b: Is Model 5 better than Model 3? (5 vs. 3) 

 Test 7c: Is Model 5 better than Model 4? (5 vs. 4) 

 Tests of Interest 

 Test -2*log(Likelihood Ratio) D. F. p-value 

 -------- ------------------------ ------ -------------- 

 Test 1 88.88 6 < 0.0001 

 Test 2 8.943 3 0.03006 

 Test 3 0.4068 2 0.8159 

 Test 4 19.96 2 < 0.0001 

 Test 5a 30.22 2 < 0.0001 

 Test 5b -10.26 0 N/A 

 Test 6a 0.8352 1 0.3608 

 Test 6b 19.12 1 < 0.0001 

 Test 7a 0.8352 1 0.3608 

 Test 7b 29.39 1 < 0.0001 

 Test 7c 5.329e-015 0 N/A 

 The p-value for Test 1 is less than .05. There appears to be a 

 difference between response and/or variances among the dose 

 levels, it seems appropriate to model the data. 

 The p-value for Test 2 is less than .1. A non-homogeneous 

 variance model appears to be appropriate. 

 The p-value for Test 3 is greater than .1. The modeled 

 variance appears to be appropriate here. 

 The p-value for Test 4 is less than .1. Model 2 may not adequately 

 describe the data; you may want to consider another model. 

 The p-value for Test 5a is less than .1. Model 3 may not adequately 

 describe the data; you may want to consider another model. 
Page 250 of 438 

 Degrees of freedom for Test 5b are less than or equal to 0. 

 The Chi-Square test for fit is not valid. 

 The p-value for Test 6a is greater than .1. Model 4 seems 

 to adequately describe the data. 

 The p-value for Test 6b is less than .05. Model 4 appears 

 to fit the data better than Model 2. 

 The p-value for Test 7a is greater than .1. Model 5 seems 

 to adequately describe the data. 

 The p-value for Test 7b is less than .05. Model 5 appears 

 to fit the data better than Model 3. 

 Degrees of freedom for Test 7c are less than or equal to 0. 

 The Chi-Square test for fit is not valid. 

 Benchmark Dose Computations: 

 Specified Effect = 0.100000 

 Risk Type = Relative deviation 

 Confidence Level = 0.950000 

 BMD and BMDL by Model 

 Model BMD BMDL 

 ------- ------------ ---------- 

 2 0.0528987 0.044071 

 3 0.115904 0.0340592 

 4 0.0477914 0.0418788 

 5 0.0477914 0.0418788 
Page 251 of 438 

 

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Page 253 of 438 

MRID 43980201 Subchronic Neurotoxicity in Rats – Male Adult 63 D of dosing - Brain 

 ==================================================================== 

 Exponential Model. (Version: 1.7; Date: 12/10/2009) 

 Input Data File: F:/dicrotophos/exp_Dicrotophos Brain M 63D_Setting.(d) 

 Gnuplot Plotting File: 

 Wed Aug 15 17:30:00 2012 

 ==================================================================== 

 BMDS Model Run 

~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ 

 

 The form of the response function by Model: 

 Model 2: Y[dose] = a * exp{sign * b * dose} 

 Model 3: Y[dose] = a * exp{sign * (b * dose)^d} 

 Model 4: Y[dose] = a * [c-(c-1) * exp{-b * dose}] 

 Model 5: Y[dose] = a * [c-(c-1) * exp{-(b * dose)^d}] 

 Note: Y[dose] is the median response for exposure = dose; 

 sign = +1 for increasing trend in data; 

 sign = -1 for decreasing trend. 

 Model 2 is nested within Models 3 and 4. 

 Model 3 is nested within Model 5. 

 Model 4 is nested within Model 5. 

 Dependent variable = Mean 

 Independent variable = Dose 

 Data are assumed to be distributed: normally 

 Variance Model: exp(lnalpha +rho *ln(Y[dose])) 

 rho is set to 0. 

 A constant variance model is fit. 

 Total number of dose groups = 4 

 Total number of records with missing values = 0 

 Maximum number of iterations = 250 

 Relative Function Convergence has been set to: 1e-008 

 Parameter Convergence has been set to: 1e-008 

 MLE solution provided: Exact 

 Initial Parameter Values 

 Variable Model 2 Model 3 Model 4 Model 5 
Page 254 of 438 

 -------- ------- ------- ------- ------- 

 lnalpha -1.49368 -1.49368 -1.49368 -1.49368 

 rho(S) 0 0 0 0 

 a 2.66662 2.66662 11.172 11.172 

 b 1.01047 1.01047 2.57269 2.57269 

 c -- -- 0.100592 0.100592 

 d -- 1 -- 1 

 (S) = Specified 

 Parameter Estimates by Model 

 Variable Model 2 Model 3 Model 4 Model 5 

 -------- ------- ------- ------- ------- 

 lnalpha -0.38777 -0.387771 -0.944247 -0.944247 

 rho 0 0 0 0 

 a 10.1109 10.1109 10.1943 10.1943 

 b 2.23756 2.23756 2.93089 2.93089 

 c -- -- 0.119181 0.119181 

 d -- 1 -- 1 

 Table of Stats From Input Data 

 Dose N Obs Mean Obs Std Dev 

 ----- --- ---------- ------------- 

 0 6 10.64 0.76 

 0.04 5 8.53 0.54 

 0.39 6 4.24 0.3 

 2.03 5 1.18 0.33 

 Estimated Values of Interest 

 Model Dose Est Mean Est Std Scaled Residual 

 ------- ------ ---------- --------- ---------------- 

 2 0 10.11 0.8238 1.573 

 0.04 9.245 0.8238 -1.942 

 0.39 4.225 0.8238 0.04529 

 2.03 0.1077 0.8238 2.911 

 3 0 10.11 0.8238 1.573 

 0.04 9.245 0.8238 -1.942 

 0.39 4.225 0.8238 0.04529 

 2.03 0.1077 0.8238 2.911 
Page 255 of 438 

 4 0 10.19 0.6237 1.751 

 0.04 9.201 0.6237 -2.406 

 0.39 4.078 0.6237 0.6364 

 2.03 1.238 0.6237 -0.2093 

 5 0 10.19 0.6237 1.751 

 0.04 9.201 0.6237 -2.406 

 0.39 4.078 0.6237 0.6364 

 2.03 1.238 0.6237 -0.2093 

 Other models for which likelihoods are calculated: 

 Model A1: Yij = Mu(i) + e(ij) 

 Var{e(ij)} = Sigma^2 

 Model A2: Yij = Mu(i) + e(ij) 

 Var{e(ij)} = Sigma(i)^2 

 Model A3: Yij = Mu(i) + e(ij) 

 Var{e(ij)} = exp(lalpha + log(mean(i)) * rho) 

 Model R: Yij = Mu + e(i) 

 Var{e(ij)} = Sigma^2 

 Likelihoods of Interest 

 Model Log(likelihood) DF AIC 

 ------- ----------------- ---- ------------ 

 A1 5.430448 5 -0.8608967 

 A2 8.704349 8 -1.408698 

 A3 5.430448 5 -0.8608967 

 R -39.71535 2 83.43071 

 2 -6.734526 3 19.46905 

 3 -6.734526 3 19.46905 

 4 -0.6132832 4 9.226566 

 5 -0.6132832 4 9.226566 

 Additive constant for all log-likelihoods = -20.22. This constant added to the 

 above values gives the log-likelihood including the term that does not 

 depend on the model parameters. 

 Explanation of Tests 
Page 256 of 438 

 Test 1: Does response and/or variances differ among Dose levels? (A2 vs. R) 

 Test 2: Are Variances Homogeneous? (A2 vs. A1) 

 Test 3: Are variances adequately modeled? (A2 vs. A3) 

 Test 4: Does Model 2 fit the data? (A3 vs. 2) 

 Test 5a: Does Model 3 fit the data? (A3 vs 3) 

 Test 5b: Is Model 3 better than Model 2? (3 vs. 2) 

 Test 6a: Does Model 4 fit the data? (A3 vs 4) 

 Test 6b: Is Model 4 better than Model 2? (4 vs. 2) 

 Test 7a: Does Model 5 fit the data? (A3 vs 5) 

 Test 7b: Is Model 5 better than Model 3? (5 vs. 3) 

 Test 7c: Is Model 5 better than Model 4? (5 vs. 4) 

 Tests of Interest 

 Test -2*log(Likelihood Ratio) D. F. p-value 

 -------- ------------------------ ------ -------------- 

 Test 1 96.84 6 < 0.0001 

 Test 2 6.548 3 0.0878 

 Test 3 6.548 3 0.0878 

 Test 4 24.33 2 < 0.0001 

 Test 5a 24.33 2 < 0.0001 

 Test 5b -4.141e-012 0 N/A 

 Test 6a 12.09 1 0.0005076 

 Test 6b 12.24 1 0.0004671 

 Test 7a 12.09 1 0.0005076 

 Test 7b 12.24 1 0.0004671 

 Test 7c -6.767e-012 0 N/A 

 The p-value for Test 1 is less than .05. There appears to be a 

 difference between response and/or variances among the dose 

 levels, it seems appropriate to model the data. 

 The p-value for Test 2 is less than .1. Consider running 

 a non-homogeneous variance model. 

 The p-value for Test 3 is less than .1. You may want to 

 consider a different variance model. 

 The p-value for Test 4 is less than .1. Model 2 may not adequately 

 describe the data; you may want to consider another model. 
Page 257 of 438 

 The p-value for Test 5a is less than .1. Model 3 may not adequately 

 describe the data; you may want to consider another model. 

 Degrees of freedom for Test 5b are less than or equal to 0. 

 The Chi-Square test for fit is not valid. 

 The p-value for Test 6a is less than .1. Model 4 may not adequately 

 describe the data; you may want to consider another model. 

 The p-value for Test 6b is less than .05. Model 4 appears 

 to fit the data better than Model 2. 

 The p-value for Test 7a is less than .1. Model 5 may not adequately 

 describe the data; you may want to consider another model. 

 The p-value for Test 7b is less than .05. Model 5 appears 

 to fit the data better than Model 3. 

 Degrees of freedom for Test 7c are less than or equal to 0. 

 The Chi-Square test for fit is not valid. 

 Benchmark Dose Computations: 

 Specified Effect = 0.100000 

 Risk Type = Relative deviation 

 Confidence Level = 0.950000 

 BMD and BMDL by Model 

 Model BMD BMDL 

 ------- ------------ ---------- 

 2 0.0470873 0.0394876 

 3 0.0470873 0.0394876 

 4 0.0411167 0.0350677 

 5 0.0411167 0.0350677 
Page 258 of 438 

 

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Page 259 of 438 

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Page 260 of 438 

MRID 43980201 Subchronic Neurotoxicity in Rats – Female Adult 63 D of dosing - Brain

 ==================================================================== 

 Exponential Model. (Version: 1.7; Date: 12/10/2009) 

 Input Data File: F:/dicrotophos/exp_Dicrotophos Brain F 63D NHV_Setting.(d) 

 Gnuplot Plotting File: 

 Fri Sep 28 10:26:47 2012 

 ==================================================================== 

 BMDS Model Run 

~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ 

 

 The form of the response function by Model: 

 Model 2: Y[dose] = a * exp{sign * b * dose} 

 Model 3: Y[dose] = a * exp{sign * (b * dose)^d} 

 Model 4: Y[dose] = a * [c-(c-1) * exp{-b * dose}] 

 Model 5: Y[dose] = a * [c-(c-1) * exp{-(b * dose)^d}] 

 Note: Y[dose] is the median response for exposure = dose; 

 sign = +1 for increasing trend in data; 

 sign = -1 for decreasing trend. 

 Model 2 is nested within Models 3 and 4. 

 Model 3 is nested within Model 5. 

 Model 4 is nested within Model 5. 

 Dependent variable = Mean 

 Independent variable = Dose 

 Data are assumed to be distributed: normally 

 Variance Model: exp(lnalpha +rho *ln(Y[dose])) 

 The variance is to be modeled as Var(i) = exp(lalpha + log(mean(i)) * rho) 

 Total number of dose groups = 4 

 Total number of records with missing values = 0 

 Maximum number of iterations = 250 

 Relative Function Convergence has been set to: 1e-008 

 Parameter Convergence has been set to: 1e-008 

 MLE solution provided: Exact 

 Initial Parameter Values 

 Variable Model 2 Model 3 Model 4 Model 5 
Page 261 of 438 

 -------- ------- ------- ------- ------- 

 lnalpha -4.68033 -4.68033 -4.68033 -4.68033 

 rho 1.87867 1.87867 1.87867 1.87867 

 a 2.3627 2.3627 11.361 11.361 

 b 0.913632 0.913632 2.25288 2.25288 

 c -- -- 0.089697 0.089697 

 d -- 1 -- 1 

 Parameter Estimates by Model 

 Variable Model 2 Model 3 Model 4 Model 5 

 -------- ------- ------- ------- ------- 

 lnalpha -4.77973 -5.02263 -4.90013 -4.90013 

 rho 3.06724 3.47531 1.99599 1.99599 

 a 8.59347 8.05 10.5797 10.5797 

 b 0.879352 0.44095 2.65259 2.65259 

 c -- -- 0.0995164 0.0995164 

 d -- 14.5433 -- 1 

 Table of Stats From Input Data 

 Dose N Obs Mean Obs Std Dev 

 ----- --- ---------- ------------- 

 0 6 10.82 1.17 

 0.04 6 9.38 0.56 

 0.45 6 3.95 0.39 

 2.38 6 1.07 0.1 

 Estimated Values of Interest 

 Model Dose Est Mean Est Std Scaled Residual 

 ------- ------ ---------- --------- ---------------- 

 2 0 8.593 2.482 2.198 

 0.04 8.296 2.351 1.129 

 0.45 5.785 1.353 -3.323 

 2.38 1.06 0.1002 0.2477 

 3 0 8.05 3.043 2.23 

 0.04 8.05 3.043 1.071 

 0.45 8.05 3.043 -3.3 

 2.38 1.07 0.09129 9.564e-010 

 4 0 10.58 0.9086 0.6479 

 0.04 9.621 0.8264 -0.7133 
Page 262 of 438 

 0.45 3.941 0.3391 0.0685 

 2.38 1.07 0.09233 -0.00313 

 5 0 10.58 0.9086 0.6479 

 0.04 9.621 0.8264 -0.7133 

 0.45 3.941 0.3391 0.0685 

 2.38 1.07 0.09233 -0.00313 

 Other models for which likelihoods are calculated: 

 Model A1: Yij = Mu(i) + e(ij) 

 Var{e(ij)} = Sigma^2 

 Model A2: Yij = Mu(i) + e(ij) 

 Var{e(ij)} = Sigma(i)^2 

 Model A3: Yij = Mu(i) + e(ij) 

 Var{e(ij)} = exp(lalpha + log(mean(i)) * rho) 

 Model R: Yij = Mu + e(i) 

 Var{e(ij)} = Sigma^2 

 Likelihoods of Interest 

 Model Log(likelihood) DF AIC 

 ------- ----------------- ---- ------------ 

 A1 -0.5237584 5 11.04752 

 A2 12.18991 8 -8.379818 

 A3 11.14526 6 -10.29053 

 R -45.33264 2 94.66529 

 2 -10.59215 4 29.18429 

 3 -17.66907 5 45.33813 

 4 10.50284 5 -11.00568 

 5 10.50284 5 -11.00568 

 Additive constant for all log-likelihoods = -22.05. This constant added to the 

 above values gives the log-likelihood including the term that does not 

 depend on the model parameters. 

 Explanation of Tests 

 Test 1: Does response and/or variances differ among Dose levels? (A2 vs. R) 
Page 263 of 438 

 Test 2: Are Variances Homogeneous? (A2 vs. A1) 

 Test 3: Are variances adequately modeled? (A2 vs. A3) 

 Test 4: Does Model 2 fit the data? (A3 vs. 2) 

 Test 5a: Does Model 3 fit the data? (A3 vs 3) 

 Test 5b: Is Model 3 better than Model 2? (3 vs. 2) 

 Test 6a: Does Model 4 fit the data? (A3 vs 4) 

 Test 6b: Is Model 4 better than Model 2? (4 vs. 2) 

 Test 7a: Does Model 5 fit the data? (A3 vs 5) 

 Test 7b: Is Model 5 better than Model 3? (5 vs. 3) 

 Test 7c: Is Model 5 better than Model 4? (5 vs. 4) 

 Tests of Interest 

 Test -2*log(Likelihood Ratio) D. F. p-value 

 -------- ------------------------ ------ -------------- 

 Test 1 115 6 < 0.0001 

 Test 2 25.43 3 < 0.0001 

 Test 3 2.089 2 0.3518 

 Test 4 43.47 2 < 0.0001 

 Test 5a 57.63 1 < 0.0001 

 Test 5b -14.15 1 N/A 

 Test 6a 1.285 1 0.257 

 Test 6b 42.19 1 < 0.0001 

 Test 7a 1.285 1 0.257 

 Test 7b 56.34 0 N/A 

 Test 7c -1.219e-012 0 N/A 

 The p-value for Test 1 is less than .05. There appears to be a 

 difference between response and/or variances among the dose 

 levels, it seems appropriate to model the data. 

 The p-value for Test 2 is less than .1. A non-homogeneous 

 variance model appears to be appropriate. 

 The p-value for Test 3 is greater than .1. The modeled 

 variance appears to be appropriate here. 

 The p-value for Test 4 is less than .1. Model 2 may not adequately 

 describe the data; you may want to consider another model. 

 The p-value for Test 5a is less than .1. Model 3 may not adequately 
Page 264 of 438 

 describe the data; you may want to consider another model. 

 The p-value for Test 5b is less than .05. Model 3 appears 

 to fit the data better than Model 2. 

 The p-value for Test 6a is greater than .1. Model 4 seems 

 to adequately describe the data. 

 The p-value for Test 6b is less than .05. Model 4 appears 

 to fit the data better than Model 2. 

 The p-value for Test 7a is greater than .1. Model 5 seems 

 to adequately describe the data. 

 Degrees of freedom for Test 7b are less than or equal to 0. 

 The Chi-Square test for fit is not valid. 

 Degrees of freedom for Test 7c are less than or equal to 0. 

 The Chi-Square test for fit is not valid. 

 Benchmark Dose Computations: 

 Specified Effect = 0.100000 

 Risk Type = Relative deviation 

 Confidence Level = 0.950000 

 BMD and BMDL by Model 

 Model BMD BMDL 

 ------- ------------ ---------- 

 2 0.119816 0.111804 

 3 1.94272 0.0264226 

 4 0.0443778 0.0409235 

 5 0.0443778 0.0409235 
Page 265 of 438 

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Page 266 of 438 

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Page 267 of 438 

MRID 43980201 Subchronic Neurotoxicity in Rats – Male Adult 98 D of dosing - Brain

 ==================================================================== 

 Exponential Model. (Version: 1.7; Date: 12/10/2009) 

 Input Data File: F:/dicrotophos/exp_Dicrotophos Brain M 98D NHV_Setting.(d) 

 Gnuplot Plotting File: 

 Fri Sep 28 10:14:31 2012 

 ==================================================================== 

 BMDS Model Run 

~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ 

 

 The form of the response function by Model: 

 Model 2: Y[dose] = a * exp{sign * b * dose} 

 Model 3: Y[dose] = a * exp{sign * (b * dose)^d} 

 Model 4: Y[dose] = a * [c-(c-1) * exp{-b * dose}] 

 Model 5: Y[dose] = a * [c-(c-1) * exp{-(b * dose)^d}] 

 Note: Y[dose] is the median response for exposure = dose; 

 sign = +1 for increasing trend in data; 

 sign = -1 for decreasing trend. 

 Model 2 is nested within Models 3 and 4. 

 Model 3 is nested within Model 5. 

 Model 4 is nested within Model 5. 

 Dependent variable = Mean 

 Independent variable = Dose 

 Data are assumed to be distributed: normally 

 Variance Model: exp(lnalpha +rho *ln(Y[dose])) 

 The variance is to be modeled as Var(i) = exp(lalpha + log(mean(i)) * rho) 

 Total number of dose groups = 4 

 Total number of records with missing values = 0 

 Maximum number of iterations = 250 

 Relative Function Convergence has been set to: 1e-008 

 Parameter Convergence has been set to: 1e-008 

 MLE solution provided: Exact 

 Initial Parameter Values 
Page 268 of 438 

 Variable Model 2 Model 3 Model 4 Model 5 

 -------- ------- ------- ------- ------- 

 lnalpha -5.05353 -5.05353 -5.05353 -5.05353 

 rho 1.62388 1.62388 1.62388 1.62388 

 a 2.71461 2.71461 10.3215 10.3215 

 b 0.959459 0.959459 2.49046 2.49046 

 c -- -- 0.115339 0.115339 

 d -- 1 -- 1 

 Parameter Estimates by Model 

 Variable Model 2 Model 3 Model 4 Model 5 

 -------- ------- ------- ------- ------- 

 lnalpha -0.746064 -5.80558 -5.07656 -5.07656 

 rho -0.445126 3.37553 1.72791 1.72791 

 a 9.58233 7.99941 9.58415 9.58415 

 b 2.07729 0.917593 2.5519 2.5519 

 c -- -- 0.125271 0.125271 

 d -- 1 -- 1 

 Table of Stats From Input Data 

 Dose N Obs Mean Obs Std Dev 

 ----- --- ---------- ------------- 

 0 5 9.83 0.5 

 0.04 6 8.57 0.34 

 0.39 6 4.3 0.43 

 2.03 5 1.25 0.08 

 Estimated Values of Interest 

 Model Dose Est Mean Est Std Scaled Residual 

 ------- ------ ---------- --------- ---------------- 

 2 0 9.582 0.4164 1.33 

 0.04 8.818 0.4242 -1.434 

 0.39 4.262 0.4987 0.1858 

 2.03 0.1413 1.065 2.329 

 3 0 7.999 1.834 2.231 

 0.04 7.711 1.724 1.22 

 0.39 5.593 1.003 -3.158 

 2.03 1.242 0.07909 0.2286 

 4 0 9.584 0.5567 0.9874 
Page 269 of 438 

 0.04 8.771 0.5157 -0.953 

 0.39 4.299 0.2785 0.004762 

 2.03 1.248 0.09565 0.05186 

 5 0 9.584 0.5567 0.9874 

 0.04 8.771 0.5157 -0.953 

 0.39 4.299 0.2785 0.004762 

 2.03 1.248 0.09565 0.05186 

 Other models for which likelihoods are calculated: 

 Model A1: Yij = Mu(i) + e(ij) 

 Var{e(ij)} = Sigma^2 

 Model A2: Yij = Mu(i) + e(ij) 

 Var{e(ij)} = Sigma(i)^2 

 Model A3: Yij = Mu(i) + e(ij) 

 Var{e(ij)} = exp(lalpha + log(mean(i)) * rho) 

 Model R: Yij = Mu + e(i) 

 Var{e(ij)} = Sigma^2 

 Likelihoods of Interest 

 Model Log(likelihood) DF AIC 

 ------- ----------------- ---- ------------ 

 A1 12.79932 5 -15.59864 

 A2 18.8407 8 -21.68141 

 A3 16.39977 6 -20.79954 

 R -37.54094 2 79.08187 

 2 2.38671 4 3.226581 

 3 -4.63259 4 17.26518 

 4 15.30633 5 -20.61266 

 5 15.30633 5 -20.61266 

 Additive constant for all log-likelihoods = -20.22. This constant added to the 

 above values gives the log-likelihood including the term that does not 

 depend on the model parameters. 

 Explanation of Tests 
Page 270 of 438 

 Test 1: Does response and/or variances differ among Dose levels? (A2 vs. R) 

 Test 2: Are Variances Homogeneous? (A2 vs. A1) 

 Test 3: Are variances adequately modeled? (A2 vs. A3) 

 Test 4: Does Model 2 fit the data? (A3 vs. 2) 

 Test 5a: Does Model 3 fit the data? (A3 vs 3) 

 Test 5b: Is Model 3 better than Model 2? (3 vs. 2) 

 Test 6a: Does Model 4 fit the data? (A3 vs 4) 

 Test 6b: Is Model 4 better than Model 2? (4 vs. 2) 

 Test 7a: Does Model 5 fit the data? (A3 vs 5) 

 Test 7b: Is Model 5 better than Model 3? (5 vs. 3) 

 Test 7c: Is Model 5 better than Model 4? (5 vs. 4) 

 Tests of Interest 

 Test -2*log(Likelihood Ratio) D. F. p-value 

 -------- ------------------------ ------ -------------- 

 Test 1 112.8 6 < 0.0001 

 Test 2 12.08 3 0.007105 

 Test 3 4.882 2 0.08708 

 Test 4 28.03 2 < 0.0001 

 Test 5a 42.06 2 < 0.0001 

 Test 5b -14.04 0 N/A 

 Test 6a 2.187 1 0.1392 

 Test 6b 25.84 1 < 0.0001 

 Test 7a 2.187 1 0.1392 

 Test 7b 39.88 1 < 0.0001 

 Test 7c 0 0 N/A 

 The p-value for Test 1 is less than .05. There appears to be a 

 difference between response and/or variances among the dose 

 levels, it seems appropriate to model the data. 

 The p-value for Test 2 is less than .1. A non-homogeneous 

 variance model appears to be appropriate. 

 The p-value for Test 3 is less than .1. You may want to 

 consider a different variance model. 

 The p-value for Test 4 is less than .1. Model 2 may not adequately 

 describe the data; you may want to consider another model. 
Page 271 of 438 

 The p-value for Test 5a is less than .1. Model 3 may not adequately 

 describe the data; you may want to consider another model. 

 Degrees of freedom for Test 5b are less than or equal to 0. 

 The Chi-Square test for fit is not valid. 

 The p-value for Test 6a is greater than .1. Model 4 seems 

 to adequately describe the data. 

 The p-value for Test 6b is less than .05. Model 4 appears 

 to fit the data better than Model 2. 

 The p-value for Test 7a is greater than .1. Model 5 seems 

 to adequately describe the data. 

 The p-value for Test 7b is less than .05. Model 5 appears 

 to fit the data better than Model 3. 

 Degrees of freedom for Test 7c are less than or equal to 0. 

 The Chi-Square test for fit is not valid. 

 Benchmark Dose Computations: 

 Specified Effect = 0.100000 

 Risk Type = Relative deviation 

 Confidence Level = 0.950000 

 BMD and BMDL by Model 

 Model BMD BMDL 

 ------- ------------ ---------- 

 2 0.0507201 0.045494 

 3 0.114823 0.107812 

 4 0.0475727 0.0440646 

 5 0.0475727 0.0440646 

 
Page 272 of 438 

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Page 274 of 438 

MRID 43980201 Subchronic Neurotoxicity in Rats – Female Adult 98 D of dosing - Brain

 ==================================================================== 

 Exponential Model. (Version: 1.7; Date: 12/10/2009) 

 Input Data File: F:/dicrotophos/exp_Dicrotophos Brain F 98D NHV_Setting.(d) 

 Gnuplot Plotting File: 

 Fri Sep 28 10:31:24 2012 

 ==================================================================== 

 BMDS Model Run 

~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ 

 

 The form of the response function by Model: 

 Model 2: Y[dose] = a * exp{sign * b * dose} 

 Model 3: Y[dose] = a * exp{sign * (b * dose)^d} 

 Model 4: Y[dose] = a * [c-(c-1) * exp{-b * dose}] 

 Model 5: Y[dose] = a * [c-(c-1) * exp{-(b * dose)^d}] 

 Note: Y[dose] is the median response for exposure = dose; 

 sign = +1 for increasing trend in data; 

 sign = -1 for decreasing trend. 

 Model 2 is nested within Models 3 and 4. 

 Model 3 is nested within Model 5. 

 Model 4 is nested within Model 5. 

 Dependent variable = Mean 

 Independent variable = Dose 

 Data are assumed to be distributed: normally 

 Variance Model: exp(lnalpha +rho *ln(Y[dose])) 

 The variance is to be modeled as Var(i) = exp(lalpha + log(mean(i)) * rho) 

 Total number of dose groups = 4 

 Total number of records with missing values = 0 

 Maximum number of iterations = 250 

 Relative Function Convergence has been set to: 1e-008 

 Parameter Convergence has been set to: 1e-008 

 MLE solution provided: Exact 

 Initial Parameter Values 

 Variable Model 2 Model 3 Model 4 Model 5 
Page 275 of 438 

 -------- ------- ------- ------- ------- 

 lnalpha -4.89038 -4.89038 -4.89038 -4.89038 

 rho 1.92736 1.92736 1.92736 1.92736 

 a 2.55186 2.55186 11.256 11.256 

 b 0.847726 0.847726 2.18707 2.18707 

 c -- -- 0.104071 0.104071 

 d -- 1 -- 1 

 Parameter Estimates by Model 

 Variable Model 2 Model 3 Model 4 Model 5 

 -------- ------- ------- ------- ------- 

 lnalpha -5.27493 -5.27493 -5.11347 -5.11347 

 rho 3.33392 3.33393 2.02428 2.02428 

 a 8.37786 8.37786 10.4017 10.4017 

 b 0.809137 0.809137 2.6591 2.6591 

 c -- -- 0.116653 0.116653 

 d -- 1 -- 1 

 Table of Stats From Input Data 

 Dose N Obs Mean Obs Std Dev 

 ----- --- ---------- ------------- 

 0 6 10.72 0.87 

 0.04 6 9.16 0.66 

 0.45 6 4 0.38 

 2.38 6 1.23 0.1 

 Estimated Values of Interest 

 Model Dose Est Mean Est Std Scaled Residual 

 ------- ------ ---------- --------- ---------------- 

 2 0 8.378 2.474 2.319 

 0.04 8.111 2.344 1.096 

 0.45 5.821 1.348 -3.308 

 2.38 1.221 0.09983 0.2155 

 3 0 8.378 2.474 2.319 

 0.04 8.111 2.344 1.096 

 0.45 5.821 1.348 -3.308 

 2.38 1.221 0.09983 0.2155 

 4 0 10.4 0.83 0.9393 

 0.04 9.475 0.7552 -1.02 
Page 276 of 438 

 0.45 3.99 0.3147 0.07557 

 2.38 1.23 0.09562 0.005597 

 5 0 10.4 0.83 0.9393 

 0.04 9.475 0.7552 -1.02 

 0.45 3.99 0.3147 0.07557 

 2.38 1.23 0.09562 0.005597 

 Other models for which likelihoods are calculated: 

 Model A1: Yij = Mu(i) + e(ij) 

 Var{e(ij)} = Sigma^2 

 Model A2: Yij = Mu(i) + e(ij) 

 Var{e(ij)} = Sigma(i)^2 

 Model A3: Yij = Mu(i) + e(ij) 

 Var{e(ij)} = exp(lalpha + log(mean(i)) * rho) 

 Model R: Yij = Mu + e(i) 

 Var{e(ij)} = Sigma^2 

 Likelihoods of Interest 

 Model Log(likelihood) DF AIC 

 ------- ----------------- ---- ------------ 

 A1 3.249723 5 3.500554 

 A2 13.13754 8 -10.27508 

 A3 12.9266 6 -13.8532 

 R -44.46281 2 92.92563 

 2 -10.51323 4 29.02647 

 3 -10.51323 4 29.02647 

 4 11.8236 5 -13.6472 

 5 11.8236 5 -13.6472 

 Additive constant for all log-likelihoods = -22.05. This constant added to the 

 above values gives the log-likelihood including the term that does not 

 depend on the model parameters. 

 Explanation of Tests 

 Test 1: Does response and/or variances differ among Dose levels? (A2 vs. R) 
Page 277 of 438 

 Test 2: Are Variances Homogeneous? (A2 vs. A1) 

 Test 3: Are variances adequately modeled? (A2 vs. A3) 

 Test 4: Does Model 2 fit the data? (A3 vs. 2) 

 Test 5a: Does Model 3 fit the data? (A3 vs 3) 

 Test 5b: Is Model 3 better than Model 2? (3 vs. 2) 

 Test 6a: Does Model 4 fit the data? (A3 vs 4) 

 Test 6b: Is Model 4 better than Model 2? (4 vs. 2) 

 Test 7a: Does Model 5 fit the data? (A3 vs 5) 

 Test 7b: Is Model 5 better than Model 3? (5 vs. 3) 

 Test 7c: Is Model 5 better than Model 4? (5 vs. 4) 

 Tests of Interest 

 Test -2*log(Likelihood Ratio) D. F. p-value 

 -------- ------------------------ ------ -------------- 

 Test 1 115.2 6 < 0.0001 

 Test 2 19.78 3 0.0001889 

 Test 3 0.4219 2 0.8098 

 Test 4 46.88 2 < 0.0001 

 Test 5a 46.88 2 < 0.0001 

 Test 5b -3.421e-011 0 N/A 

 Test 6a 2.206 1 0.1375 

 Test 6b 44.67 1 < 0.0001 

 Test 7a 2.206 1 0.1375 

 Test 7b 44.67 1 < 0.0001 

 Test 7c -8.917e-013 0 N/A 

 The p-value for Test 1 is less than .05. There appears to be a 

 difference between response and/or variances among the dose 

 levels, it seems appropriate to model the data. 

 The p-value for Test 2 is less than .1. A non-homogeneous 

 variance model appears to be appropriate. 

 The p-value for Test 3 is greater than .1. The modeled 

 variance appears to be appropriate here. 

 The p-value for Test 4 is less than .1. Model 2 may not adequately 

 describe the data; you may want to consider another model. 

 The p-value for Test 5a is less than .1. Model 3 may not adequately 
Page 278 of 438 

 describe the data; you may want to consider another model. 

 Degrees of freedom for Test 5b are less than or equal to 0. 

 The Chi-Square test for fit is not valid. 

 The p-value for Test 6a is greater than .1. Model 4 seems 

 to adequately describe the data. 

 The p-value for Test 6b is less than .05. Model 4 appears 

 to fit the data better than Model 2. 

 The p-value for Test 7a is greater than .1. Model 5 seems 

 to adequately describe the data. 

 The p-value for Test 7b is less than .05. Model 5 appears 

 to fit the data better than Model 3. 

 Degrees of freedom for Test 7c are less than or equal to 0. 

 The Chi-Square test for fit is not valid. 

 Benchmark Dose Computations: 

 Specified Effect = 0.100000 

 Risk Type = Relative deviation 

 Confidence Level = 0.950000 

 BMD and BMDL by Model 

 Model BMD BMDL 

 ------- ------------ ---------- 

 2 0.130213 0.121365 

 3 0.130213 0.121365 

 4 0.0451815 0.0417724 

 5 0.0451815 0.0417724 
Page 279 of 438 

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Page 280 of 438 

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Page 281 of 438 

MRID 43980201 Subchronic Neurotoxicity in Rats – Male Adult 35 D of dosing - RBC

==================================================================== 

 Exponential Model. (Version: 1.7; Date: 12/10/2009) 

 Input Data File: F:/dicrotophos/exp_Dicrotophos RBC M 35D_Setting.(d) 

 Gnuplot Plotting File: 

 Wed Aug 15 18:22:26 2012 

 ==================================================================== 

 BMDS Model Run 

~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ 

 

 The form of the response function by Model: 

 Model 2: Y[dose] = a * exp{sign * b * dose} 

 Model 3: Y[dose] = a * exp{sign * (b * dose)^d} 

 Model 4: Y[dose] = a * [c-(c-1) * exp{-b * dose}] 

 Model 5: Y[dose] = a * [c-(c-1) * exp{-(b * dose)^d}] 

 Note: Y[dose] is the median response for exposure = dose; 

 sign = +1 for increasing trend in data; 

 sign = -1 for decreasing trend. 

 Model 2 is nested within Models 3 and 4. 

 Model 3 is nested within Model 5. 

 Model 4 is nested within Model 5. 

 Dependent variable = Mean 

 Independent variable = Dose 

 Data are assumed to be distributed: normally 

 Variance Model: exp(lnalpha +rho *ln(Y[dose])) 

 rho is set to 0. 

 A constant variance model is fit. 

 Total number of dose groups = 4 

 Total number of records with missing values = 0 

 Maximum number of iterations = 250 

 Relative Function Convergence has been set to: 1e-008 

 Parameter Convergence has been set to: 1e-008 

 MLE solution provided: Exact 

 Initial Parameter Values 

 Variable Model 2 Model 3 Model 4 Model 5 
Page 282 of 438 

 -------- ------- ------- ------- ------- 

 lnalpha 10.4438 10.4438 10.4438 10.4438 

 rho(S) 0 0 0 0 

 a 1304.83 1304.83 2186.1 2186.1 

 b 0.263495 0.263495 1.63727 1.63727 

 c -- -- 0.486624 0.486624 

 d -- 1 -- 1 

 (S) = Specified 

 Parameter Estimates by Model 

 Variable Model 2 Model 3 Model 4 Model 5 

 -------- ------- ------- ------- ------- 

 lnalpha 11.5468 11.5468 10.4752 10.4438 

 rho 0 0 0 0 

 a 1878.08 1878.08 2115.78 2082 

 b 0.322765 0.322765 5.15125 4.94339 

 c -- -- 0.520209 0.536503 

 d -- 1 -- 1.30887 

 Table of Stats From Input Data 

 Dose N Obs Mean Obs Std Dev 

 ----- --- ---------- ------------- 

 0 6 2082 198 

 0.04 6 1973 137 

 0.39 6 1208 224 

 2.03 6 1117 238 

 Estimated Values of Interest 

 Model Dose Est Mean Est Std Scaled Residual 

 ------- ------ ---------- --------- ---------------- 

 2 0 1878 321.6 1.553 

 0.04 1854 321.6 0.9063 

 0.39 1656 321.6 -3.411 

 2.03 975.3 321.6 1.079 

 3 0 1878 321.6 1.553 

 0.04 1854 321.6 0.9063 

 0.39 1656 321.6 -3.411 

 2.03 975.3 321.6 1.079 
Page 283 of 438 

 4 0 2116 188.2 -0.4396 

 0.04 1927 188.2 0.6019 

 0.39 1237 188.2 -0.3748 

 2.03 1101 188.2 0.2125 

 5 0 2082 185.3 -4.731e-008 

 0.04 1973 185.3 -1.347e-008 

 0.39 1208 185.3 -1.916e-007 

 2.03 1117 185.3 -9.454e-008 

 Other models for which likelihoods are calculated: 

 Model A1: Yij = Mu(i) + e(ij) 

 Var{e(ij)} = Sigma^2 

 Model A2: Yij = Mu(i) + e(ij) 

 Var{e(ij)} = Sigma(i)^2 

 Model A3: Yij = Mu(i) + e(ij) 

 Var{e(ij)} = exp(lalpha + log(mean(i)) * rho) 

 Model R: Yij = Mu + e(i) 

 Var{e(ij)} = Sigma^2 

 Likelihoods of Interest 

 Model Log(likelihood) DF AIC 

 ------- ----------------- ---- ------------ 

 A1 -137.326 5 284.6519 

 A2 -136.3651 8 288.7303 

 A3 -137.326 5 284.6519 

 R -159.8279 2 323.6558 

 2 -150.562 3 307.1239 

 3 -150.562 3 307.1239 

 4 -137.7023 4 283.4047 

 5 -137.326 5 284.6519 

 Additive constant for all log-likelihoods = -22.05. This constant added to the 

 above values gives the log-likelihood including the term that does not 

 depend on the model parameters. 

 Explanation of Tests 
Page 284 of 438 

 Test 1: Does response and/or variances differ among Dose levels? (A2 vs. R) 

 Test 2: Are Variances Homogeneous? (A2 vs. A1) 

 Test 3: Are variances adequately modeled? (A2 vs. A3) 

 Test 4: Does Model 2 fit the data? (A3 vs. 2) 

 Test 5a: Does Model 3 fit the data? (A3 vs 3) 

 Test 5b: Is Model 3 better than Model 2? (3 vs. 2) 

 Test 6a: Does Model 4 fit the data? (A3 vs 4) 

 Test 6b: Is Model 4 better than Model 2? (4 vs. 2) 

 Test 7a: Does Model 5 fit the data? (A3 vs 5) 

 Test 7b: Is Model 5 better than Model 3? (5 vs. 3) 

 Test 7c: Is Model 5 better than Model 4? (5 vs. 4) 

 Tests of Interest 

 Test -2*log(Likelihood Ratio) D. F. p-value 

 -------- ------------------------ ------ -------------- 

 Test 1 46.93 6 < 0.0001 

 Test 2 1.922 3 0.5888 

 Test 3 1.922 3 0.5888 

 Test 4 26.47 2 < 0.0001 

 Test 5a 26.47 2 < 0.0001 

 Test 5b -6.253e-013 0 N/A 

 Test 6a 0.7528 1 0.3856 

 Test 6b 25.72 1 < 0.0001 

 Test 7a 5.684e-014 0 N/A 

 Test 7b 26.47 2 < 0.0001 

 Test 7c 0.7528 1 0.3856 

 The p-value for Test 1 is less than .05. There appears to be a 

 difference between response and/or variances among the dose 

 levels, it seems appropriate to model the data. 

 The p-value for Test 2 is greater than .1. A homogeneous 

 variance model appears to be appropriate here. 

 The p-value for Test 3 is greater than .1. The modeled 

 variance appears to be appropriate here. 

 The p-value for Test 4 is less than .1. Model 2 may not adequately 

 describe the data; you may want to consider another model. 
Page 285 of 438 

 The p-value for Test 5a is less than .1. Model 3 may not adequately 

 describe the data; you may want to consider another model. 

 Degrees of freedom for Test 5b are less than or equal to 0. 

 The Chi-Square test for fit is not valid. 

 The p-value for Test 6a is greater than .1. Model 4 seems 

 to adequately describe the data. 

 The p-value for Test 6b is less than .05. Model 4 appears 

 to fit the data better than Model 2. 

 Degrees of freedom for Test 7a are less than or equal to 0. 

 The Chi-Square test for fit is not valid. 

 The p-value for Test 7b is less than .05. Model 5 appears 

 to fit the data better than Model 3. 

 The p-value for Test 7c is greater than .05. Model 5 does 

 not seem to fit the data better than Model 4. 

 Benchmark Dose Computations: 

 Specified Effect = 0.100000 

 Risk Type = Relative deviation 

 Confidence Level = 0.950000 

 BMD and BMDL by Model 

 Model BMD BMDL 

 ------- ------------ ---------- 

 2 0.326431 0.225356 

 3 0.326431 0.225356 

 4 0.0453734 0.0268497 

 5 0.0686448 0.029309 

Subchronic Study (MRID 43980201) –Male RBC 35 Day ChEI 
Page 286 of 438 

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18:22 08/15 2012

BMDL BMD

 

Exponential

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Page 287 of 438 

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Exponential Model 4 with 0.95 Confidence Level

18:22 08/15 2012

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Exponential

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Exponential
Page 288 of 438 

MRID 43980201 Subchronic Neurotoxicity in Rats – Female Adult 35 D of dosing - RBC

 ==================================================================== 

 Exponential Model. (Version: 1.7; Date: 12/10/2009) 

 Input Data File: F:/dicrotophos/exp_Dicrotophos RBC F 35D_Setting.(d) 

 Gnuplot Plotting File: 

 Wed Aug 15 18:28:54 2012 

 ==================================================================== 

 BMDS Model Run 

~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ 

 

 The form of the response function by Model: 

 Model 2: Y[dose] = a * exp{sign * b * dose} 

 Model 3: Y[dose] = a * exp{sign * (b * dose)^d} 

 Model 4: Y[dose] = a * [c-(c-1) * exp{-b * dose}] 

 Model 5: Y[dose] = a * [c-(c-1) * exp{-(b * dose)^d}] 

 Note: Y[dose] is the median response for exposure = dose; 

 sign = +1 for increasing trend in data; 

 sign = -1 for decreasing trend. 

 Model 2 is nested within Models 3 and 4. 

 Model 3 is nested within Model 5. 

 Model 4 is nested within Model 5. 

 Dependent variable = Mean 

 Independent variable = Dose 

 Data are assumed to be distributed: normally 

 Variance Model: exp(lnalpha +rho *ln(Y[dose])) 

 rho is set to 0. 

 A constant variance model is fit. 

 Total number of dose groups = 4 

 Total number of records with missing values = 0 

 Maximum number of iterations = 250 

 Relative Function Convergence has been set to: 1e-008 

 Parameter Convergence has been set to: 1e-008 

 MLE solution provided: Exact 

 Initial Parameter Values 

 Variable Model 2 Model 3 Model 4 Model 5 
Page 289 of 438 

 -------- ------- ------- ------- ------- 

 lnalpha 10.1611 10.1611 10.1611 10.1611 

 rho(S) 0 0 0 0 

 a 1404.69 1404.69 2334.15 2334.15 

 b 0.202233 0.202233 1.35745 1.35745 

 c -- -- 0.493705 0.493705 

 d -- 1 -- 1 

 (S) = Specified 

 Parameter Estimates by Model 

 Variable Model 2 Model 3 Model 4 Model 5 

 -------- ------- ------- ------- ------- 

 lnalpha 11.3332 11.3332 10.2709 10.2709 

 rho 0 0 0 0 

 a 1930.7 1930.7 2215.67 2215.67 

 b 0.23614 0.23614 10.4606 10.4606 

 c -- -- 0.579666 0.579666 

 d -- 1 -- 1 

 Table of Stats From Input Data 

 Dose N Obs Mean Obs Std Dev 

 ----- --- ---------- ------------- 

 0 6 2223 194 

 0.04 6 1885 220 

 0.45 6 1372 124 

 2.38 6 1210 151 

 Estimated Values of Interest 

 Model Dose Est Mean Est Std Scaled Residual 

 ------- ------ ---------- --------- ---------------- 

 2 0 1931 289.1 2.477 

 0.04 1913 289.1 -0.2335 

 0.45 1736 289.1 -3.085 

 2.38 1101 289.1 0.9269 

 3 0 1931 289.1 2.477 

 0.04 1913 289.1 -0.2335 

 0.45 1736 289.1 -3.085 

 2.38 1101 289.1 0.9269 
Page 290 of 438 

 4 0 2216 169.9 0.1057 

 0.04 1897 169.9 -0.1763 

 0.45 1293 169.9 1.142 

 2.38 1284 169.9 -1.072 

 5 0 2216 169.9 0.1057 

 0.04 1897 169.9 -0.1763 

 0.45 1293 169.9 1.142 

 2.38 1284 169.9 -1.072 

 Other models for which likelihoods are calculated: 

 Model A1: Yij = Mu(i) + e(ij) 

 Var{e(ij)} = Sigma^2 

 Model A2: Yij = Mu(i) + e(ij) 

 Var{e(ij)} = Sigma(i)^2 

 Model A3: Yij = Mu(i) + e(ij) 

 Var{e(ij)} = exp(lalpha + log(mean(i)) * rho) 

 Model R: Yij = Mu + e(i) 

 Var{e(ij)} = Sigma^2 

 Likelihoods of Interest 

 Model Log(likelihood) DF AIC 

 ------- ----------------- ---- ------------ 

 A1 -133.9336 5 277.8673 

 A2 -132.8064 8 281.6128 

 A3 -133.9336 5 277.8673 

 R -157.7928 2 319.5856 

 2 -147.999 3 301.9979 

 3 -147.999 3 301.9979 

 4 -135.251 4 278.502 

 5 -135.251 4 278.502 

 Additive constant for all log-likelihoods = -22.05. This constant added to the 

 above values gives the log-likelihood including the term that does not 

 depend on the model parameters. 

 Explanation of Tests 
Page 291 of 438 

 Test 1: Does response and/or variances differ among Dose levels? (A2 vs. R) 

 Test 2: Are Variances Homogeneous? (A2 vs. A1) 

 Test 3: Are variances adequately modeled? (A2 vs. A3) 

 Test 4: Does Model 2 fit the data? (A3 vs. 2) 

 Test 5a: Does Model 3 fit the data? (A3 vs 3) 

 Test 5b: Is Model 3 better than Model 2? (3 vs. 2) 

 Test 6a: Does Model 4 fit the data? (A3 vs 4) 

 Test 6b: Is Model 4 better than Model 2? (4 vs. 2) 

 Test 7a: Does Model 5 fit the data? (A3 vs 5) 

 Test 7b: Is Model 5 better than Model 3? (5 vs. 3) 

 Test 7c: Is Model 5 better than Model 4? (5 vs. 4) 

 Tests of Interest 

 Test -2*log(Likelihood Ratio) D. F. p-value 

 -------- ------------------------ ------ -------------- 

 Test 1 49.97 6 < 0.0001 

 Test 2 2.254 3 0.5213 

 Test 3 2.254 3 0.5213 

 Test 4 28.13 2 < 0.0001 

 Test 5a 28.13 2 < 0.0001 

 Test 5b -2.274e-013 0 N/A 

 Test 6a 2.635 1 0.1046 

 Test 6b 25.5 1 < 0.0001 

 Test 7a 2.635 1 0.1046 

 Test 7b 25.5 1 < 0.0001 

 Test 7c 5.684e-014 0 N/A 

 The p-value for Test 1 is less than .05. There appears to be a 

 difference between response and/or variances among the dose 

 levels, it seems appropriate to model the data. 

 The p-value for Test 2 is greater than .1. A homogeneous 

 variance model appears to be appropriate here. 

 The p-value for Test 3 is greater than .1. The modeled 

 variance appears to be appropriate here. 

 The p-value for Test 4 is less than .1. Model 2 may not adequately 

 describe the data; you may want to consider another model. 
Page 292 of 438 

 The p-value for Test 5a is less than .1. Model 3 may not adequately 

 describe the data; you may want to consider another model. 

 Degrees of freedom for Test 5b are less than or equal to 0. 

 The Chi-Square test for fit is not valid. 

 The p-value for Test 6a is greater than .1. Model 4 seems 

 to adequately describe the data. 

 The p-value for Test 6b is less than .05. Model 4 appears 

 to fit the data better than Model 2. 

 The p-value for Test 7a is greater than .1. Model 5 seems 

 to adequately describe the data. 

 The p-value for Test 7b is less than .05. Model 5 appears 

 to fit the data better than Model 3. 

 Degrees of freedom for Test 7c are less than or equal to 0. 

 The Chi-Square test for fit is not valid. 

 Benchmark Dose Computations: 

 Specified Effect = 0.100000 

 Risk Type = Relative deviation 

 Confidence Level = 0.950000 

 BMD and BMDL by Model 

 Model BMD BMDL 

 ------- ------------ ---------- 

 2 0.446179 0.318588 

 3 0.446179 0.318588 

 4 0.0259723 0.015175 

 5 0.0259723 0.015175 
Page 293 of 438 

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Exponential
Page 294 of 438 

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Page 295 of 438 

MRID 43980201 Subchronic Neurotoxicity in Rats – Male Adult 63 D of dosing - RBC

 ==================================================================== 

 Exponential Model. (Version: 1.7; Date: 12/10/2009) 

 Input Data File: F:/dicrotophos/exp_Dicrotophos RBC M 63D_Setting.(d) 

 Gnuplot Plotting File: 

 Thu Aug 16 10:46:01 2012 

 ==================================================================== 

 BMDS Model Run 

~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ 

 

 The form of the response function by Model: 

 Model 2: Y[dose] = a * exp{sign * b * dose} 

 Model 3: Y[dose] = a * exp{sign * (b * dose)^d} 

 Model 4: Y[dose] = a * [c-(c-1) * exp{-b * dose}] 

 Model 5: Y[dose] = a * [c-(c-1) * exp{-(b * dose)^d}] 

 Note: Y[dose] is the median response for exposure = dose; 

 sign = +1 for increasing trend in data; 

 sign = -1 for decreasing trend. 

 Model 2 is nested within Models 3 and 4. 

 Model 3 is nested within Model 5. 

 Model 4 is nested within Model 5. 

 Dependent variable = Mean 

 Independent variable = Dose 

 Data are assumed to be distributed: normally 

 Variance Model: exp(lnalpha +rho *ln(Y[dose])) 

 rho is set to 0. 

 A constant variance model is fit. 

 Total number of dose groups = 4 

 Total number of records with missing values = 0 

 Maximum number of iterations = 250 

 Relative Function Convergence has been set to: 1e-008 

 Parameter Convergence has been set to: 1e-008 

 MLE solution provided: Exact 

 Initial Parameter Values 

 Variable Model 2 Model 3 Model 4 Model 5 
Page 296 of 438 

 -------- ------- ------- ------- ------- 

 lnalpha 10.7728 10.7728 10.7728 10.7728 

 rho(S) 0 0 0 0 

 a 1377.45 1377.45 2270.1 2270.1 

 b 0.260646 0.260646 1.61428 1.61428 

 c -- -- 0.487497 0.487497 

 d -- 1 -- 1 

 (S) = Specified 

 Parameter Estimates by Model 

 Variable Model 2 Model 3 Model 4 Model 5 

 -------- ------- ------- ------- ------- 

 lnalpha 11.645 11.645 10.7728 10.7728 

 rho 0 0 0 0 

 a 1931.26 1931.26 2163.18 2162 

 b 0.32133 0.32133 5.10741 5.10008 

 c -- -- 0.536847 0.537454 

 d -- 1 -- 1.00968 

 Table of Stats From Input Data 

 Dose N Obs Mean Obs Std Dev 

 ----- --- ---------- ------------- 

 0 6 2162 330 

 0.04 5 1980 237 

 0.39 6 1297 190 

 2.03 5 1162 158 

 Estimated Values of Interest 

 Model Dose Est Mean Est Std Scaled Residual 

 ------- ------ ---------- --------- ---------------- 

 2 0 1931 337.8 1.673 

 0.04 1907 337.8 0.4858 

 0.39 1704 337.8 -2.95 

 2.03 1006 337.8 1.033 

 3 0 1931 337.8 1.673 

 0.04 1907 337.8 0.4858 

 0.39 1704 337.8 -2.95 

 2.03 1006 337.8 1.033 
Page 297 of 438 

 4 0 2163 218.4 -0.01329 

 0.04 1978 218.4 0.01989 

 0.39 1298 218.4 -0.01114 

 2.03 1161 218.4 0.006859 

 5 0 2162 218.4 1.92e-010 

 0.04 1980 218.4 8.428e-009 

 0.39 1297 218.4 -4.705e-009 

 2.03 1162 218.4 -3.77e-008 

 Other models for which likelihoods are calculated: 

 Model A1: Yij = Mu(i) + e(ij) 

 Var{e(ij)} = Sigma^2 

 Model A2: Yij = Mu(i) + e(ij) 

 Var{e(ij)} = Sigma(i)^2 

 Model A3: Yij = Mu(i) + e(ij) 

 Var{e(ij)} = exp(lalpha + log(mean(i)) * rho) 

 Model R: Yij = Mu + e(i) 

 Var{e(ij)} = Sigma^2 

 Likelihoods of Interest 

 Model Log(likelihood) DF AIC 

 ------- ----------------- ---- ------------ 

 A1 -129.5009 5 269.0019 

 A2 -127.7203 8 271.4407 

 A3 -129.5009 5 269.0019 

 R -146.8995 2 297.799 

 2 -139.095 3 284.19 

 3 -139.095 3 284.19 

 4 -129.5013 4 267.0026 

 5 -129.5009 5 269.0019 

 Additive constant for all log-likelihoods = -20.22. This constant added to the 

 above values gives the log-likelihood including the term that does not 

 depend on the model parameters. 

 Explanation of Tests 
Page 298 of 438 

 Test 1: Does response and/or variances differ among Dose levels? (A2 vs. R) 

 Test 2: Are Variances Homogeneous? (A2 vs. A1) 

 Test 3: Are variances adequately modeled? (A2 vs. A3) 

 Test 4: Does Model 2 fit the data? (A3 vs. 2) 

 Test 5a: Does Model 3 fit the data? (A3 vs 3) 

 Test 5b: Is Model 3 better than Model 2? (3 vs. 2) 

 Test 6a: Does Model 4 fit the data? (A3 vs 4) 

 Test 6b: Is Model 4 better than Model 2? (4 vs. 2) 

 Test 7a: Does Model 5 fit the data? (A3 vs 5) 

 Test 7b: Is Model 5 better than Model 3? (5 vs. 3) 

 Test 7c: Is Model 5 better than Model 4? (5 vs. 4) 

 Tests of Interest 

 Test -2*log(Likelihood Ratio) D. F. p-value 

 -------- ------------------------ ------ -------------- 

 Test 1 38.36 6 < 0.0001 

 Test 2 3.561 3 0.3129 

 Test 3 3.561 3 0.3129 

 Test 4 19.19 2 < 0.0001 

 Test 5a 19.19 2 < 0.0001 

 Test 5b 5.684e-014 0 N/A 

 Test 6a 0.0007433 1 0.9782 

 Test 6b 19.19 1 < 0.0001 

 Test 7a 0 0 N/A 

 Test 7b 19.19 2 < 0.0001 

 Test 7c 0.0007433 1 0.9782 

 The p-value for Test 1 is less than .05. There appears to be a 

 difference between response and/or variances among the dose 

 levels, it seems appropriate to model the data. 

 The p-value for Test 2 is greater than .1. A homogeneous 

 variance model appears to be appropriate here. 

 The p-value for Test 3 is greater than .1. The modeled 

 variance appears to be appropriate here. 

 The p-value for Test 4 is less than .1. Model 2 may not adequately 

 describe the data; you may want to consider another model. 
Page 299 of 438 

 The p-value for Test 5a is less than .1. Model 3 may not adequately 

 describe the data; you may want to consider another model. 

 Degrees of freedom for Test 5b are less than or equal to 0. 

 The Chi-Square test for fit is not valid. 

 The p-value for Test 6a is greater than .1. Model 4 seems 

 to adequately describe the data. 

 The p-value for Test 6b is less than .05. Model 4 appears 

 to fit the data better than Model 2. 

 Degrees of freedom for Test 7a are less than or equal to 0. 

 The Chi-Square test for fit is not valid. 

 The p-value for Test 7b is less than .05. Model 5 appears 

 to fit the data better than Model 3. 

 The p-value for Test 7c is greater than .05. Model 5 does 

 not seem to fit the data better than Model 4. 

 Benchmark Dose Computations: 

 Specified Effect = 0.100000 

 Risk Type = Relative deviation 

 Confidence Level = 0.950000 

 BMD and BMDL by Model 

 Model BMD BMDL 

 ------- ------------ ---------- 

 2 0.327888 0.217228 

 3 0.327888 0.217228 

 4 0.0476235 0.0220455 

 5 0.0484141 0.022048 
Page 300 of 438 

 

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Exponential
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Exponential
Page 302 of 438 

MRID 43980201 Subchronic Neurotoxicity in Rats – Female Adult 63 D of dosing - RBC

 ==================================================================== 

 Exponential Model. (Version: 1.7; Date: 12/10/2009) 

 Input Data File: F:/dicrotophos/exp_Dicrotophos RBC F 63D NHV_Setting.(d) 

 Gnuplot Plotting File: 

 Fri Sep 28 11:18:26 2012 

 ==================================================================== 

 BMDS Model Run 

~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ 

 

 The form of the response function by Model: 

 Model 2: Y[dose] = a * exp{sign * b * dose} 

 Model 3: Y[dose] = a * exp{sign * (b * dose)^d} 

 Model 4: Y[dose] = a * [c-(c-1) * exp{-b * dose}] 

 Model 5: Y[dose] = a * [c-(c-1) * exp{-(b * dose)^d}] 

 Note: Y[dose] is the median response for exposure = dose; 

 sign = +1 for increasing trend in data; 

 sign = -1 for decreasing trend. 

 Model 2 is nested within Models 3 and 4. 

 Model 3 is nested within Model 5. 

 Model 4 is nested within Model 5. 

 Dependent variable = Mean 

 Independent variable = Dose 

 Data are assumed to be distributed: normally 

 Variance Model: exp(lnalpha +rho *ln(Y[dose])) 

 The variance is to be modeled as Var(i) = exp(lalpha + log(mean(i)) * rho) 

 Total number of dose groups = 4 

 Total number of records with missing values = 0 

 Maximum number of iterations = 250 

 Relative Function Convergence has been set to: 1e-008 

 Parameter Convergence has been set to: 1e-008 

 MLE solution provided: Exact 

 Initial Parameter Values 
Page 303 of 438 

 Variable Model 2 Model 3 Model 4 Model 5 

 -------- ------- ------- ------- ------- 

 lnalpha 18.7242 18.7242 18.7242 18.7242 

 rho -1.11765 -1.11765 -1.11765 -1.11765 

 a 1345.69 74.3657 2343.6 2343.6 

 b 0.255319 -0.616097 1.42005 1.42005 

 c -- -- 0.452296 0.452296 

 d -- 2 -- 1 

 Parameter Estimates by Model 

 Variable Model 2 Model 3 Model 4 Model 5 

 -------- ------- ------- ------- ------- 

 lnalpha 20.2881 -16.3558 14.1275 14.0468 

 rho -1.28831 3.79399 -0.483572 -0.473011 

 a 2210.33 1880 2245.09 2234.69 

 b 1.10865 0.402495 3.856 3.79697 

 c -- -- 0.49561 0.499659 

 d -- 15.03 -- 1.07725 

 Table of Stats From Input Data 

 Dose N Obs Mean Obs Std Dev 

 ----- --- ---------- ------------- 

 0 6 2232 94 

 0.04 6 2100 267 

 0.45 6 1308 266 

 2.38 6 1113 185 

 Estimated Values of Interest 

 Model Dose Est Mean Est Std Scaled Residual 

 ------- ------ ---------- --------- ---------------- 

 2 0 2210 178.3 0.2977 

 0.04 2114 183.5 -0.1929 

 0.45 1342 245.9 -0.3399 

 2.38 158 975.7 2.398 

 3 0 1880 456.5 1.889 

 0.04 1880 456.5 1.18 

 0.45 1880 456.5 -3.069 

 2.38 1113 168.9 -3.162e-011 

 4 0 2245 180.9 -0.1772 
Page 304 of 438 

 0.04 2083 184.2 0.223 

 0.45 1312 206 -0.05237 

 2.38 1113 214.4 0.002226 

 5 0 2235 181.2 -0.03642 

 0.04 2097 183.9 0.03843 

 0.45 1305 205.8 0.03589 

 2.38 1117 213.5 -0.04142 

 Other models for which likelihoods are calculated: 

 Model A1: Yij = Mu(i) + e(ij) 

 Var{e(ij)} = Sigma^2 

 Model A2: Yij = Mu(i) + e(ij) 

 Var{e(ij)} = Sigma(i)^2 

 Model A3: Yij = Mu(i) + e(ij) 

 Var{e(ij)} = exp(lalpha + log(mean(i)) * rho) 

 Model R: Yij = Mu + e(i) 

 Var{e(ij)} = Sigma^2 

 Likelihoods of Interest 

 Model Log(likelihood) DF AIC 

 ------- ----------------- ---- ------------ 

 A1 -138.7208 5 287.4416 

 A2 -135.4185 8 286.837 

 A3 -138.6256 6 289.2512 

 R -162.2392 2 328.4784 

 2 -148.7014 4 305.4028 

 3 -153.0004 5 316.0008 

 4 -138.6573 5 287.3147 

 5 -138.6256 6 289.2512 

 Additive constant for all log-likelihoods = -22.05. This constant added to the 

 above values gives the log-likelihood including the term that does not 

 depend on the model parameters. 

 Explanation of Tests 
Page 305 of 438 

 Test 1: Does response and/or variances differ among Dose levels? (A2 vs. R) 

 Test 2: Are Variances Homogeneous? (A2 vs. A1) 

 Test 3: Are variances adequately modeled? (A2 vs. A3) 

 Test 4: Does Model 2 fit the data? (A3 vs. 2) 

 Test 5a: Does Model 3 fit the data? (A3 vs 3) 

 Test 5b: Is Model 3 better than Model 2? (3 vs. 2) 

 Test 6a: Does Model 4 fit the data? (A3 vs 4) 

 Test 6b: Is Model 4 better than Model 2? (4 vs. 2) 

 Test 7a: Does Model 5 fit the data? (A3 vs 5) 

 Test 7b: Is Model 5 better than Model 3? (5 vs. 3) 

 Test 7c: Is Model 5 better than Model 4? (5 vs. 4) 

 Tests of Interest 

 Test -2*log(Likelihood Ratio) D. F. p-value 

 -------- ------------------------ ------ -------------- 

 Test 1 53.64 6 < 0.0001 

 Test 2 6.605 3 0.08563 

 Test 3 6.414 2 0.04047 

 Test 4 20.15 2 < 0.0001 

 Test 5a 28.75 1 < 0.0001 

 Test 5b -8.598 1 N/A 

 Test 6a 0.06344 1 0.8011 

 Test 6b 20.09 1 < 0.0001 

 Test 7a -4.857e-009 0 N/A 

 Test 7b 28.75 1 < 0.0001 

 Test 7c 0.06344 1 0.8011 

 The p-value for Test 1 is less than .05. There appears to be a 

 difference between response and/or variances among the dose 

 levels, it seems appropriate to model the data. 

 The p-value for Test 2 is less than .1. A non-homogeneous 

 variance model appears to be appropriate. 

 The p-value for Test 3 is less than .1. You may want to 

 consider a different variance model. 

 The p-value for Test 4 is less than .1. Model 2 may not adequately 

 describe the data; you may want to consider another model. 
Page 306 of 438 

 The p-value for Test 5a is less than .1. Model 3 may not adequately 

 describe the data; you may want to consider another model. 

 The p-value for Test 5b is less than .05. Model 3 appears 

 to fit the data better than Model 2. 

 The p-value for Test 6a is greater than .1. Model 4 seems 

 to adequately describe the data. 

 The p-value for Test 6b is less than .05. Model 4 appears 

 to fit the data better than Model 2. 

 Degrees of freedom for Test 7a are less than or equal to 0. 

 The Chi-Square test for fit is not valid. 

 The p-value for Test 7b is less than .05. Model 5 appears 

 to fit the data better than Model 3. 

 The p-value for Test 7c is greater than .05. Model 5 does 

 not seem to fit the data better than Model 4. 

 Benchmark Dose Computations: 

 Specified Effect = 0.100000 

 Risk Type = Relative deviation 

 Confidence Level = 0.950000 

 BMD and BMDL by Model 

 Model BMD BMDL 

 ------- ------------ ---------- 

 2 0.0950353 0.0725077 

 3 2.13902 0.0543219 

 4 0.0573055 0.0329713 

 5 0.0653959 0.0332344 
Page 307 of 438 

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11:18 09/28 2012

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Exponential

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Exponential
Page 308 of 438 

 

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Page 309 of 438 

MRID 43980201 Subchronic Neurotoxicity in Rats – Male Adult 98 D of dosing - RBC

 ==================================================================== 

 Exponential Model. (Version: 1.7; Date: 12/10/2009) 

 Input Data File: F:/dicrotophos/exp_Dicrotophos RBC M 98D_Setting.(d) 

 Gnuplot Plotting File: 

 Thu Aug 16 10:51:51 2012 

 ==================================================================== 

 BMDS Model Run 

~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ 

 

 The form of the response function by Model: 

 Model 2: Y[dose] = a * exp{sign * b * dose} 

 Model 3: Y[dose] = a * exp{sign * (b * dose)^d} 

 Model 4: Y[dose] = a * [c-(c-1) * exp{-b * dose}] 

 Model 5: Y[dose] = a * [c-(c-1) * exp{-(b * dose)^d}] 

 Note: Y[dose] is the median response for exposure = dose; 

 sign = +1 for increasing trend in data; 

 sign = -1 for decreasing trend. 

 Model 2 is nested within Models 3 and 4. 

 Model 3 is nested within Model 5. 

 Model 4 is nested within Model 5. 

 Dependent variable = Mean 

 Independent variable = Dose 

 Data are assumed to be distributed: normally 

 Variance Model: exp(lnalpha +rho *ln(Y[dose])) 

 rho is set to 0. 

 A constant variance model is fit. 

 Total number of dose groups = 4 

 Total number of records with missing values = 0 

 Maximum number of iterations = 250 

 Relative Function Convergence has been set to: 1e-008 

 Parameter Convergence has been set to: 1e-008 

 MLE solution provided: Exact 

 Initial Parameter Values 

 Variable Model 2 Model 3 Model 4 Model 5 
Page 310 of 438 

 -------- ------- ------- ------- ------- 

 lnalpha 10.8012 10.8012 10.8012 10.8012 

 rho(S) 0 0 0 0 

 a 1301.44 1301.44 2475.9 2475.9 

 b 0.300176 0.300176 1.80553 1.80553 

 c -- -- 0.416203 0.416203 

 d -- 1 -- 1 

 (S) = Specified 

 Parameter Estimates by Model 

 Variable Model 2 Model 3 Model 4 Model 5 

 -------- ------- ------- ------- ------- 

 lnalpha 12.0285 12.0285 10.8076 10.8076 

 rho 0 0 0 0 

 a 1964.71 1964.71 2351.37 2351.37 

 b 0.411444 0.411444 9.25845 9.25845 

 c -- -- 0.470352 0.470352 

 d -- 1 -- 1 

 Table of Stats From Input Data 

 Dose N Obs Mean Obs Std Dev 

 ----- --- ---------- ------------- 

 0 5 2358 230 

 0.04 6 1957 279 

 0.39 6 1163 246 

 2.03 5 1082 210 

 Estimated Values of Interest 

 Model Dose Est Mean Est Std Scaled Residual 

 ------- ------ ---------- --------- ---------------- 

 2 0 1965 409.2 2.149 

 0.04 1933 409.2 0.1458 

 0.39 1673 409.2 -3.055 

 2.03 852.2 409.2 1.255 

 3 0 1965 409.2 2.149 

 0.04 1933 409.2 0.1458 

 0.39 1673 409.2 -3.055 

 2.03 852.2 409.2 1.255 
Page 311 of 438 

 4 0 2351 222.2 0.06673 

 0.04 1966 222.2 -0.0983 

 0.39 1140 222.2 0.2575 

 2.03 1106 222.2 -0.2412 

 5 0 2351 222.2 0.06673 

 0.04 1966 222.2 -0.0983 

 0.39 1140 222.2 0.2575 

 2.03 1106 222.2 -0.2412 

 Other models for which likelihoods are calculated: 

 Model A1: Yij = Mu(i) + e(ij) 

 Var{e(ij)} = Sigma^2 

 Model A2: Yij = Mu(i) + e(ij) 

 Var{e(ij)} = Sigma(i)^2 

 Model A3: Yij = Mu(i) + e(ij) 

 Var{e(ij)} = exp(lalpha + log(mean(i)) * rho) 

 Model R: Yij = Mu + e(i) 

 Var{e(ij)} = Sigma^2 

 Likelihoods of Interest 

 Model Log(likelihood) DF AIC 

 ------- ----------------- ---- ------------ 

 A1 -129.8135 5 269.6271 

 A2 -129.5355 8 275.0711 

 A3 -129.8135 5 269.6271 

 R -150.6552 2 305.3105 

 2 -143.3131 3 292.6261 

 3 -143.3131 3 292.6261 

 4 -129.8831 4 267.7661 

 5 -129.8831 4 267.7661 

 Additive constant for all log-likelihoods = -20.22. This constant added to the 

 above values gives the log-likelihood including the term that does not 

 depend on the model parameters. 

 Explanation of Tests 
Page 312 of 438 

 Test 1: Does response and/or variances differ among Dose levels? (A2 vs. R) 

 Test 2: Are Variances Homogeneous? (A2 vs. A1) 

 Test 3: Are variances adequately modeled? (A2 vs. A3) 

 Test 4: Does Model 2 fit the data? (A3 vs. 2) 

 Test 5a: Does Model 3 fit the data? (A3 vs 3) 

 Test 5b: Is Model 3 better than Model 2? (3 vs. 2) 

 Test 6a: Does Model 4 fit the data? (A3 vs 4) 

 Test 6b: Is Model 4 better than Model 2? (4 vs. 2) 

 Test 7a: Does Model 5 fit the data? (A3 vs 5) 

 Test 7b: Is Model 5 better than Model 3? (5 vs. 3) 

 Test 7c: Is Model 5 better than Model 4? (5 vs. 4) 

 Tests of Interest 

 Test -2*log(Likelihood Ratio) D. F. p-value 

 -------- ------------------------ ------ -------------- 

 Test 1 42.24 6 < 0.0001 

 Test 2 0.556 3 0.9064 

 Test 3 0.556 3 0.9064 

 Test 4 27 2 < 0.0001 

 Test 5a 27 2 < 0.0001 

 Test 5b -1.023e-012 0 N/A 

 Test 6a 0.139 1 0.7092 

 Test 6b 26.86 1 < 0.0001 

 Test 7a 0.139 1 0.7092 

 Test 7b 26.86 1 < 0.0001 

 Test 7c 5.684e-014 0 N/A 

 The p-value for Test 1 is less than .05. There appears to be a 

 difference between response and/or variances among the dose 

 levels, it seems appropriate to model the data. 

 The p-value for Test 2 is greater than .1. A homogeneous 

 variance model appears to be appropriate here. 

 The p-value for Test 3 is greater than .1. The modeled 

 variance appears to be appropriate here. 

 The p-value for Test 4 is less than .1. Model 2 may not adequately 

 describe the data; you may want to consider another model. 
Page 313 of 438 

 The p-value for Test 5a is less than .1. Model 3 may not adequately 

 describe the data; you may want to consider another model. 

 Degrees of freedom for Test 5b are less than or equal to 0. 

 The Chi-Square test for fit is not valid. 

 The p-value for Test 6a is greater than .1. Model 4 seems 

 to adequately describe the data. 

 The p-value for Test 6b is less than .05. Model 4 appears 

 to fit the data better than Model 2. 

 The p-value for Test 7a is greater than .1. Model 5 seems 

 to adequately describe the data. 

 The p-value for Test 7b is less than .05. Model 5 appears 

 to fit the data better than Model 3. 

 Degrees of freedom for Test 7c are less than or equal to 0. 

 The Chi-Square test for fit is not valid. 

 Benchmark Dose Computations: 

 Specified Effect = 0.100000 

 Risk Type = Relative deviation 

 Confidence Level = 0.950000 

 BMD and BMDL by Model 

 Model BMD BMDL 

 ------- ------------ ---------- 

 2 0.256075 0.155401 

 3 0.256075 0.155401 

 4 0.0226006 0.0130755 

 5 0.0226006 0.0130755 
Page 314 of 438 

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Exponential
Page 315 of 438 

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Page 316 of 438 

MRID 43980201 Subchronic Neurotoxicity in Rats – Female Adult 98 D of dosing - RBC

 ==================================================================== 

 Exponential Model. (Version: 1.7; Date: 12/10/2009) 

 Input Data File: F:/dicrotophos/exp_Dicrotophos RBC F 98D_Setting.(d) 

 Gnuplot Plotting File: 

 Thu Aug 16 11:05:41 2012 

 ==================================================================== 

 BMDS Model Run 

~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ 

 

 The form of the response function by Model: 

 Model 2: Y[dose] = a * exp{sign * b * dose} 

 Model 3: Y[dose] = a * exp{sign * (b * dose)^d} 

 Model 4: Y[dose] = a * [c-(c-1) * exp{-b * dose}] 

 Model 5: Y[dose] = a * [c-(c-1) * exp{-(b * dose)^d}] 

 Note: Y[dose] is the median response for exposure = dose; 

 sign = +1 for increasing trend in data; 

 sign = -1 for decreasing trend. 

 Model 2 is nested within Models 3 and 4. 

 Model 3 is nested within Model 5. 

 Model 4 is nested within Model 5. 

 Dependent variable = Mean 

 Independent variable = Dose 

 Data are assumed to be distributed: normally 

 Variance Model: exp(lnalpha +rho *ln(Y[dose])) 

 rho is set to 0. 

 A constant variance model is fit. 

 Total number of dose groups = 4 

 Total number of records with missing values = 0 

 Maximum number of iterations = 250 

 Relative Function Convergence has been set to: 1e-008 

 Parameter Convergence has been set to: 1e-008 

 MLE solution provided: Exact 

 Initial Parameter Values 

 Variable Model 2 Model 3 Model 4 Model 5 
Page 317 of 438 

 -------- ------- ------- ------- ------- 

 lnalpha 12.1245 12.1245 12.1245 12.1245 

 rho(S) 0 0 0 0 

 a 1352.9 1352.9 2732.1 2732.1 

 b 0.359925 0.359925 1.5662 1.5662 

 c -- -- 0.356258 0.356258 

 d -- 1 -- 1 

 (S) = Specified 

 Parameter Estimates by Model 

 Variable Model 2 Model 3 Model 4 Model 5 

 -------- ------- ------- ------- ------- 

 lnalpha 12.5983 12.5983 12.1658 12.1263 

 rho 0 0 0 0 

 a 2413.34 2413.34 2657.26 2576 

 b 0.4809 0.4809 3.02373 2.33836 

 c -- -- 0.378347 0.396739 

 d -- 1 -- 7.2391 

 Table of Stats From Input Data 

 Dose N Obs Mean Obs Std Dev 

 ----- --- ---------- ------------- 

 0 6 2550 138 

 0.04 6 2602 896 

 0.45 6 1388 124 

 2.38 6 1022 218 

 Estimated Values of Interest 

 Model Dose Est Mean Est Std Scaled Residual 

 ------- ------ ---------- --------- ---------------- 

 2 0 2413 544.1 0.6152 

 0.04 2367 544.1 1.056 

 0.45 1944 544.1 -2.502 

 2.38 768.3 544.1 1.142 

 3 0 2413 544.1 0.6152 

 0.04 2367 544.1 1.056 

 0.45 1944 544.1 -2.502 

 2.38 768.3 544.1 1.142 
Page 318 of 438 

 4 0 2657 438.3 -0.5995 

 0.04 2469 438.3 0.7429 

 0.45 1429 438.3 -0.2295 

 2.38 1007 438.3 0.08604 

 5 0 2576 429.7 -0.1482 

 0.04 2576 429.7 0.1482 

 0.45 1388 429.7 7.29e-008 

 2.38 1022 429.7 3.877e-007 

 Other models for which likelihoods are calculated: 

 Model A1: Yij = Mu(i) + e(ij) 

 Var{e(ij)} = Sigma^2 

 Model A2: Yij = Mu(i) + e(ij) 

 Var{e(ij)} = Sigma(i)^2 

 Model A3: Yij = Mu(i) + e(ij) 

 Var{e(ij)} = exp(lalpha + log(mean(i)) * rho) 

 Model R: Yij = Mu + e(i) 

 Var{e(ij)} = Sigma^2 

 Likelihoods of Interest 

 Model Log(likelihood) DF AIC 

 ------- ----------------- ---- ------------ 

 A1 -157.4935 5 324.9869 

 A2 -141.392 8 298.7839 

 A3 -157.4935 5 324.9869 

 R -173.004 2 350.0079 

 2 -163.1795 3 332.359 

 3 -163.1795 3 332.359 

 4 -157.9892 4 323.9784 

 5 -157.5155 5 325.0309 

 Additive constant for all log-likelihoods = -22.05. This constant added to the 

 above values gives the log-likelihood including the term that does not 

 depend on the model parameters. 

 Explanation of Tests 
Page 319 of 438 

 Test 1: Does response and/or variances differ among Dose levels? (A2 vs. R) 

 Test 2: Are Variances Homogeneous? (A2 vs. A1) 

 Test 3: Are variances adequately modeled? (A2 vs. A3) 

 Test 4: Does Model 2 fit the data? (A3 vs. 2) 

 Test 5a: Does Model 3 fit the data? (A3 vs 3) 

 Test 5b: Is Model 3 better than Model 2? (3 vs. 2) 

 Test 6a: Does Model 4 fit the data? (A3 vs 4) 

 Test 6b: Is Model 4 better than Model 2? (4 vs. 2) 

 Test 7a: Does Model 5 fit the data? (A3 vs 5) 

 Test 7b: Is Model 5 better than Model 3? (5 vs. 3) 

 Test 7c: Is Model 5 better than Model 4? (5 vs. 4) 

 Tests of Interest 

 Test -2*log(Likelihood Ratio) D. F. p-value 

 -------- ------------------------ ------ -------------- 

 Test 1 63.22 6 < 0.0001 

 Test 2 32.2 3 < 0.0001 

 Test 3 32.2 3 < 0.0001 

 Test 4 11.37 2 0.003393 

 Test 5a 11.37 2 0.003393 

 Test 5b -5.684e-013 0 N/A 

 Test 6a 0.9915 1 0.3194 

 Test 6b 10.38 1 0.001273 

 Test 7a 0.04397 0 N/A 

 Test 7b 11.33 2 0.003468 

 Test 7c 0.9475 1 0.3304 

 The p-value for Test 1 is less than .05. There appears to be a 

 difference between response and/or variances among the dose 

 levels, it seems appropriate to model the data. 

 The p-value for Test 2 is less than .1. Consider running 

 a non-homogeneous variance model. 

 The p-value for Test 3 is less than .1. You may want to 

 consider a different variance model. 

 The p-value for Test 4 is less than .1. Model 2 may not adequately 

 describe the data; you may want to consider another model. 
Page 320 of 438 

 The p-value for Test 5a is less than .1. Model 3 may not adequately 

 describe the data; you may want to consider another model. 

 Degrees of freedom for Test 5b are less than or equal to 0. 

 The Chi-Square test for fit is not valid. 

 The p-value for Test 6a is greater than .1. Model 4 seems 

 to adequately describe the data. 

 The p-value for Test 6b is less than .05. Model 4 appears 

 to fit the data better than Model 2. 

 Degrees of freedom for Test 7a are less than or equal to 0. 

 The Chi-Square test for fit is not valid. 

 The p-value for Test 7b is less than .05. Model 5 appears 

 to fit the data better than Model 3. 

 The p-value for Test 7c is greater than .05. Model 5 does 

 not seem to fit the data better than Model 4. 

 Benchmark Dose Computations: 

 Specified Effect = 0.100000 

 Risk Type = Relative deviation 

 Confidence Level = 0.950000 

 BMD and BMDL by Model 

 Model BMD BMDL 

 ------- ------------ ---------- 

 2 0.21909 0.129923 

 3 0.21909 0.129923 

 4 0.058001 0.0295989 

 5 0.337773 0.0348488 
Page 321 of 438 

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Page 322 of 438 

 

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Exponential
Page 323 of 438 

MRID 44527802 - Chronic Toxicity in Rats (2 year) – Male Adult 371D of dosing - Brain 

 ==================================================================== 

 Exponential Model. (Version: 1.7; Date: 12/10/2009) 

 Input Data File: F:/dicrotophos/exp_Dicrotophos Brain M 371D NHV_Setting.(d) 

 Gnuplot Plotting File: 

 Fri Sep 28 11:32:09 2012 

 ==================================================================== 

 BMDS Model Run 

~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ 

 

 The form of the response function by Model: 

 Model 2: Y[dose] = a * exp{sign * b * dose} 

 Model 3: Y[dose] = a * exp{sign * (b * dose)^d} 

 Model 4: Y[dose] = a * [c-(c-1) * exp{-b * dose}] 

 Model 5: Y[dose] = a * [c-(c-1) * exp{-(b * dose)^d}] 

 Note: Y[dose] is the median response for exposure = dose; 

 sign = +1 for increasing trend in data; 

 sign = -1 for decreasing trend. 

 Model 2 is nested within Models 3 and 4. 

 Model 3 is nested within Model 5. 

 Model 4 is nested within Model 5. 

 Dependent variable = Mean 

 Independent variable = Dose 

 Data are assumed to be distributed: normally 

 Variance Model: exp(lnalpha +rho *ln(Y[dose])) 

 The variance is to be modeled as Var(i) = exp(lalpha + log(mean(i)) * rho) 

 Total number of dose groups = 4 

 Total number of records with missing values = 0 

 Maximum number of iterations = 250 

 Relative Function Convergence has been set to: 1e-008 

 Parameter Convergence has been set to: 1e-008 

 MLE solution provided: Exact 

 Initial Parameter Values 

 Variable Model 2 Model 3 Model 4 Model 5 

 -------- ------- ------- ------- ------- 
Page 324 of 438 

 lnalpha -1.73281 -1.73281 -1.73281 -1.73281 

 rho -0.426944 -0.426944 -0.426944 -0.426944 

 a 3.19814 3.19814 11.235 11.235 

 b 1.39146 1.39146 3.6419 3.6419 

 c -- -- 0.106809 0.106809 

 d -- 1 -- 1 

 Parameter Estimates by Model 

 Variable Model 2 Model 3 Model 4 Model 5 

 -------- ------- ------- ------- ------- 

 lnalpha -0.282356 -0.282357 -2.69836 -2.69836 

 rho -0.197562 -0.197562 0.900588 0.900588 

 a 9.95502 9.95502 9.97461 9.97461 

 b 3.51045 3.51045 4.39481 4.39481 

 c -- -- 0.126785 0.126785 

 d -- 1 -- 1 

 Table of Stats From Input Data 

 Dose N Obs Mean Obs Std Dev 

 ----- --- ---------- ------------- 

 0 6 10.7 0.13 

 0.02 8 8.63 0.58 

 0.25 7 4.26 0.29 

 1.42 5 1.26 0.38 

 Estimated Values of Interest 

 Model Dose Est Mean Est Std Scaled Residual 

 ------- ------ ---------- --------- ---------------- 

 2 0 9.955 0.692 2.637 

 0.02 9.28 0.6968 -2.639 

 0.25 4.139 0.7547 0.4241 

 1.42 0.0681 1.132 2.354 

 3 0 9.955 0.692 2.637 

 0.02 9.28 0.6968 -2.639 

 0.25 4.139 0.7547 0.4241 

 1.42 0.0681 1.132 2.354 

 4 0 9.975 0.7309 2.431 

 0.02 9.242 0.7062 -2.45 

 0.25 4.168 0.4934 0.495 
Page 325 of 438 

 1.42 1.282 0.2901 -0.1665 

 5 0 9.975 0.7309 2.431 

 0.02 9.242 0.7062 -2.45 

 0.25 4.168 0.4934 0.495 

 1.42 1.282 0.2901 -0.1665 

 Other models for which likelihoods are calculated: 

 Model A1: Yij = Mu(i) + e(ij) 

 Var{e(ij)} = Sigma^2 

 Model A2: Yij = Mu(i) + e(ij) 

 Var{e(ij)} = Sigma(i)^2 

 Model A3: Yij = Mu(i) + e(ij) 

 Var{e(ij)} = exp(lalpha + log(mean(i)) * rho) 

 Model R: Yij = Mu + e(i) 

 Var{e(ij)} = Sigma^2 

 Likelihoods of Interest 

 Model Log(likelihood) DF AIC 

 ------- ----------------- ---- ------------ 

 A1 12.98972 5 -15.97945 

 A2 19.28066 8 -22.56132 

 A3 13.10721 6 -14.21441 

 R -45.55398 2 95.10797 

 2 -6.55156 4 21.10312 

 3 -6.55156 4 21.10312 

 4 2.796104 5 4.407792 

 5 2.796104 5 4.407792 

 Additive constant for all log-likelihoods = -23.89. This constant added to the 

 above values gives the log-likelihood including the term that does not 

 depend on the model parameters. 

 Explanation of Tests 

 Test 1: Does response and/or variances differ among Dose levels? (A2 vs. R) 

 Test 2: Are Variances Homogeneous? (A2 vs. A1) 
Page 326 of 438 

 Test 3: Are variances adequately modeled? (A2 vs. A3) 

 Test 4: Does Model 2 fit the data? (A3 vs. 2) 

 Test 5a: Does Model 3 fit the data? (A3 vs 3) 

 Test 5b: Is Model 3 better than Model 2? (3 vs. 2) 

 Test 6a: Does Model 4 fit the data? (A3 vs 4) 

 Test 6b: Is Model 4 better than Model 2? (4 vs. 2) 

 Test 7a: Does Model 5 fit the data? (A3 vs 5) 

 Test 7b: Is Model 5 better than Model 3? (5 vs. 3) 

 Test 7c: Is Model 5 better than Model 4? (5 vs. 4) 

 Tests of Interest 

 Test -2*log(Likelihood Ratio) D. F. p-value 

 -------- ------------------------ ------ -------------- 

 Test 1 129.7 6 < 0.0001 

 Test 2 12.58 3 0.005634 

 Test 3 12.35 2 0.002084 

 Test 4 39.32 2 < 0.0001 

 Test 5a 39.32 2 < 0.0001 

 Test 5b -3.245e-011 0 N/A 

 Test 6a 20.62 1 < 0.0001 

 Test 6b 18.7 1 < 0.0001 

 Test 7a 20.62 1 < 0.0001 

 Test 7b 18.7 1 < 0.0001 

 Test 7c -2.723e-012 0 N/A 

 The p-value for Test 1 is less than .05. There appears to be a 

 difference between response and/or variances among the dose 

 levels, it seems appropriate to model the data. 

 The p-value for Test 2 is less than .1. A non-homogeneous 

 variance model appears to be appropriate. 

 The p-value for Test 3 is less than .1. You may want to 

 consider a different variance model. 

 The p-value for Test 4 is less than .1. Model 2 may not adequately 

 describe the data; you may want to consider another model. 

 The p-value for Test 5a is less than .1. Model 3 may not adequately 

 describe the data; you may want to consider another model. 
Page 327 of 438 

 Degrees of freedom for Test 5b are less than or equal to 0. 

 The Chi-Square test for fit is not valid. 

 The p-value for Test 6a is less than .1. Model 4 may not adequately 

 describe the data; you may want to consider another model. 

 The p-value for Test 6b is less than .05. Model 4 appears 

 to fit the data better than Model 2. 

 The p-value for Test 7a is less than .1. Model 5 may not adequately 

 describe the data; you may want to consider another model. 

 The p-value for Test 7b is less than .05. Model 5 appears 

 to fit the data better than Model 3. 

 Degrees of freedom for Test 7c are less than or equal to 0. 

 The Chi-Square test for fit is not valid. 

 Benchmark Dose Computations: 

 Specified Effect = 0.100000 

 Risk Type = Relative deviation 

 Confidence Level = 0.950000 

 BMD and BMDL by Model 

 Model BMD BMDL 

 ------- ------------ ---------- 

 2 0.0300134 0.0259369 

 3 0.0300134 0.0259369 

 4 0.0276746 0.0247364 

 5 0.0276746 0.0247364 
Page 328 of 438 

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11:32 09/28 2012

BMDLBMD

 

Exponential

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Exponential Model 3 with 0.95 Confidence Level

11:32 09/28 2012

BMDLBMD

 

Exponential
Page 329 of 438 

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Exponential Model 4 with 0.95 Confidence Level

11:32 09/28 2012

BMDLBMD

 

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Exponential
Page 330 of 438 

MRID 44527802 - Chronic Toxicity in Rats (2 year) – Female Adult 371D of dosing - Brain 

 ==================================================================== 

 Exponential Model. (Version: 1.7; Date: 12/10/2009) 

 Input Data File: F:/dicrotophos/exp_Dicrotophos Brain F 371D NHV_Setting.(d) 

 Gnuplot Plotting File: 

 Fri Sep 28 11:46:05 2012 

 ==================================================================== 

 BMDS Model Run 

~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ 

 

 The form of the response function by Model: 

 Model 2: Y[dose] = a * exp{sign * b * dose} 

 Model 3: Y[dose] = a * exp{sign * (b * dose)^d} 

 Model 4: Y[dose] = a * [c-(c-1) * exp{-b * dose}] 

 Model 5: Y[dose] = a * [c-(c-1) * exp{-(b * dose)^d}] 

 Note: Y[dose] is the median response for exposure = dose; 

 sign = +1 for increasing trend in data; 

 sign = -1 for decreasing trend. 

 Model 2 is nested within Models 3 and 4. 

 Model 3 is nested within Model 5. 

 Model 4 is nested within Model 5. 

 Dependent variable = Mean 

 Independent variable = Dose 

 Data are assumed to be distributed: normally 

 Variance Model: exp(lnalpha +rho *ln(Y[dose])) 

 The variance is to be modeled as Var(i) = exp(lalpha + log(mean(i)) * rho) 

 Total number of dose groups = 4 

 Total number of records with missing values = 0 

 Maximum number of iterations = 250 

 Relative Function Convergence has been set to: 1e-008 

 Parameter Convergence has been set to: 1e-008 

 MLE solution provided: Exact 

 Initial Parameter Values 

 Variable Model 2 Model 3 Model 4 Model 5 
Page 331 of 438 

 -------- ------- ------- ------- ------- 

 lnalpha -2.23363 -2.23363 -2.23363 -2.23363 

 rho 1.2928 1.2928 1.2928 1.2928 

 a 3.16268 3.16268 11.9595 11.9595 

 b 1.10318 1.10318 2.92122 2.92122 

 c -- -- 0.11308 0.11308 

 d -- 1 -- 1 

 Parameter Estimates by Model 

 Variable Model 2 Model 3 Model 4 Model 5 

 -------- ------- ------- ------- ------- 

 lnalpha -3.01945 -2.23363 -2.5126 -2.5126 

 rho 2.17165 1.2928 1.74029 1.74029 

 a 9.30288 3.16268 10.2893 10.2893 

 b 1.1014 1.10318 2.55838 2.55838 

 c -- -- 0.124037 0.124037 

 d -- 1 -- 1 

 Table of Stats From Input Data 

 Dose N Obs Mean Obs Std Dev 

 ----- --- ---------- ------------- 

 0 8 11.39 0.8 

 0.03 8 8.78 1.77 

 0.32 8 5.03 1.89 

 1.74 7 1.42 0.3 

 Estimated Values of Interest 

 Model Dose Est Mean Est Std Scaled Residual 

 ------- ------ ---------- --------- ---------------- 

 2 0 9.303 2.489 2.371 

 0.03 9.001 2.402 -0.2597 

 0.32 6.54 1.698 -2.515 

 1.74 1.369 0.3107 0.4364 

 3 0 0 0 1.#IO 

 0.03 0 0 1.#IO 

 0.32 0 0 1.#IO 

 1.74 0 0 1.#IO 

 4 0 10.29 2.164 1.438 

 0.03 9.623 2.042 -1.168 
Page 332 of 438 

 0.32 5.251 1.205 -0.5189 

 1.74 1.381 0.3771 0.2711 

 5 0 10.29 2.164 1.438 

 0.03 9.623 2.042 -1.168 

 0.32 5.251 1.205 -0.5189 

 1.74 1.381 0.3771 0.2711 

 Other models for which likelihoods are calculated: 

 Model A1: Yij = Mu(i) + e(ij) 

 Var{e(ij)} = Sigma^2 

 Model A2: Yij = Mu(i) + e(ij) 

 Var{e(ij)} = Sigma(i)^2 

 Model A3: Yij = Mu(i) + e(ij) 

 Var{e(ij)} = exp(lalpha + log(mean(i)) * rho) 

 Model R: Yij = Mu + e(i) 

 Var{e(ij)} = Sigma^2 

 Likelihoods of Interest 

 Model Log(likelihood) DF AIC 

 ------- ----------------- ---- ------------ 

 A1 -23.50406 5 57.00811 

 A2 -12.80559 8 41.61118 

 A3 -20.49633 6 52.99266 

 R -57.95135 2 119.9027 

 2 -25.85755 4 59.71509 

 3 -1098.597 4 2205.195 

 4 -22.05583 5 54.11166 

 5 -22.05583 5 54.11166 

 Additive constant for all log-likelihoods = -28.49. This constant added to the 

 above values gives the log-likelihood including the term that does not 

 depend on the model parameters. 

 Explanation of Tests 

 Test 1: Does response and/or variances differ among Dose levels? (A2 vs. R) 
Page 333 of 438 

 Test 2: Are Variances Homogeneous? (A2 vs. A1) 

 Test 3: Are variances adequately modeled? (A2 vs. A3) 

 Test 4: Does Model 2 fit the data? (A3 vs. 2) 

 Test 5a: Does Model 3 fit the data? (A3 vs 3) 

 Test 5b: Is Model 3 better than Model 2? (3 vs. 2) 

 Test 6a: Does Model 4 fit the data? (A3 vs 4) 

 Test 6b: Is Model 4 better than Model 2? (4 vs. 2) 

 Test 7a: Does Model 5 fit the data? (A3 vs 5) 

 Test 7b: Is Model 5 better than Model 3? (5 vs. 3) 

 Test 7c: Is Model 5 better than Model 4? (5 vs. 4) 

 Tests of Interest 

 Test -2*log(Likelihood Ratio) D. F. p-value 

 -------- ------------------------ ------ -------------- 

 Test 1 90.29 6 < 0.0001 

 Test 2 21.4 3 < 0.0001 

 Test 3 15.38 2 0.000457 

 Test 4 10.72 2 0.004695 

 Test 5a 2156 2 < 0.0001 

 Test 5b -2145 0 N/A 

 Test 6a 3.119 1 0.07738 

 Test 6b 7.603 1 0.005826 

 Test 7a 3.119 1 0.07738 

 Test 7b 2153 1 < 0.0001 

 Test 7c 7.105e-015 0 N/A 

 The p-value for Test 1 is less than .05. There appears to be a 

 difference between response and/or variances among the dose 

 levels, it seems appropriate to model the data. 

 The p-value for Test 2 is less than .1. A non-homogeneous 

 variance model appears to be appropriate. 

 The p-value for Test 3 is less than .1. You may want to 

 consider a different variance model. 

 The p-value for Test 4 is less than .1. Model 2 may not adequately 

 describe the data; you may want to consider another model. 

 The p-value for Test 5a is less than .1. Model 3 may not adequately 
Page 334 of 438 

 describe the data; you may want to consider another model. 

 Degrees of freedom for Test 5b are less than or equal to 0. 

 The Chi-Square test for fit is not valid. 

 The p-value for Test 6a is less than .1. Model 4 may not adequately 

 describe the data; you may want to consider another model. 

 The p-value for Test 6b is less than .05. Model 4 appears 

 to fit the data better than Model 2. 

 The p-value for Test 7a is less than .1. Model 5 may not adequately 

 describe the data; you may want to consider another model. 

 The p-value for Test 7b is less than .05. Model 5 appears 

 to fit the data better than Model 3. 

 Degrees of freedom for Test 7c are less than or equal to 0. 

 The Chi-Square test for fit is not valid. 

 Benchmark Dose Computations: 

 Specified Effect = 0.100000 

 Risk Type = Relative deviation 

 Confidence Level = 0.950000 

 BMD and BMDL by Model 

 Model BMD BMDL 

 ------- ------------ ---------- 

 2 0.0956609 0.084013 

 3 0.0955059 Bad completion 

 4 0.0473812 0.0369167 

 5 0.0473812 0.0369167 
Page 335 of 438 

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BMDL BMD

 

Exponential

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Page 336 of 438 

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Exponential
Page 337 of 438 

MRID 44527802 - Chronic Toxicity in Rats (2 year) – Male Adult 700D of dosing - Brain

 ==================================================================== 

 Exponential Model. (Version: 1.7; Date: 12/10/2009) 

 Input Data File: F:/dicrotophos/exp_Dicrotophos Brain M 700D_Setting.(d) 

 Gnuplot Plotting File: 

 Thu Aug 16 15:31:49 2012 

 ==================================================================== 

 BMDS Model Run 

~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ 

 

 The form of the response function by Model: 

 Model 2: Y[dose] = a * exp{sign * b * dose} 

 Model 3: Y[dose] = a * exp{sign * (b * dose)^d} 

 Model 4: Y[dose] = a * [c-(c-1) * exp{-b * dose}] 

 Model 5: Y[dose] = a * [c-(c-1) * exp{-(b * dose)^d}] 

 Note: Y[dose] is the median response for exposure = dose; 

 sign = +1 for increasing trend in data; 

 sign = -1 for decreasing trend. 

 Model 2 is nested within Models 3 and 4. 

 Model 3 is nested within Model 5. 

 Model 4 is nested within Model 5. 

 Dependent variable = Mean 

 Independent variable = Dose 

 Data are assumed to be distributed: normally 

 Variance Model: exp(lnalpha +rho *ln(Y[dose])) 

 rho is set to 0. 

 A constant variance model is fit. 

 Total number of dose groups = 3 

 Total number of records with missing values = 0 

 Maximum number of iterations = 250 

 Relative Function Convergence has been set to: 1e-008 

 Parameter Convergence has been set to: 1e-008 

 MLE solution provided: Exact 

 Initial Parameter Values 
Page 338 of 438 

 Variable Model 2 Model 3 Model 4 Model 5 

 -------- ------- ------- ------- ------- 

 lnalpha -1.33784 -1.33784 -1.33784 NC 

 rho(S) 0 0 0 NC 

 a 5.90295 5.90295 9.3765 NC 

 b 3.83083 3.83083 14.5936 NC 

 c -- -- 0.342294 NC 

 d -- 1 -- NC 

 (S) = Specified 

 Parameter Estimates by Model 

 Variable Model 2 Model 3 Model 4 Model 5 

 -------- ------- ------- ------- ------- 

 lnalpha -1.20104 -1.20104 -1.33784 NC 

 rho 0 0 0 NC 

 a 8.76465 8.76465 8.93 NC 

 b 3.94725 3.94725 9.89295 NC 

 c -- -- 0.320052 NC 

 d -- 1 -- NC 

 NC = No Convergence 

 Table of Stats From Input Data 

 Dose N Obs Mean Obs Std Dev 

 ----- --- ---------- ------------- 

 0 8 8.93 0.64 

 0.02 6 7.84 0.48 

 0.25 3 3.37 0.47 

 Estimated Values of Interest 

 Model Dose Est Mean Est Std Scaled Residual 

 ------- ------ ---------- --------- ---------------- 

 2 0 8.765 0.5485 0.8526 

 0.02 8.099 0.5485 -1.158 

 0.25 3.267 0.5485 0.3248 

 3 0 8.765 0.5485 0.8526 

 0.02 8.099 0.5485 -1.158 

 0.25 3.267 0.5485 0.3248 
Page 339 of 438 

 4 0 8.93 0.5123 -5.119e-008 

 0.02 7.84 0.5123 -9.392e-008 

 0.25 3.37 0.5123 8.996e-008 

 Other models for which likelihoods are calculated: 

 Model A1: Yij = Mu(i) + e(ij) 

 Var{e(ij)} = Sigma^2 

 Model A2: Yij = Mu(i) + e(ij) 

 Var{e(ij)} = Sigma(i)^2 

 Model A3: Yij = Mu(i) + e(ij) 

 Var{e(ij)} = exp(lalpha + log(mean(i)) * rho) 

 Model R: Yij = Mu + e(i) 

 Var{e(ij)} = Sigma^2 

 Likelihoods of Interest 

 Model Log(likelihood) DF AIC 

 ------- ----------------- ---- ------------ 

 A1 2.871643 4 2.256713 

 A2 3.428468 6 5.143065 

 A3 2.871643 4 2.256713 

 R -20.8416 2 45.6832 

 2 1.708829 3 2.582342 

 3 1.708829 3 2.582342 

 4 2.871643 4 2.256713 

 Additive constant for all log-likelihoods = -15.62. This constant added to the 

 above values gives the log-likelihood including the term that does not 

 depend on the model parameters. 

 Explanation of Tests 

 Test 1: Does response and/or variances differ among Dose levels? (A2 vs. R) 

 Test 2: Are Variances Homogeneous? (A2 vs. A1) 

 Test 3: Are variances adequately modeled? (A2 vs. A3) 

 Test 4: Does Model 2 fit the data? (A3 vs. 2) 
Page 340 of 438 

 Test 5a: Does Model 3 fit the data? (A3 vs 3) 

 Test 5b: Is Model 3 better than Model 2? (3 vs. 2) 

 Test 6a: Does Model 4 fit the data? (A3 vs 4) 

 Test 6b: Is Model 4 better than Model 2? (4 vs. 2) 

 Tests of Interest 

 Test -2*log(Likelihood Ratio) D. F. p-value 

 -------- ------------------------ ------ -------------- 

 Test 1 48.54 4 < 0.0001 

 Test 2 1.114 2 0.573 

 Test 3 1.114 2 0.573 

 Test 4 2.326 1 0.1273 

 Test 5a 2.326 1 0.1273 

 Test 5b -3.759e-012 0 N/A 

 Test 6a 2.576e-014 0 N/A 

 Test 6b 2.326 1 0.1273 

 The p-value for Test 1 is less than .05. There appears to be a 

 difference between response and/or variances among the dose 

 levels, it seems appropriate to model the data. 

 The p-value for Test 2 is greater than .1. A homogeneous 

 variance model appears to be appropriate here. 

 The p-value for Test 3 is greater than .1. The modeled 

 variance appears to be appropriate here. 

 The p-value for Test 4 is greater than .1. Model 2 seems 

 to adequately describe the data. 

 The p-value for Test 5a is greater than .1. Model 3 seems 

 to adequately describe the data. 

 Degrees of freedom for Test 5b are less than or equal to 0. 

 The Chi-Square test for fit is not valid. 

 Degrees of freedom for Test 6a are less than or equal to 0. 

 The Chi-Square test for fit is not valid. 

 The p-value for Test 6b is greater than .05. Model 4 does 

 not seem to fit the data better than Model 2. 
Page 341 of 438 

 Benchmark Dose Computations: 

 Specified Effect = 0.100000 

 Risk Type = Relative deviation 

 Confidence Level = 0.950000 

 BMD and BMDL by Model 

 Model BMD BMDL 

 ------- ------------ ---------- 

 2 0.0266922 0.0224428 

 3 0.0266922 0.0224428 

 4 0.0160799 0.0109029 

 5 0 0 Not computed 
Page 342 of 438 

44527802 Male Brain 700 Days 

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Exponential

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Exponential
Page 343 of 438 

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BMDL BMD

 

Exponential
Page 344 of 438 

MRID 44527802 - Chronic Toxicity in Rats (2 year) – Female Adult 735D of dosing - Brain

==================================================================== 

 Exponential Model. (Version: 1.7; Date: 12/10/2009) 

 Input Data File: F:/dicrotophos/exp_Dicrotophos Brain F 735D NHV_Setting.(d) 

 Gnuplot Plotting File: 

 Fri Sep 28 11:54:25 2012 

 ==================================================================== 

 BMDS Model Run 

~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ 

 

 The form of the response function by Model: 

 Model 2: Y[dose] = a * exp{sign * b * dose} 

 Model 3: Y[dose] = a * exp{sign * (b * dose)^d} 

 Model 4: Y[dose] = a * [c-(c-1) * exp{-b * dose}] 

 Model 5: Y[dose] = a * [c-(c-1) * exp{-(b * dose)^d}] 

 Note: Y[dose] is the median response for exposure = dose; 

 sign = +1 for increasing trend in data; 

 sign = -1 for decreasing trend. 

 Model 2 is nested within Models 3 and 4. 

 Model 3 is nested within Model 5. 

 Model 4 is nested within Model 5. 

 Dependent variable = Mean 

 Independent variable = Dose 

 Data are assumed to be distributed: normally 

 Variance Model: exp(lnalpha +rho *ln(Y[dose])) 

 The variance is to be modeled as Var(i) = exp(lalpha + log(mean(i)) * rho) 

 Total number of dose groups = 4 

 Total number of records with missing values = 0 

 Maximum number of iterations = 250 

 Relative Function Convergence has been set to: 1e-008 

 Parameter Convergence has been set to: 1e-008 

 MLE solution provided: Exact 

 Initial Parameter Values 

 Variable Model 2 Model 3 Model 4 Model 5 

 -------- ------- ------- ------- ------- 
Page 345 of 438 

 lnalpha -2.69825 -2.69825 -2.69825 -2.69825 

 rho 1.19931 1.19931 1.19931 1.19931 

 a 2.13771 2.13771 8.7885 8.7885 

 b 1.29137 1.29137 3.09918 3.09918 

 c -- -- 0.084526 0.084526 

 d -- 1 -- 1 

 Parameter Estimates by Model 

 Variable Model 2 Model 3 Model 4 Model 5 

 -------- ------- ------- ------- ------- 

 lnalpha -0.326076 -2.69825 -2.25807 -2.25807 

 rho 0.103578 1.19931 1.28449 1.28449 

 a 7.92961 2.13771 7.87151 7.87151 

 b 2.48182 1.29137 2.75747 2.75747 

 c -- -- 0.0867278 0.0867278 

 d -- 1 -- 1 

 Table of Stats From Input Data 

 Dose N Obs Mean Obs Std Dev 

 ----- --- ---------- ------------- 

 0 8 8.37 0.7 

 0.03 7 6.83 0.63 

 0.32 8 3.55 1.26 

 1.74 6 0.78 0.17 

 Estimated Values of Interest 

 Model Dose Est Mean Est Std Scaled Residual 

 ------- ------ ---------- --------- ---------------- 

 2 0 7.93 0.9457 1.317 

 0.03 7.361 0.9421 -1.49 

 0.32 3.584 0.9076 -0.1053 

 1.74 0.1056 0.7562 2.184 

 3 0 0 0 1.#IO 

 0.03 0 0 1.#IO 

 0.32 0 0 1.#IO 

 1.74 0 0 1.#IO 

 4 0 7.872 1.217 1.159 

 0.03 7.301 1.159 -1.074 

 0.32 3.657 0.7436 -0.4084 
Page 346 of 438 

 1.74 0.742 0.2669 0.3491 

 5 0 7.872 1.217 1.159 

 0.03 7.301 1.159 -1.074 

 0.32 3.657 0.7436 -0.4084 

 1.74 0.742 0.2669 0.3491 

 Other models for which likelihoods are calculated: 

 Model A1: Yij = Mu(i) + e(ij) 

 Var{e(ij)} = Sigma^2 

 Model A2: Yij = Mu(i) + e(ij) 

 Var{e(ij)} = Sigma(i)^2 

 Model A3: Yij = Mu(i) + e(ij) 

 Var{e(ij)} = exp(lalpha + log(mean(i)) * rho) 

 Model R: Yij = Mu + e(i) 

 Var{e(ij)} = Sigma^2 

 Likelihoods of Interest 

 Model Log(likelihood) DF AIC 

 ------- ----------------- ---- ------------ 

 A1 -6.814623 5 23.62925 

 A2 2.525238 8 10.94952 

 A3 -5.369442 6 22.73888 

 R -46.0087 2 96.01741 

 2 -11.18381 4 30.36762 

 3 -1729.478 4 3466.955 

 4 -6.80877 5 23.61754 

 5 -6.80877 5 23.61754 

 Additive constant for all log-likelihoods = -26.65. This constant added to the 

 above values gives the log-likelihood including the term that does not 

 depend on the model parameters. 

 Explanation of Tests 

 Test 1: Does response and/or variances differ among Dose levels? (A2 vs. R) 

 Test 2: Are Variances Homogeneous? (A2 vs. A1) 
Page 347 of 438 

 Test 3: Are variances adequately modeled? (A2 vs. A3) 

 Test 4: Does Model 2 fit the data? (A3 vs. 2) 

 Test 5a: Does Model 3 fit the data? (A3 vs 3) 

 Test 5b: Is Model 3 better than Model 2? (3 vs. 2) 

 Test 6a: Does Model 4 fit the data? (A3 vs 4) 

 Test 6b: Is Model 4 better than Model 2? (4 vs. 2) 

 Test 7a: Does Model 5 fit the data? (A3 vs 5) 

 Test 7b: Is Model 5 better than Model 3? (5 vs. 3) 

 Test 7c: Is Model 5 better than Model 4? (5 vs. 4) 

 Tests of Interest 

 Test -2*log(Likelihood Ratio) D. F. p-value 

 -------- ------------------------ ------ -------------- 

 Test 1 97.07 6 < 0.0001 

 Test 2 18.68 3 0.0003184 

 Test 3 15.79 2 0.0003727 

 Test 4 11.63 2 0.002984 

 Test 5a 3448 2 < 0.0001 

 Test 5b -3437 0 N/A 

 Test 6a 2.879 1 0.08976 

 Test 6b 8.75 1 0.003096 

 Test 7a 2.879 1 0.08976 

 Test 7b 3445 1 < 0.0001 

 Test 7c -8.882e-015 0 N/A 

 The p-value for Test 1 is less than .05. There appears to be a 

 difference between response and/or variances among the dose 

 levels, it seems appropriate to model the data. 

 The p-value for Test 2 is less than .1. A non-homogeneous 

 variance model appears to be appropriate. 

 The p-value for Test 3 is less than .1. You may want to 

 consider a different variance model. 

 The p-value for Test 4 is less than .1. Model 2 may not adequately 

 describe the data; you may want to consider another model. 

 The p-value for Test 5a is less than .1. Model 3 may not adequately 

 describe the data; you may want to consider another model. 
Page 348 of 438 

 Degrees of freedom for Test 5b are less than or equal to 0. 

 The Chi-Square test for fit is not valid. 

 The p-value for Test 6a is less than .1. Model 4 may not adequately 

 describe the data; you may want to consider another model. 

 The p-value for Test 6b is less than .05. Model 4 appears 

 to fit the data better than Model 2. 

 The p-value for Test 7a is less than .1. Model 5 may not adequately 

 describe the data; you may want to consider another model. 

 The p-value for Test 7b is less than .05. Model 5 appears 

 to fit the data better than Model 3. 

 Degrees of freedom for Test 7c are less than or equal to 0. 

 The Chi-Square test for fit is not valid. 

 Benchmark Dose Computations: 

 Specified Effect = 0.100000 

 Risk Type = Relative deviation 

 Confidence Level = 0.950000 

 BMD and BMDL by Model 

 Model BMD BMDL 

 ------- ------------ ---------- 

 2 0.0424529 0.0340801 

 3 0.0815883 Bad completion 

 4 0.042056 0.0345724 

 5 0.042056 0.0345724 

 
Page 349 of 438 

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Exponential Model 2 with 0.95 Confidence Level

11:54 09/28 2012

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Exponential

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Page 350 of 438 

 

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Exponential Model 4 with 0.95 Confidence Level

11:54 09/28 2012

BMDL BMD

 

Exponential

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Exponential Model 5 with 0.95 Confidence Level

11:54 09/28 2012

BMDLBMD

 

Exponential
Page 351 of 438 

MRID 44527802 - Chronic Toxicity in Rats (2 year) – Male Adult 371D of dosing – RBC – 

dropped high dose for this analysis 

 ==================================================================== 

 Exponential Model. (Version: 1.7; Date: 12/10/2009) 

 Input Data File: F:/dicrotophos/exp_Dicrotophos RBC M 371D drop HD 

NHV_Setting.(d) 

 Gnuplot Plotting File: 

 Fri Sep 28 12:35:39 2012 

 ==================================================================== 

 BMDS Model Run 

~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ 

 

 The form of the response function by Model: 

 Model 2: Y[dose] = a * exp{sign * b * dose} 

 Model 3: Y[dose] = a * exp{sign * (b * dose)^d} 

 Model 4: Y[dose] = a * [c-(c-1) * exp{-b * dose}] 

 Model 5: Y[dose] = a * [c-(c-1) * exp{-(b * dose)^d}] 

 Note: Y[dose] is the median response for exposure = dose; 

 sign = +1 for increasing trend in data; 

 sign = -1 for decreasing trend. 

 Model 2 is nested within Models 3 and 4. 

 Model 3 is nested within Model 5. 

 Model 4 is nested within Model 5. 

 Dependent variable = Mean 

 Independent variable = Dose 

 Data are assumed to be distributed: normally 

 Variance Model: exp(lnalpha +rho *ln(Y[dose])) 

 The variance is to be modeled as Var(i) = exp(lalpha + log(mean(i)) * rho) 

 Total number of dose groups = 3 

 Total number of records with missing values = 0 

 Maximum number of iterations = 250 

 Relative Function Convergence has been set to: 1e-008 

 Parameter Convergence has been set to: 1e-008 

 MLE solution provided: Exact 

 Initial Parameter Values 
Page 352 of 438 

 Variable Model 2 Model 3 Model 4 Model 5 

 -------- ------- ------- ------- ------- 

 lnalpha 15.4749 15.4749 15.4749 NC 

 rho -0.706578 -0.706578 -0.706578 NC 

 a 1508.63 53.5272 2674.35 NC 

 b 2.68357 -1.46949 12.8291 NC 

 c -- -- 0.448707 NC 

 d -- 2 -- NC 

 Parameter Estimates by Model 

 Variable Model 2 Model 3 Model 4 Model 5 

 -------- ------- ------- ------- ------- 

 lnalpha 5.45303 15.4749 8.74185 NC 

 rho 0.659525 -0.706578 0.211036 NC 

 a 2462.72 53.5272 2545.95 NC 

 b 2.7058 -1.46949 11.6043 NC 

 c -- -- 0.465397 NC 

 d -- 2 -- NC 

 NC = No Convergence 

 Table of Stats From Input Data 

 Dose N Obs Mean Obs Std Dev 

 ----- --- ---------- ------------- 

 0 6 2547 94 

 0.02 8 2263 249 

 0.25 7 1260 169 

 Estimated Values of Interest 

 Model Dose Est Mean Est Std Scaled Residual 

 ------- ------ ---------- --------- ---------------- 

 2 0 2463 200.7 1.029 

 0.02 2333 197.1 -1.004 

 0.25 1252 160.5 0.1303 

 3 0 0 1.98e+112 3.151e-109 

 0.02 0 1.98e+112 3.233e-109 

 0.25 0 1.98e+112 1.684e-109 

 4 0 2546 181 0.01424 

 0.02 2264 178.8 -0.01644 
Page 353 of 438 

 0.25 1260 168 0.004953 

 Other models for which likelihoods are calculated: 

 Model A1: Yij = Mu(i) + e(ij) 

 Var{e(ij)} = Sigma^2 

 Model A2: Yij = Mu(i) + e(ij) 

 Var{e(ij)} = Sigma(i)^2 

 Model A3: Yij = Mu(i) + e(ij) 

 Var{e(ij)} = exp(lalpha + log(mean(i)) * rho) 

 Model R: Yij = Mu + e(i) 

 Var{e(ij)} = Sigma^2 

 Likelihoods of Interest 

 Model Log(likelihood) DF AIC 

 ------- ----------------- ---- ------------ 

 A1 -119.0649 4 246.1299 

 A2 -116.1881 6 244.3761 

 A3 -119.0482 5 248.0964 

 R -143.7683 2 291.5365 

 2 -120.1301 4 248.2601 

 3 -270.0204 5 550.0409 

 4 -119.0482 5 248.0964 

 Additive constant for all log-likelihoods = -19.3. This constant added to the 

 above values gives the log-likelihood including the term that does not 

 depend on the model parameters. 

 Explanation of Tests 

 Test 1: Does response and/or variances differ among Dose levels? (A2 vs. R) 

 Test 2: Are Variances Homogeneous? (A2 vs. A1) 

 Test 3: Are variances adequately modeled? (A2 vs. A3) 

 Test 4: Does Model 2 fit the data? (A3 vs. 2) 

 Test 5a: Does Model 3 fit the data? (A3 vs 3) 

 Test 5b: Is Model 3 better than Model 2? (3 vs. 2) 
Page 354 of 438 

 Test 6a: Does Model 4 fit the data? (A3 vs 4) 

 Test 6b: Is Model 4 better than Model 2? (4 vs. 2) 

 Tests of Interest 

 Test -2*log(Likelihood Ratio) D. F. p-value 

 -------- ------------------------ ------ -------------- 

 Test 1 55.16 4 < 0.0001 

 Test 2 5.754 2 0.05631 

 Test 3 5.72 1 0.01677 

 Test 4 2.164 1 0.1413 

 Test 5a 301.9 0 N/A 

 Test 5b -299.8 1 N/A 

 Test 6a -1.596e-010 0 N/A 

 Test 6b 2.164 1 0.1413 

 The p-value for Test 1 is less than .05. There appears to be a 

 difference between response and/or variances among the dose 

 levels, it seems appropriate to model the data. 

 The p-value for Test 2 is less than .1. A non-homogeneous 

 variance model appears to be appropriate. 

 The p-value for Test 3 is less than .1. You may want to 

 consider a different variance model. 

 The p-value for Test 4 is greater than .1. Model 2 seems 

 to adequately describe the data. 

 Degrees of freedom for Test 5a are less than or equal to 0. 

 The Chi-Square test for fit is not valid. 

 The p-value for Test 5b is less than .05. Model 3 appears 

 to fit the data better than Model 2. 

 Degrees of freedom for Test 6a are less than or equal to 0. 

 The Chi-Square test for fit is not valid. 

 The p-value for Test 6b is greater than .05. Model 4 does 

 not seem to fit the data better than Model 2. 

 Benchmark Dose Computations: 
Page 355 of 438 

 Specified Effect = 0.100000 

 Risk Type = Relative deviation 

 Confidence Level = 0.950000 

 BMD and BMDL by Model 

 Model BMD BMDL 

 ------- ------------ ---------- 

 2 0.0389388 0.0336212 

 3 -0.220888 Bad completion 

 4 0.0178461 0.010819 

 5 0 0 Not computed 

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Mean Response

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Exponential Model 2 with 0.95 Confidence Level

12:35 09/28 2012

BMDL BMD

 

Exponential
Page 356 of 438 

 

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Mean Response

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Exponential Model 3 with 0.95 Confidence Level

12:35 09/28 2012

BMDL BMD

 

Exponential

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Exponential Model 4 with 0.95 Confidence Level

12:35 09/28 2012

BMDL BMD

 

Exponential
Page 357 of 438 

MRID 44527802 - Chronic Toxicity in Rats (2 year) – Female Adult 371D of dosing – RBC- 

 ==================================================================== 

 Exponential Model. (Version: 1.7; Date: 12/10/2009) 

 Input Data File: F:/dicrotophos/exp_Dicrotophos RBC F 371D NHV_Setting.(d) 

 Gnuplot Plotting File: 

 Fri Sep 28 12:46:41 2012 

 ==================================================================== 

 BMDS Model Run 

~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ 

 

 The form of the response function by Model: 

 Model 2: Y[dose] = a * exp{sign * b * dose} 

 Model 3: Y[dose] = a * exp{sign * (b * dose)^d} 

 Model 4: Y[dose] = a * [c-(c-1) * exp{-b * dose}] 

 Model 5: Y[dose] = a * [c-(c-1) * exp{-(b * dose)^d}] 

 Note: Y[dose] is the median response for exposure = dose; 

 sign = +1 for increasing trend in data; 

 sign = -1 for decreasing trend. 

 Model 2 is nested within Models 3 and 4. 

 Model 3 is nested within Model 5. 

 Model 4 is nested within Model 5. 

 Dependent variable = Mean 

 Independent variable = Dose 

 Data are assumed to be distributed: normally 

 Variance Model: exp(lnalpha +rho *ln(Y[dose])) 

 The variance is to be modeled as Var(i) = exp(lalpha + log(mean(i)) * rho) 

 Total number of dose groups = 4 

 Total number of records with missing values = 0 

 Maximum number of iterations = 250 

 Relative Function Convergence has been set to: 1e-008 

 Parameter Convergence has been set to: 1e-008 

 MLE solution provided: Exact 

 Initial Parameter Values 

 Variable Model 2 Model 3 Model 4 Model 5 

 -------- ------- ------- ------- ------- 
Page 358 of 438 

 lnalpha 14.2165 14.2165 14.2165 14.2165 

 rho -0.445437 -0.445437 -0.445437 -0.445437 

 a 1531.17 9.86089 2812.95 2812.95 

 b 0.39272 -0.742496 2.04011 2.04011 

 c -- -- 0.405268 0.405268 

 d -- 4 -- 1 

 Parameter Estimates by Model 

 Variable Model 2 Model 3 Model 4 Model 5 

 -------- ------- ------- ------- ------- 

 lnalpha -11.619 -11.619 8.07627 8.07627 

 rho 3.12527 3.12527 0.411867 0.411867 

 a 2249.65 2249.65 2609.32 2609.32 

 b 0.378668 0.378668 5.2003 5.2003 

 c -- -- 0.46184 0.46184 

 d -- 1 -- 1 

 Table of Stats From Input Data 

 Dose N Obs Mean Obs Std Dev 

 ----- --- ---------- ------------- 

 0 8 2679 120 

 0.03 8 2321 385 

 0.32 8 1494 337 

 1.74 7 1197 178 

 Estimated Values of Interest 

 Model Dose Est Mean Est Std Scaled Residual 

 ------- ------ ---------- --------- ---------------- 

 2 0 2250 518.9 2.34 

 0.03 2224 509.8 0.5369 

 0.32 1993 429.4 -3.286 

 1.74 1164 185.3 0.4706 

 3 0 2250 518.9 2.34 

 0.03 2224 509.8 0.5368 

 0.32 1993 429.4 -3.286 

 1.74 1164 185.3 0.4706 

 4 0 2609 286.6 0.6876 

 0.03 2406 281.9 -0.8577 

 0.32 1471 254.7 0.2554 
Page 359 of 438 

 1.74 1205 244.5 -0.08933 

 5 0 2609 286.6 0.6876 

 0.03 2406 281.9 -0.8577 

 0.32 1471 254.7 0.2554 

 1.74 1205 244.5 -0.08933 

 Other models for which likelihoods are calculated: 

 Model A1: Yij = Mu(i) + e(ij) 

 Var{e(ij)} = Sigma^2 

 Model A2: Yij = Mu(i) + e(ij) 

 Var{e(ij)} = Sigma(i)^2 

 Model A3: Yij = Mu(i) + e(ij) 

 Var{e(ij)} = exp(lalpha + log(mean(i)) * rho) 

 Model R: Yij = Mu + e(i) 

 Var{e(ij)} = Sigma^2 

 Likelihoods of Interest 

 Model Log(likelihood) DF AIC 

 ------- ----------------- ---- ------------ 

 A1 -188.0859 5 386.1717 

 A2 -182.1171 8 380.2342 

 A3 -188.0761 6 388.1521 

 R -216.2988 2 436.5976 

 2 -200.4398 4 408.8795 

 3 -200.4398 4 408.8795 

 4 -188.7125 5 387.4251 

 5 -188.7125 5 387.4251 

 Additive constant for all log-likelihoods = -28.49. This constant added to the 

 above values gives the log-likelihood including the term that does not 

 depend on the model parameters. 

 Explanation of Tests 

 Test 1: Does response and/or variances differ among Dose levels? (A2 vs. R) 

 Test 2: Are Variances Homogeneous? (A2 vs. A1) 
Page 360 of 438 

 Test 3: Are variances adequately modeled? (A2 vs. A3) 

 Test 4: Does Model 2 fit the data? (A3 vs. 2) 

 Test 5a: Does Model 3 fit the data? (A3 vs 3) 

 Test 5b: Is Model 3 better than Model 2? (3 vs. 2) 

 Test 6a: Does Model 4 fit the data? (A3 vs 4) 

 Test 6b: Is Model 4 better than Model 2? (4 vs. 2) 

 Test 7a: Does Model 5 fit the data? (A3 vs 5) 

 Test 7b: Is Model 5 better than Model 3? (5 vs. 3) 

 Test 7c: Is Model 5 better than Model 4? (5 vs. 4) 

 Tests of Interest 

 Test -2*log(Likelihood Ratio) D. F. p-value 

 -------- ------------------------ ------ -------------- 

 Test 1 68.36 6 < 0.0001 

 Test 2 11.94 3 0.007601 

 Test 3 11.92 2 0.002583 

 Test 4 24.73 2 < 0.0001 

 Test 5a 24.73 2 < 0.0001 

 Test 5b -2.331e-012 0 N/A 

 Test 6a 1.273 1 0.2592 

 Test 6b 23.45 1 < 0.0001 

 Test 7a 1.273 1 0.2592 

 Test 7b 23.45 1 < 0.0001 

 Test 7c 0 0 N/A 

 The p-value for Test 1 is less than .05. There appears to be a 

 difference between response and/or variances among the dose 

 levels, it seems appropriate to model the data. 

 The p-value for Test 2 is less than .1. A non-homogeneous 

 variance model appears to be appropriate. 

 The p-value for Test 3 is less than .1. You may want to 

 consider a different variance model. 

 The p-value for Test 4 is less than .1. Model 2 may not adequately 

 describe the data; you may want to consider another model. 

 The p-value for Test 5a is less than .1. Model 3 may not adequately 

 describe the data; you may want to consider another model. 
Page 361 of 438 

 Degrees of freedom for Test 5b are less than or equal to 0. 

 The Chi-Square test for fit is not valid. 

 The p-value for Test 6a is greater than .1. Model 4 seems 

 to adequately describe the data. 

 The p-value for Test 6b is less than .05. Model 4 appears 

 to fit the data better than Model 2. 

 The p-value for Test 7a is greater than .1. Model 5 seems 

 to adequately describe the data. 

 The p-value for Test 7b is less than .05. Model 5 appears 

 to fit the data better than Model 3. 

 Degrees of freedom for Test 7c are less than or equal to 0. 

 The Chi-Square test for fit is not valid. 

 Benchmark Dose Computations: 

 Specified Effect = 0.100000 

 Risk Type = Relative deviation 

 Confidence Level = 0.950000 

 BMD and BMDL by Model 

 Model BMD BMDL 

 ------- ------------ ---------- 

 2 0.27824 0.213749 

 3 0.27824 0.213749 

 4 0.0395307 0.0159367 

 5 0.0395307 0.0159367 
Page 362 of 438 

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Mean Response

dose

Exponential Model 2 with 0.95 Confidence Level

12:46 09/28 2012

BMDL BMD

 

Exponential

 1000

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Mean Response

dose

Exponential Model 3 with 0.95 Confidence Level

12:46 09/28 2012

BMDL BMD

 

Exponential
Page 363 of 438 

 1000

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Mean Response

dose

Exponential Model 4 with 0.95 Confidence Level

12:46 09/28 2012

BMDL BMD

 

Exponential

 1000

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Mean Response

dose

Exponential Model 5 with 0.95 Confidence Level

12:46 09/28 2012

BMDL BMD

 

Exponential
Page 364 of 438 

MRID 44527802 - Chronic Toxicity in Rats (2 year) – Male Adult 700D of dosing - RBC

 ==================================================================== 

 Exponential Model. (Version: 1.7; Date: 12/10/2009) 

 Input Data File: F:/dicrotophos/exp_Dicrotophos RBC M 700D NHV_Setting.(d) 

 Gnuplot Plotting File: 

 Fri Sep 28 12:41:03 2012 

 ==================================================================== 

 BMDS Model Run 

~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ 

 

 The form of the response function by Model: 

 Model 2: Y[dose] = a * exp{sign * b * dose} 

 Model 3: Y[dose] = a * exp{sign * (b * dose)^d} 

 Model 4: Y[dose] = a * [c-(c-1) * exp{-b * dose}] 

 Model 5: Y[dose] = a * [c-(c-1) * exp{-(b * dose)^d}] 

 Note: Y[dose] is the median response for exposure = dose; 

 sign = +1 for increasing trend in data; 

 sign = -1 for decreasing trend. 

 Model 2 is nested within Models 3 and 4. 

 Model 3 is nested within Model 5. 

 Model 4 is nested within Model 5. 

 Dependent variable = Mean 

 Independent variable = Dose 

 Data are assumed to be distributed: normally 

 Variance Model: exp(lnalpha +rho *ln(Y[dose])) 

 The variance is to be modeled as Var(i) = exp(lalpha + log(mean(i)) * rho) 

 Total number of dose groups = 3 

 Total number of records with missing values = 0 

 Maximum number of iterations = 250 

 Relative Function Convergence has been set to: 1e-008 

 Parameter Convergence has been set to: 1e-008 

 MLE solution provided: Exact 

 Initial Parameter Values 

 Variable Model 2 Model 3 Model 4 Model 5 

 -------- ------- ------- ------- ------- 
Page 365 of 438 

 lnalpha 10.4904 10.4904 10.4904 NC 

 rho 0.128353 0.128353 0.128353 NC 

 a 2092.3 2092.3 3080.7 NC 

 b 2.73147 2.73147 13.0533 NC 

 c -- -- 0.438058 NC 

 d -- 1 -- NC 

 Parameter Estimates by Model 

 Variable Model 2 Model 3 Model 4 Model 5 

 -------- ------- ------- ------- ------- 

 lnalpha 4.57664 4.57663 5.67375 NC 

 rho 0.904493 0.904495 0.727192 NC 

 a 2781.29 2781.29 2936.06 NC 

 b 2.80583 2.80583 21.5306 NC 

 c -- -- 0.481103 NC 

 d -- 1 -- NC 

 NC = No Convergence 

 Table of Stats From Input Data 

 Dose N Obs Mean Obs Std Dev 

 ----- --- ---------- ------------- 

 0 8 2934 360 

 0.02 6 2407 262 

 0.25 3 1417 317 

 Estimated Values of Interest 

 Model Dose Est Mean Est Std Scaled Residual 

 ------- ------ ---------- --------- ---------------- 

 2 0 2781 356 1.213 

 0.02 2630 347.1 -1.57 

 0.25 1379 259.2 0.253 

 3 0 2781 356 1.213 

 0.02 2630 347.1 -1.57 

 0.25 1379 259.2 0.253 

 4 0 2936 311.1 -0.01871 

 0.02 2403 289.2 0.03388 

 0.25 1420 238.9 -0.01847 
Page 366 of 438 

 Other models for which likelihoods are calculated: 

 Model A1: Yij = Mu(i) + e(ij) 

 Var{e(ij)} = Sigma^2 

 Model A2: Yij = Mu(i) + e(ij) 

 Var{e(ij)} = Sigma(i)^2 

 Model A3: Yij = Mu(i) + e(ij) 

 Var{e(ij)} = exp(lalpha + log(mean(i)) * rho) 

 Model R: Yij = Mu + e(i) 

 Var{e(ij)} = Sigma^2 

 Likelihoods of Interest 

 Model Log(likelihood) DF AIC 

 ------- ----------------- ---- ------------ 

 A1 -105.016 4 218.032 

 A2 -104.5863 6 221.1726 

 A3 -104.8524 5 219.7047 

 R -117.7888 2 239.5777 

 2 -107.27 4 222.54 

 3 -107.27 4 222.54 

 4 -104.8524 5 219.7047 

 Additive constant for all log-likelihoods = -15.62. This constant added to the 

 above values gives the log-likelihood including the term that does not 

 depend on the model parameters. 

 Explanation of Tests 

 Test 1: Does response and/or variances differ among Dose levels? (A2 vs. R) 

 Test 2: Are Variances Homogeneous? (A2 vs. A1) 

 Test 3: Are variances adequately modeled? (A2 vs. A3) 

 Test 4: Does Model 2 fit the data? (A3 vs. 2) 

 Test 5a: Does Model 3 fit the data? (A3 vs 3) 

 Test 5b: Is Model 3 better than Model 2? (3 vs. 2) 

 Test 6a: Does Model 4 fit the data? (A3 vs 4) 
Page 367 of 438 

 Test 6b: Is Model 4 better than Model 2? (4 vs. 2) 

 Tests of Interest 

 Test -2*log(Likelihood Ratio) D. F. p-value 

 -------- ------------------------ ------ -------------- 

 Test 1 26.41 4 < 0.0001 

 Test 2 0.8594 2 0.6507 

 Test 3 0.5321 1 0.4657 

 Test 4 4.835 1 0.02788 

 Test 5a 4.835 1 0.02788 

 Test 5b -4.604e-012 0 N/A 

 Test 6a -2.842e-014 0 N/A 

 Test 6b 4.835 1 0.02788 

 The p-value for Test 1 is less than .05. There appears to be a 

 difference between response and/or variances among the dose 

 levels, it seems appropriate to model the data. 

 The p-value for Test 2 is greater than .1. Consider 

 running a homogeneous model. 

 The p-value for Test 3 is greater than .1. The modeled 

 variance appears to be appropriate here. 

 The p-value for Test 4 is less than .1. Model 2 may not adequately 

 describe the data; you may want to consider another model. 

 The p-value for Test 5a is less than .1. Model 3 may not adequately 

 describe the data; you may want to consider another model. 

 Degrees of freedom for Test 5b are less than or equal to 0. 

 The Chi-Square test for fit is not valid. 

 Degrees of freedom for Test 6a are less than or equal to 0. 

 The Chi-Square test for fit is not valid. 

 The p-value for Test 6b is less than .05. Model 4 appears 

 to fit the data better than Model 2. 

 Benchmark Dose Computations: 

 Specified Effect = 0.100000 
Page 368 of 438 

 Risk Type = Relative deviation 

 Confidence Level = 0.950000 

 BMD and BMDL by Model 

 Model BMD BMDL 

 ------- ------------ ---------- 

 2 0.0375506 0.0244537 

 3 0.0375506 0.0244537 

 4 0.00994309 0.00605707 

 5 0 0 Not computed 

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 3000

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Mean Response

dose

Exponential Model 2 with 0.95 Confidence Level

12:41 09/28 2012

BMDL BMD

 

Exponential
Page 369 of 438 

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Mean Response

dose

Exponential Model 3 with 0.95 Confidence Level

12:41 09/28 2012

BMDL BMD

 

Exponential

 500

 1000

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Mean Response

dose

Exponential Model 4 with 0.95 Confidence Level

12:41 09/28 2012

BMDL BMD

 

Exponential
Page 370 of 438 

MRID 44527802 - Chronic Toxicity in Rats (2 year) – Female Adult 735D of dosing – RBC 

– dropped high dose for this analysis

 ==================================================================== 

 Exponential Model. (Version: 1.7; Date: 12/10/2009) 

 Input Data File: F:/dicrotophos/exp_Dicrotophos RBC F 735D drop HD_Setting.(d) 

 Gnuplot Plotting File: 

 Tue Aug 21 18:02:22 2012 

 ==================================================================== 

 BMDS Model Run 

~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ 

 

 The form of the response function by Model: 

 Model 2: Y[dose] = a * exp{sign * b * dose} 

 Model 3: Y[dose] = a * exp{sign * (b * dose)^d} 

 Model 4: Y[dose] = a * [c-(c-1) * exp{-b * dose}] 

 Model 5: Y[dose] = a * [c-(c-1) * exp{-(b * dose)^d}] 

 Note: Y[dose] is the median response for exposure = dose; 

 sign = +1 for increasing trend in data; 

 sign = -1 for decreasing trend. 

 Model 2 is nested within Models 3 and 4. 

 Model 3 is nested within Model 5. 

 Model 4 is nested within Model 5. 

 Dependent variable = Mean 

 Independent variable = Dose 

 Data are assumed to be distributed: normally 

 Variance Model: exp(lnalpha +rho *ln(Y[dose])) 

 rho is set to 0. 

 A constant variance model is fit. 

 Total number of dose groups = 3 

 Total number of records with missing values = 0 

 Maximum number of iterations = 250 

 Relative Function Convergence has been set to: 1e-008 

 Parameter Convergence has been set to: 1e-008 

 MLE solution provided: Exact 

 Initial Parameter Values 
Page 371 of 438 

 Variable Model 2 Model 3 Model 4 Model 5 

 -------- ------- ------- ------- ------- 

 lnalpha 11.3837 11.3837 11.3837 NC 

 rho(S) 0 0 0 NC 

 a 1557.33 1557.33 2718.45 NC 

 b 1.95711 1.95711 9.77858 NC 

 c -- -- 0.469105 NC 

 d -- 1 -- NC 

 (S) = Specified 

 Parameter Estimates by Model 

 Variable Model 2 Model 3 Model 4 Model 5 

 -------- ------- ------- ------- ------- 

 lnalpha 11.4546 11.4546 11.3837 NC 

 rho 0 0 0 NC 

 a 2504.58 2504.58 2589 NC 

 b 1.99571 1.99571 10.2157 NC 

 c -- -- 0.498094 NC 

 d -- 1 -- NC 

 NC = No Convergence 

 Table of Stats From Input Data 

 Dose N Obs Mean Obs Std Dev 

 ----- --- ---------- ------------- 

 0 8 2589 255 

 0.03 7 2246 208 

 0.32 8 1339 432 

 Estimated Values of Interest 

 Model Dose Est Mean Est Std Scaled Residual 

 ------- ------ ---------- --------- ---------------- 

 2 0 2505 307.1 0.7774 

 0.03 2359 307.1 -0.9736 

 0.32 1322 307.1 0.1523 

 3 0 2505 307.1 0.7774 

 0.03 2359 307.1 -0.9736 
Page 372 of 438 

 0.32 1322 307.1 0.1523 

 4 0 2589 296.4 3.613e-008 

 0.03 2246 296.4 1.708e-008 

 0.32 1339 296.4 2.188e-009 

 Other models for which likelihoods are calculated: 

 Model A1: Yij = Mu(i) + e(ij) 

 Var{e(ij)} = Sigma^2 

 Model A2: Yij = Mu(i) + e(ij) 

 Var{e(ij)} = Sigma(i)^2 

 Model A3: Yij = Mu(i) + e(ij) 

 Var{e(ij)} = exp(lalpha + log(mean(i)) * rho) 

 Model R: Yij = Mu + e(i) 

 Var{e(ij)} = Sigma^2 

 Likelihoods of Interest 

 Model Log(likelihood) DF AIC 

 ------- ----------------- ---- ------------ 

 A1 -142.4122 4 292.8244 

 A2 -140.1325 6 292.265 

 A3 -142.4122 4 292.8244 

 R -159.1432 2 322.2865 

 2 -143.2283 3 292.4565 

 3 -143.2283 3 292.4565 

 4 -142.4122 4 292.8244 

 Additive constant for all log-likelihoods = -21.14. This constant added to the 

 above values gives the log-likelihood including the term that does not 

 depend on the model parameters. 

 Explanation of Tests 

 Test 1: Does response and/or variances differ among Dose levels? (A2 vs. R) 

 Test 2: Are Variances Homogeneous? (A2 vs. A1) 

 Test 3: Are variances adequately modeled? (A2 vs. A3) 

 Test 4: Does Model 2 fit the data? (A3 vs. 2) 
Page 373 of 438 

 Test 5a: Does Model 3 fit the data? (A3 vs 3) 

 Test 5b: Is Model 3 better than Model 2? (3 vs. 2) 

 Test 6a: Does Model 4 fit the data? (A3 vs 4) 

 Test 6b: Is Model 4 better than Model 2? (4 vs. 2) 

 Tests of Interest 

 Test -2*log(Likelihood Ratio) D. F. p-value 

 -------- ------------------------ ------ -------------- 

 Test 1 38.02 4 < 0.0001 

 Test 2 4.559 2 0.1023 

 Test 3 4.559 2 0.1023 

 Test 4 1.632 1 0.2014 

 Test 5a 1.632 1 0.2014 

 Test 5b -5.684e-014 0 N/A 

 Test 6a 0 0 N/A 

 Test 6b 1.632 1 0.2014 

 The p-value for Test 1 is less than .05. There appears to be a 

 difference between response and/or variances among the dose 

 levels, it seems appropriate to model the data. 

 The p-value for Test 2 is greater than .1. A homogeneous 

 variance model appears to be appropriate here. 

 The p-value for Test 3 is greater than .1. The modeled 

 variance appears to be appropriate here. 

 The p-value for Test 4 is greater than .1. Model 2 seems 

 to adequately describe the data. 

 The p-value for Test 5a is greater than .1. Model 3 seems 

 to adequately describe the data. 

 Degrees of freedom for Test 5b are less than or equal to 0. 

 The Chi-Square test for fit is not valid. 

 Degrees of freedom for Test 6a are less than or equal to 0. 

 The Chi-Square test for fit is not valid. 

 The p-value for Test 6b is greater than .05. Model 4 does 

 not seem to fit the data better than Model 2. 
Page 374 of 438 

 Benchmark Dose Computations: 

 Specified Effect = 0.100000 

 Risk Type = Relative deviation 

 Confidence Level = 0.950000 

 BMD and BMDL by Model 

 Model BMD BMDL 

 ------- ------------ ---------- 

 2 0.0527935 0.0419443 

 3 0.0527935 0.0419443 

 4 0.0217502 0.0113337 

 5 0 0 Not computed 
Page 375 of 438 

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Mean Response

dose

Exponential Model 2 with 0.95 Confidence Level

18:02 08/21 2012

BMDL BMD

 

Exponential

 1000

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Mean Response

dose

Exponential Model 3 with 0.95 Confidence Level

18:02 08/21 2012

BMDL BMD

 

Exponential
Page 376 of 438 

 28- Day Dermal Study - MRID 46484501

Male Brain ChE Inhibition

 ==================================================================== 

 Exponential Model. (Version: 1.7; Date: 12/10/2009) 

 Input Data File: C:/Usepa/BMDS212/Data/exp_Testrunscontinuous_Setting.(d) 

 Gnuplot Plotting File: 

 Tue Apr 10 11:56:01 2012 

 ==================================================================== 

 BMDS Model Run 

~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ 

 

 The form of the response function by Model: 

 Model 2: Y[dose] = a * exp{sign * b * dose} 

 Model 3: Y[dose] = a * exp{sign * (b * dose)^d} 

 Model 4: Y[dose] = a * [c-(c-1) * exp{-b * dose}] 

 Model 5: Y[dose] = a * [c-(c-1) * exp{-(b * dose)^d}] 

 Note: Y[dose] is the median response for exposure = dose; 

 sign = +1 for increasing trend in data; 

 sign = -1 for decreasing trend. 

 Model 2 is nested within Models 3 and 4. 

 Model 3 is nested within Model 5. 

 Model 4 is nested within Model 5. 

 1000

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Mean Response

dose

Exponential Model 4 with 0.95 Confidence Level

18:02 08/21 2012

BMDL BMD

 

Exponential
Page 377 of 438 

 Dependent variable = Mean 

 Independent variable = Dose 

 Data are assumed to be distributed: normally 

 Variance Model: exp(lnalpha +rho *ln(Y[dose])) 

 rho is set to 0. 

 A constant variance model is fit. 

 Total number of dose groups = 5 

 Total number of records with missing values = 0 

 Maximum number of iterations = 250 

 Relative Function Convergence has been set to: 1e-008 

 Parameter Convergence has been set to: 1e-008 

 MLE solution provided: Exact 

 Initial Parameter Values 

 Variable Model 2 Model 3 Model 4 Model 5 

 -------- ------- ------- ------- ------- 

 lnalpha -1.61623 -1.61623 -1.61623 -1.61623 

 rho(S) 0 0 0 0 

 a 3.4103 3.4103 5.355 5.355 

 b 0.00976756 0.00976756 0.0433234 0.0433234 

 c -- -- 0.393046 

0.393046 

 d -- 1 -- 1 

 (S) = Specified 

 Parameter Estimates by Model 

 Variable Model 2 Model 3 Model 4 Model 5 

 -------- ------- ------- ------- ------- 

 lnalpha -1.14855 -1.14855 -1.38383 -1.49766 

 rho 0 0 0 0 

 a 4.79786 4.79786 5.10701 4.85581 

 b 0.0105971 0.0105971 0.0517293 0.0915615 

 c -- -- 0.42112 0.455124 

 d -- 1 -- 6.42604 

 Table of Stats From Input Data 

 Dose N Obs Mean Obs Std Dev 

 ----- --- ---------- ------------- 

 0 5 5.1 0.42 

 2 5 4.61 0.42 

 5 5 4.84 0.55 

 10 5 3.71 0.65 

 80 4 2.21 0.39 

 Estimated Values of Interest 

 Model Dose Est Mean Est Std Scaled Residual 

 ------- ------ ---------- --------- ---------------- 

 2 0 4.798 0.5631 1.2 

 2 4.697 0.5631 -0.3464 

 5 4.55 0.5631 1.151 
Page 378 of 438 

 10 4.315 0.5631 -2.404 

 80 2.055 0.5631 0.5496 

 3 0 4.798 0.5631 1.2 

 2 4.697 0.5631 -0.3464 

 5 4.55 0.5631 1.151 

 10 4.315 0.5631 -2.404 

 80 2.055 0.5631 0.5496 

 4 0 5.107 0.5006 -0.03133 

 2 4.816 0.5006 -0.9221 

 5 4.433 0.5006 1.817 

 10 3.913 0.5006 -0.9069 

 80 2.198 0.5006 0.04868 

 5 0 4.856 0.4729 1.155 

 2 4.856 0.4729 -1.162 

 5 4.838 0.4729 0.007529 

 10 3.71 0.4729 -8.731e-005 

 80 2.21 0.4729 1.064e-005 

 Other models for which likelihoods are calculated: 

 Model A1: Yij = Mu(i) + e(ij) 

 Var{e(ij)} = Sigma^2 

 Model A2: Yij = Mu(i) + e(ij) 

 Var{e(ij)} = Sigma(i)^2 

 Model A3: Yij = Mu(i) + e(ij) 

 Var{e(ij)} = exp(lalpha + log(mean(i)) * rho) 

 Model R: Yij = Mu + e(i) 

 Var{e(ij)} = Sigma^2 

 Likelihoods of Interest 

 Model Log(likelihood) DF AIC 

 ------- ----------------- ---- ------------ 

 A1 7.394781 6 -2.789563 

 A2 8.391339 10 3.217322 

 A3 7.394781 6 -2.789563 

 R -14.15813 2 32.31627 

 2 1.782609 3 2.434781 

 3 1.782609 3 2.434781 

 4 4.60599 4 -1.21198 

 5 5.971963 5 -1.943926 

 Additive constant for all log-likelihoods = -22.05. This constant added to the 

 above values gives the log-likelihood including the term that does not 

 depend on the model parameters. 

 Explanation of Tests 

 Test 1: Does response and/or variances differ among Dose levels? (A2 vs. R) 

 Test 2: Are Variances Homogeneous? (A2 vs. A1) 

 Test 3: Are variances adequately modeled? (A2 vs. A3) 

 Test 4: Does Model 2 fit the data? (A3 vs. 2) 

 Test 5a: Does Model 3 fit the data? (A3 vs 3) 

 Test 5b: Is Model 3 better than Model 2? (3 vs. 2) 
Page 379 of 438 

 Test 6a: Does Model 4 fit the data? (A3 vs 4) 

 Test 6b: Is Model 4 better than Model 2? (4 vs. 2) 

 Test 7a: Does Model 5 fit the data? (A3 vs 5) 

 Test 7b: Is Model 5 better than Model 3? (5 vs. 3) 

 Test 7c: Is Model 5 better than Model 4? (5 vs. 4) 

 Tests of Interest 

 Test -2*log(Likelihood Ratio) D. F. p-value 

 -------- ------------------------ ------ -------------- 

 Test 1 45.1 8 < 0.0001 

 Test 2 1.993 4 0.737 

 Test 3 1.993 4 0.737 

 Test 4 11.22 3 0.01057 

 Test 5a 11.22 3 0.01057 

 Test 5b -7.994e-015 0 N/A 

 Test 6a 5.578 2 0.0615 

 Test 6b 5.647 1 0.01749 

 Test 7a 2.846 1 0.09162 

 Test 7b 8.379 2 0.01516 

 Test 7c 2.732 1 0.09836 

 The p-value for Test 1 is less than .05. There appears to be a 

 difference between response and/or variances among the dose 

 levels, it seems appropriate to model the data. 

 The p-value for Test 2 is greater than .1. A homogeneous 

 variance model appears to be appropriate here. 

 The p-value for Test 3 is greater than .1. The modeled 

 variance appears to be appropriate here. 

 The p-value for Test 4 is less than .1. Model 2 may not adequately 

 describe the data; you may want to consider another model. 

 The p-value for Test 5a is less than .1. Model 3 may not adequately 

 describe the data; you may want to consider another model. 

 Degrees of freedom for Test 5b are less than or equal to 0. 

 The Chi-Square test for fit is not valid. 

 The p-value for Test 6a is less than .1. Model 4 may not adequately 

 describe the data; you may want to consider another model. 

 The p-value for Test 6b is less than .05. Model 4 appears 

 to fit the data better than Model 2. 

 The p-value for Test 7a is less than .1. Model 5 may not adequately 

 describe the data; you may want to consider another model. 

 The p-value for Test 7b is less than .05. Model 5 appears 

 to fit the data better than Model 3. 

 The p-value for Test 7c is greater than .05. Model 5 does 

 not seem to fit the data better than Model 4. 

 Benchmark Dose Computations: 
Page 380 of 438 

 Specified Effect = 0.100000 

 Risk Type = Relative deviation 

 Confidence Level = 0.950000 

 BMD and BMDL by Model 

 Model BMD BMDL 

 ------- ------------ ---------- 

 2 9.94238 7.45725 

 3 9.94238 7.45725 

 4 3.6661 2.41499 (p = 0.062 – better visual fit than 

model 5) 

 5 8.52006 3.50191 (p = 0.092) 

 1.5

 2

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 3

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Page 381 of 438 

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Page 382 of 438 

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 2

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Mean Response

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Exponential Model 4 with 0.95 Confidence Level

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Page 383 of 438 

Female Brain ChE Inhibition

==================================================================

== 

 Exponential Model. (Version: 1.7; Date: 12/10/2009) 

 Input Data File: C:/Usepa/BMDS212/Data/exp_Testrunscontinuous_Setting.(d) 

 Gnuplot Plotting File: 

 Tue Apr 10 13:29:49 2012 

==================================================================

== 

 BMDS Model Run 

 1.5

 2

 2.5

 3

 3.5

 4

 4.5

 5

 5.5

 6

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Mean Response

dose

Exponential Model 5 with 0.95 Confidence Level

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Page 384 of 438 

~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

~~~ 

 

 The form of the response function by Model: 

 Model 2: Y[dose] = a * exp{sign * b * dose} 

 Model 3: Y[dose] = a * exp{sign * (b * dose)^d} 

 Model 4: Y[dose] = a * [c-(c-1) * exp{-b * dose}] 

 Model 5: Y[dose] = a * [c-(c-1) * exp{-(b * dose)^d}] 

 Note: Y[dose] is the median response for exposure = dose; 

 sign = +1 for increasing trend in data; 

 sign = -1 for decreasing trend. 

 Model 2 is nested within Models 3 and 4. 

 Model 3 is nested within Model 5. 

 Model 4 is nested within Model 5. 

 Dependent variable = Mean 

 Independent variable = Dose 

 Data are assumed to be distributed: normally 

 Variance Model: exp(lnalpha +rho *ln(Y[dose])) 

 The variance is to be modeled as Var(i) = exp(lalpha + log(mean(i)) * rho) 

 Total number of dose groups = 5 

 Total number of records with missing values = 0 

 Maximum number of iterations = 250 

 Relative Function Convergence has been set to: 1e-008 

 Parameter Convergence has been set to: 1e-008 

 MLE solution provided: Exact 

 Initial Parameter Values 

 Variable Model 2 Model 3 Model 4 Model 5 

 -------- ------- ------- ------- ------- 

 lnalpha -5.68391 -5.68391 -5.68391 -5.68391 

 rho 3.13014 3.13014 3.13014 3.13014 

 a 3.06133 3.06133 5.3445 5.3445 

 b 0.0122454 0.0122454 0.0474812 0.0474812 

 c -- -- 0.313629 0.313629 

 d -- 1 -- 1 
Page 385 of 438 

 Parameter Estimates by Model 

 Variable Model 2 Model 3 Model 4 Model 5 

 -------- ------- ------- ------- ------- 

 lnalpha -5.79711 -5.81027 -5.87967 -5.87967 

 rho 3.53656 3.70235 3.47947 3.47947 

 a 4.61968 4.4075 4.99158 4.99158 

 b 0.0121034 0.0124162 0.0491276 0.0491276 

 c -- -- 0.340431 0.340431 

 d -- 12.7156 -- 1 

 Table of Stats From Input Data 

 Dose N Obs Mean Obs Std Dev 

 ----- --- ---------- ------------- 

 0 5 5.09 1.15 

 2 5 4.32 0.47 

 5 5 4.46 0.97 

 10 5 3.76 0.18 

 80 4 1.76 0.18 

 Estimated Values of Interest 

 Model Dose Est Mean Est Std Scaled Residual 

 ------- ------ ---------- --------- ---------------- 

 2 0 4.62 0.8249 1.275 

 2 4.509 0.7903 -0.5353 

 5 4.348 0.7412 0.3367 

 10 4.093 0.666 -1.118 

 80 1.754 0.1489 0.07704 

 3 0 4.407 0.8528 1.79 

 2 4.407 0.8528 -0.2294 

 5 4.407 0.8528 0.1377 

 10 4.407 0.8528 -1.698 

 80 1.76 0.1559 1.34e-010 

 4 0 4.992 0.8669 0.2539 

 2 4.683 0.776 -1.047 

 5 4.275 0.6619 0.6265 

 10 3.714 0.5183 0.1999 

 80 1.764 0.1419 -0.05563 

 5 0 4.992 0.8669 0.2539 

 2 4.683 0.776 -1.047 

 5 4.275 0.6619 0.6265 

 10 3.714 0.5183 0.1999 
Page 386 of 438 

 80 1.764 0.1419 -0.05563 

 Other models for which likelihoods are calculated: 

 Model A1: Yij = Mu(i) + e(ij) 

 Var{e(ij)} = Sigma^2 

 Model A2: Yij = Mu(i) + e(ij) 

 Var{e(ij)} = Sigma(i)^2 

 Model A3: Yij = Mu(i) + e(ij) 

 Var{e(ij)} = exp(lalpha + log(mean(i)) * rho) 

 Model R: Yij = Mu + e(i) 

 Var{e(ij)} = Sigma^2 

 Likelihoods of Interest 

 Model Log(likelihood) DF AIC 

 ------- ----------------- ---- ------------ 

 A1 -1.689579 6 15.37916 

 A2 9.468585 10 1.06283 

 A3 4.932064 7 4.135871 

 R -17.51614 2 39.03229 

 2 1.288065 4 5.423869 

 3 -1.380025 5 12.76005 

 4 3.140973 5 3.718054 

 5 3.140973 5 3.718054 

 Additive constant for all log-likelihoods = -22.05. This constant added to the 

 above values gives the log-likelihood including the term that does not 

 depend on the model parameters. 

 Explanation of Tests 

 Test 1: Does response and/or variances differ among Dose levels? (A2 vs. R) 

 Test 2: Are Variances Homogeneous? (A2 vs. A1) 

 Test 3: Are variances adequately modeled? (A2 vs. A3) 

 Test 4: Does Model 2 fit the data? (A3 vs. 2) 

 Test 5a: Does Model 3 fit the data? (A3 vs 3) 
Page 387 of 438 

 Test 5b: Is Model 3 better than Model 2? (3 vs. 2) 

 Test 6a: Does Model 4 fit the data? (A3 vs 4) 

 Test 6b: Is Model 4 better than Model 2? (4 vs. 2) 

 Test 7a: Does Model 5 fit the data? (A3 vs 5) 

 Test 7b: Is Model 5 better than Model 3? (5 vs. 3) 

 Test 7c: Is Model 5 better than Model 4? (5 vs. 4) 

 Tests of Interest 

 Test -2*log(Likelihood Ratio) D. F. p-value 

 -------- ------------------------ ------ -------------- 

 Test 1 53.97 8 < 0.0001 

 Test 2 22.32 4 0.0001734 

 Test 3 9.073 3 0.02834 

 Test 4 7.288 3 0.06326 

 Test 5a 12.62 2 0.001814 

 Test 5b -5.336 1 N/A 

 Test 6a 3.582 2 0.1668 

 Test 6b 3.706 1 0.05422 

 Test 7a 3.582 2 0.1668 

 Test 7b 9.042 0 N/A 

 Test 7c 3.553e-015 0 N/A 

 The p-value for Test 1 is less than .05. There appears to be a 

 difference between response and/or variances among the dose 

 levels, it seems appropriate to model the data. 

 The p-value for Test 2 is less than .1. A non-homogeneous 

 variance model appears to be appropriate. 

 The p-value for Test 3 is less than .1. You may want to 

 consider a different variance model. 

 The p-value for Test 4 is less than .1. Model 2 may not adequately 

 describe the data; you may want to consider another model. 

 The p-value for Test 5a is less than .1. Model 3 may not adequately 

 describe the data; you may want to consider another model. 

 The p-value for Test 5b is less than .05. Model 3 appears 

 to fit the data better than Model 2. 
Page 388 of 438 

 The p-value for Test 6a is greater than .1. Model 4 seems 

 to adequately describe the data. 

 The p-value for Test 6b is greater than .05. Model 4 does 

 not seem to fit the data better than Model 2. 

 The p-value for Test 7a is greater than .1. Model 5 seems 

 to adequately describe the data. 

 Degrees of freedom for Test 7b are less than or equal to 0. 

 The Chi-Square test for fit is not valid. 

 Degrees of freedom for Test 7c are less than or equal to 0. 

 The Chi-Square test for fit is not valid. 

 Benchmark Dose Computations: 

 Specified Effect = 0.100000 

 Risk Type = Relative deviation 

 Confidence Level = 0.950000 

 BMD and BMDL by Model 

 Model BMD BMDL 

 ------- ------------ ---------- 

 2 8.705 7.80685 

 3 67.4765 6.76989 

 4 3.34679 2.14411 

 5 3.34679 2.14411 
Page 389 of 438 

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Page 390 of 438 

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Exponential Model 3 with 0.95 Confidence Level

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Page 391 of 438 

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 7

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Mean Response

dose

Exponential Model 4 with 0.95 Confidence Level

13:29 04/10 2012

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Page 392 of 438 

Male RBC ChE Inhibition 

 ==================================================================== 

 Exponential Model. (Version: 1.7; Date: 12/10/2009) 

 Input Data File: C:/Usepa/BMDS212/Data/exp_Testrunscontinuous_Setting.(d) 

 Gnuplot Plotting File: 

 Mon Apr 09 11:59:34 2012 

 ==================================================================== 

 BMDS Model Run 

~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ 

 

 The form of the response function by Model: 

 Model 2: Y[dose] = a * exp{sign * b * dose} 

 Model 3: Y[dose] = a * exp{sign * (b * dose)^d} 

 Model 4: Y[dose] = a * [c-(c-1) * exp{-b * dose}] 

 Model 5: Y[dose] = a * [c-(c-1) * exp{-(b * dose)^d}] 

 Note: Y[dose] is the median response for exposure = dose; 

 sign = +1 for increasing trend in data; 

 sign = -1 for decreasing trend. 

 Model 2 is nested within Models 3 and 4. 

 Model 3 is nested within Model 5. 

 1

 2

 3

 4

 5

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 7

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Exponential Model 5 with 0.95 Confidence Level

13:29 04/10 2012

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Page 393 of 438 

 Model 4 is nested within Model 5. 

 Dependent variable = Mean 

 Independent variable = Dose 

 Data are assumed to be distributed: normally 

 Variance Model: exp(lnalpha +rho *ln(Y[dose])) 

 The variance is to be modeled as Var(i) = exp(lalpha + log(mean(i)) * rho) 

 Total number of dose groups = 5 

 Total number of records with missing values = 0 

 Maximum number of iterations = 250 

 Relative Function Convergence has been set to: 1e-008 

 Parameter Convergence has been set to: 1e-008 

 MLE solution provided: Exact 

 Initial Parameter Values 

 Variable Model 2 Model 3 Model 4 Model 5 

 -------- ------- ------- ------- ------- 

 lnalpha 18.3126 18.3126 18.3126 18.3126 

 rho -1.07049 -1.07049 -1.07049 -1.07049 

 a 1705.62 17.471 2502.15 2502.15 

 b 0.0065143 -1.01194e-005 0.0389852 0.0389852 

 c -- -- 0.489864 

0.489864 

 d -- 3 -- 1 

 Parameter Estimates by Model 

 Variable Model 2 Model 3 Model 4 Model 5 

 -------- ------- ------- ------- ------- 

 lnalpha -0.767797 -124.287 3.91331 3.91331 

 rho 1.54984 18 0.868281 0.868281 

 a 2134.93 1939.92 2340.26 2340.26 

 b 0.00662482 1.01952e-005 0.0799318 0.0799318 

 c -- -- 0.55037 0.55037 

 d -- 3.96819 -- 1 

 Table of Stats From Input Data 

 Dose N Obs Mean Obs Std Dev 

 ----- --- ---------- ------------- 

 0 5 2383 78 

 2 5 2115 314 

 5 5 2012 243 

 10 5 1772 114 

 80 4 1287 196 

 Estimated Values of Interest 

 Model Dose Est Mean Est Std Scaled Residual 

 ------- ------ ---------- --------- ---------------- 

 2 0 2135 259 2.142 

 2 2107 256.3 0.07131 

 5 2065 252.4 -0.4727 

 10 1998 246 -2.055 
Page 394 of 438 

 80 1257 171.8 0.3535 

 3 0 1940 399.4 2.48 

 2 1940 399.4 0.9801 

 5 1940 399.4 0.4035 

 10 1940 399.4 -0.94 

 80 1940 399.4 -3.269 

 4 0 2340 205.4 0.4654 

 2 2185 199.3 -0.7831 

 5 1994 191.5 0.2148 

 10 1761 181.5 0.1338 

 80 1290 158.5 -0.03491 

 5 0 2340 205.4 0.4654 

 2 2185 199.3 -0.7831 

 5 1994 191.5 0.2148 

 10 1761 181.5 0.1338 

 80 1290 158.5 -0.03491 

 Other models for which likelihoods are calculated: 

 Model A1: Yij = Mu(i) + e(ij) 

 Var{e(ij)} = Sigma^2 

 Model A2: Yij = Mu(i) + e(ij) 

 Var{e(ij)} = Sigma(i)^2 

 Model A3: Yij = Mu(i) + e(ij) 

 Var{e(ij)} = exp(lalpha + log(mean(i)) * rho) 

 Model R: Yij = Mu + e(i) 

 Var{e(ij)} = Sigma^2 

 Likelihoods of Interest 

 Model Log(likelihood) DF AIC 

 ------- ----------------- ---- ------------ 

 A1 -137.2995 6 286.5989 

 A2 -131.9825 10 283.9649 

 A3 -137.2605 7 288.521 

 R -155.7613 2 315.5227 

 2 -143.2835 4 294.5669 

 3 -155.7613 5 321.5227 

 4 -137.6444 5 285.2888 

 5 -137.6444 5 285.2888 

 Additive constant for all log-likelihoods = -22.05. This constant added to the 

 above values gives the log-likelihood including the term that does not 

 depend on the model parameters. 

 Explanation of Tests 

 Test 1: Does response and/or variances differ among Dose levels? (A2 vs. R) 

 Test 2: Are Variances Homogeneous? (A2 vs. A1) 

 Test 3: Are variances adequately modeled? (A2 vs. A3) 

 Test 4: Does Model 2 fit the data? (A3 vs. 2) 

 Test 5a: Does Model 3 fit the data? (A3 vs 3) 

 Test 5b: Is Model 3 better than Model 2? (3 vs. 2) 
Page 395 of 438 

 Test 6a: Does Model 4 fit the data? (A3 vs 4) 

 Test 6b: Is Model 4 better than Model 2? (4 vs. 2) 

 Test 7a: Does Model 5 fit the data? (A3 vs 5) 

 Test 7b: Is Model 5 better than Model 3? (5 vs. 3) 

 Test 7c: Is Model 5 better than Model 4? (5 vs. 4) 

 Tests of Interest 

 Test -2*log(Likelihood Ratio) D. F. p-value 

 -------- ------------------------ ------ -------------- 

 Test 1 47.56 8 < 0.0001 

 Test 2 10.63 4 0.031 

 Test 3 10.56 3 0.01439 

 Test 4 12.05 3 0.007228 

 Test 5a 37 2 < 0.0001 

 Test 5b -24.96 1 N/A 

 Test 6a 0.7678 2 0.6812 

 Test 6b 11.28 1 0.0007843 

 Test 7a 0.7678 2 0.6812 

 Test 7b 36.23 0 N/A 

 Test 7c 0 0 N/A 

 The p-value for Test 1 is less than .05. There appears to be a 

 difference between response and/or variances among the dose 

 levels, it seems appropriate to model the data. 

 The p-value for Test 2 is less than .1. A non-homogeneous 

 variance model appears to be appropriate. 

 The p-value for Test 3 is less than .1. You may want to 

 consider a different variance model. 

 The p-value for Test 4 is less than .1. Model 2 may not adequately 

 describe the data; you may want to consider another model. 

 The p-value for Test 5a is less than .1. Model 3 may not adequately 

 describe the data; you may want to consider another model. 

 The p-value for Test 5b is less than .05. Model 3 appears 

 to fit the data better than Model 2. 

 The p-value for Test 6a is greater than .1. Model 4 seems 

 to adequately describe the data. 

 The p-value for Test 6b is less than .05. Model 4 appears 

 to fit the data better than Model 2. 

 The p-value for Test 7a is greater than .1. Model 5 seems 

 to adequately describe the data. 

 Degrees of freedom for Test 7b are less than or equal to 0. 

 The Chi-Square test for fit is not valid. 

 Degrees of freedom for Test 7c are less than or equal to 0. 

 The Chi-Square test for fit is not valid. 

 Benchmark Dose Computations: 

 Specified Effect = 0.100000 
Page 396 of 438 

 Risk Type = Relative deviation 

 Confidence Level = 0.950000 

 BMD and BMDL by Model 

 Model BMD BMDL 

 ------- ------------ ---------- 

 2 15.9039 11.3002 

 3 55630.7 66.322 

 4 3.14705 2.12215 

 5 3.14705 2.12215 

 1000

 1200

 1400

 1600

 1800

 2000

 2200

 2400

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 0 10 20 30 40 50 60 70 8

Mean Response

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Exponential Model 2 with 0.95 Confidence Level

11:59 04/09 2012

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Page 397 of 438 

 1000

 1200

 1400

 1600

 1800

 2000

 2200

 2400

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 0 10000 20000 30000 40000 50000

Mean Response

dose

Exponential Model 3 with 0.95 Confidence Level

11:59 04/09 2012

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Page 398 of 438 

 1000

 1200

 1400

 1600

 1800

 2000

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 2400

 2600

 0 10 20 30 40 50 60 70 8

Mean Response

dose

Exponential Model 4 with 0.95 Confidence Level

11:59 04/09 2012

BMDL BMD

 

Exponential
Page 399 of 438 

Female RBC ChE Inhibition 

 ==================================================================== 

 Exponential Model. (Version: 1.7; Date: 12/10/2009) 

 Input Data File: C:/Usepa/BMDS212/Data/exp_Testrunscontinuous_Setting.(d) 

 Gnuplot Plotting File: 

 Tue Apr 10 10:43:13 2012 

 ==================================================================== 

 BMDS Model Run 

~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ 

 

 The form of the response function by Model: 

 Model 2: Y[dose] = a * exp{sign * b * dose} 

 Model 3: Y[dose] = a * exp{sign * (b * dose)^d} 

 Model 4: Y[dose] = a * [c-(c-1) * exp{-b * dose}] 

 Model 5: Y[dose] = a * [c-(c-1) * exp{-(b * dose)^d}] 

 1000

 1200

 1400

 1600

 1800

 2000

 2200

 2400

 2600

 0 10 20 30 40 50 60 70 8

Mean Response

dose

Exponential Model 5 with 0.95 Confidence Level

11:59 04/09 2012

BMDL BMD

 

Exponential
Page 400 of 438 

 Note: Y[dose] is the median response for exposure = dose; 

 sign = +1 for increasing trend in data; 

 sign = -1 for decreasing trend. 

 Model 2 is nested within Models 3 and 4. 

 Model 3 is nested within Model 5. 

 Model 4 is nested within Model 5. 

 Dependent variable = Mean 

 Independent variable = Dose 

 Data are assumed to be distributed: normally 

 Variance Model: exp(lnalpha +rho *ln(Y[dose])) 

 The variance is to be modeled as Var(i) = exp(lalpha + log(mean(i)) * rho) 

 Total number of dose groups = 5 

 Total number of records with missing values = 0 

 Maximum number of iterations = 250 

 Relative Function Convergence has been set to: 1e-008 

 Parameter Convergence has been set to: 1e-008 

 MLE solution provided: Exact 

 Initial Parameter Values 

 Variable Model 2 Model 3 Model 4 Model 5 

 -------- ------- ------- ------- ------- 

 lnalpha 7.85523 7.85523 7.85523 7.85523 

 rho 0.480096 0.480096 0.480096 0.480096 

 a 3433.25 3433.25 5411.7 5411.7 

 b 0.00969376 0.00969376 0.0433292 0.0433292 

 c -- -- 0.39192 

0.39192 

 d -- 1 -- 1 

 Parameter Estimates by Model 

 Variable Model 2 Model 3 Model 4 Model 5 

 -------- ------- ------- ------- ------- 

 lnalpha -3.57205 -3.57205 -0.559726 -0.559726 

 rho 1.90958 1.90958 1.51063 1.51063 

 a 4773.54 4773.54 5120.9 5120.9 

 b 0.00973329 0.00973329 0.0525265 0.0525265 

 c -- -- 0.426574 0.426574 

 d -- 1 -- 1 

 Table of Stats From Input Data 

 Dose N Obs Mean Obs Std Dev 

 ----- --- ---------- ------------- 

 0 5 5154 183 

 2 5 4847 736 

 5 5 4341 503 

 10 5 3972 348 

 80 4 2227 297 

 Estimated Values of Interest 
Page 401 of 438 

 Model Dose Est Mean Est Std Scaled Residual 

 ------- ------ ---------- --------- ---------------- 

 2 0 4774 545.6 1.559 

 2 4682 535.5 0.691 

 5 4547 520.8 -0.8836 

 10 4331 497.2 -1.614 

 80 2191 259.4 0.2764 

 3 0 4774 545.6 1.559 

 2 4682 535.5 0.691 

 5 4547 520.8 -0.8836 

 10 4331 497.2 -1.614 

 80 2191 259.4 0.2764 

 4 0 5121 478.8 0.1546 

 2 4828 458 0.09243 

 5 4443 430.1 -0.5285 

 10 3921 391.4 0.291 

 80 2228 255.4 -0.01082 

 5 0 5121 478.8 0.1546 

 2 4828 458 0.09243 

 5 4443 430.1 -0.5285 

 10 3921 391.4 0.291 

 80 2228 255.4 -0.01082 

 Other models for which likelihoods are calculated: 

 Model A1: Yij = Mu(i) + e(ij) 

 Var{e(ij)} = Sigma^2 

 Model A2: Yij = Mu(i) + e(ij) 

 Var{e(ij)} = Sigma(i)^2 

 Model A3: Yij = Mu(i) + e(ij) 

 Var{e(ij)} = exp(lalpha + log(mean(i)) * rho) 

 Model R: Yij = Mu + e(i) 

 Var{e(ij)} = Sigma^2 

 Likelihoods of Interest 

 Model Log(likelihood) DF AIC 

 ------- ----------------- ---- ------------ 

 A1 -156.4691 6 324.9381 

 A2 -151.3857 10 322.7713 

 A3 -155.6395 7 325.279 

 R -179.0346 2 362.0691 

 2 -159.4802 4 326.9604 

 3 -159.4802 4 326.9604 

 4 -155.8312 5 321.6624 

 5 -155.8312 5 321.6624 

 Additive constant for all log-likelihoods = -22.05. This constant added to the 

 above values gives the log-likelihood including the term that does not 

 depend on the model parameters. 

 Explanation of Tests 

 Test 1: Does response and/or variances differ among Dose levels? (A2 vs. R) 

 Test 2: Are Variances Homogeneous? (A2 vs. A1) 
Page 402 of 438 

 Test 3: Are variances adequately modeled? (A2 vs. A3) 

 Test 4: Does Model 2 fit the data? (A3 vs. 2) 

 Test 5a: Does Model 3 fit the data? (A3 vs 3) 

 Test 5b: Is Model 3 better than Model 2? (3 vs. 2) 

 Test 6a: Does Model 4 fit the data? (A3 vs 4) 

 Test 6b: Is Model 4 better than Model 2? (4 vs. 2) 

 Test 7a: Does Model 5 fit the data? (A3 vs 5) 

 Test 7b: Is Model 5 better than Model 3? (5 vs. 3) 

 Test 7c: Is Model 5 better than Model 4? (5 vs. 4) 

 Tests of Interest 

 Test -2*log(Likelihood Ratio) D. F. p-value 

 -------- ------------------------ ------ -------------- 

 Test 1 55.3 8 < 0.0001 

 Test 2 10.17 4 0.03771 

 Test 3 8.508 3 0.03661 

 Test 4 7.681 3 0.05308 

 Test 5a 7.681 3 0.05308 

 Test 5b -6.821e-013 0 N/A 

 Test 6a 0.3834 2 0.8256 

 Test 6b 7.298 1 0.006903 

 Test 7a 0.3834 2 0.8256 

 Test 7b 7.298 1 0.006903 

 Test 7c 0 0 N/A 

 The p-value for Test 1 is less than .05. There appears to be a 

 difference between response and/or variances among the dose 

 levels, it seems appropriate to model the data. 

 The p-value for Test 2 is less than .1. A non-homogeneous 

 variance model appears to be appropriate. 

 The p-value for Test 3 is less than .1. You may want to 

 consider a different variance model. 

 The p-value for Test 4 is less than .1. Model 2 may not adequately 

 describe the data; you may want to consider another model. 

 The p-value for Test 5a is less than .1. Model 3 may not adequately 

 describe the data; you may want to consider another model. 

 Degrees of freedom for Test 5b are less than or equal to 0. 

 The Chi-Square test for fit is not valid. 

 The p-value for Test 6a is greater than .1. Model 4 seems 

 to adequately describe the data. 

 The p-value for Test 6b is less than .05. Model 4 appears 

 to fit the data better than Model 2. 

 The p-value for Test 7a is greater than .1. Model 5 seems 

 to adequately describe the data. 

 The p-value for Test 7b is less than .05. Model 5 appears 

 to fit the data better than Model 3. 

 Degrees of freedom for Test 7c are less than or equal to 0. 
Page 403 of 438 

 The Chi-Square test for fit is not valid. 

 Benchmark Dose Computations: 

 Specified Effect = 0.100000 

 Risk Type = Relative deviation 

 Confidence Level = 0.950000 

 BMD and BMDL by Model 

 Model BMD BMDL 

 ------- ------------ ---------- 

 2 10.8248 8.91619 

 3 10.8248 8.91619 

 4 3.64832 2.54018 

 5 3.64832 2.54018 
Page 404 of 438 

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Page 407 of 438 

 Inhalation Study

Male Brain ChE – 28-day Inhalation – MRID 48146702 

!!! Warning: optimum may not have been found for Model 3 !!! 

!!! Bad completion code in maximum likelihood optimization routine !!! 

!!! Try choosing different initial values !!! 

Warning: optimum may not have been found form model 2 (BMDL). Bad process completion 

in Optimization routine. 

 ==================================================================== 

 Exponential Model. (Version: 1.7; Date: 12/10/2009) 

 Input Data File: C:/Documents and Settings/jliccion/My 

Documents/BMDS220/Data/exp_UntitledData_Setting.(d) 

 Gnuplot Plotting File: 

 Mon Jul 30 14:12:00 2012 

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Page 408 of 438 

 ==================================================================== 

 BMDS Model Run 

~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ 

 

 The form of the response function by Model: 

 Model 2: Y[dose] = a * exp{sign * b * dose} 

 Model 3: Y[dose] = a * exp{sign * (b * dose)^d} 

 Model 4: Y[dose] = a * [c-(c-1) * exp{-b * dose}] 

 Model 5: Y[dose] = a * [c-(c-1) * exp{-(b * dose)^d}] 

 Note: Y[dose] is the median response for exposure = dose; 

 sign = +1 for increasing trend in data; 

 sign = -1 for decreasing trend. 

 Model 2 is nested within Models 3 and 4. 

 Model 3 is nested within Model 5. 

 Model 4 is nested within Model 5. 

 Dependent variable = Mean 

 Independent variable = Dose 

 Data are assumed to be distributed: normally 

 Variance Model: exp(lnalpha +rho *ln(Y[dose])) 

 The variance is to be modeled as Var(i) = exp(lalpha + log(mean(i)) * rho) 

 Total number of dose groups = 4 

 Total number of records with missing values = 0 

 Maximum number of iterations = 250 

 Relative Function Convergence has been set to: 1e-008 

 Parameter Convergence has been set to: 1e-008 

 MLE solution provided: Exact 

 Initial Parameter Values 

 Variable Model 2 Model 3 Model 4 Model 5 

 -------- ------- ------- ------- ------- 

 lnalpha 10.4647 10.4647 10.4647 10.4647 

 rho 0.302693 0.302693 0.302693 0.302693 

 a 4595.58 4595.58 24486 24486 

 b 0.977116 0.977116 0.915869 0.505406 

 c -- -- 6.19129e-005 

0.0589647 

 d -- 1 -- 2 

 Parameter Estimates by Model 

 Variable Model 2 Model 3 Model 4 Model 5 

 -------- ------- ------- ------- ------- 

 lnalpha 77.6412 10.4647 77.6412 9.37824 

 rho -6.32986 0.302693 -6.32986 0.439104 

 a 23644.7 4595.58 23644.7 23323.4 

 b 0.239859 0.977116 0.239859 0.538103 

 c -- -- 0 0 

 d -- 1 -- 2.25879 

 Table of Stats From Input Data 
Page 409 of 438 

 Dose N Obs Mean Obs Std Dev 

 ----- --- ---------- ------------- 

 0 10 2.332e+004 1495 

 0.097 10 2.329e+004 874.2 

 0.73 10 2.067e+004 461.8 

 2.9 10 1516 582.5 

 Estimated Values of Interest 

 Model Dose Est Mean Est Std Scaled Residual 

 ------- ------ ---------- --------- ---------------- 

 2 0 2.364e+004 1040 -0.9875 

 0.097 2.31e+004 1119 0.534 

 0.73 1.985e+004 1810 1.438 

 2.9 1.179e+004 9399 -3.458 

 3 0 0 4.026e-045 1.832e+049 

 0.097 0 4.026e-045 1.83e+049 

 0.73 0 4.026e-045 1.624e+049 

 2.9 0 4.026e-045 1.191e+048 

 4 0 2.364e+004 1040 -0.9875 

 0.097 2.31e+004 1119 0.534 

 0.73 1.985e+004 1810 1.438 

 2.9 1.179e+004 9399 -3.458 

 5 0 2.332e+004 989.5 -0.01075 

 0.097 2.329e+004 989.2 -0.01211 

 0.73 2.066e+004 963.5 0.02627 

 2.9 1518 543.1 -0.008893 

 Other models for which likelihoods are calculated: 

 Model A1: Yij = Mu(i) + e(ij) 

 Var{e(ij)} = Sigma^2 

 Model A2: Yij = Mu(i) + e(ij) 

 Var{e(ij)} = Sigma(i)^2 

 Model A3: Yij = Mu(i) + e(ij) 

 Var{e(ij)} = exp(lalpha + log(mean(i)) * rho) 

 Model R: Yij = Mu + e(i) 

 Var{e(ij)} = Sigma^2 

 Likelihoods of Interest 

 Model Log(likelihood) DF AIC 

 ------- ----------------- ---- ------------ 

 A1 -291.8297 5 593.6593 

 A2 -283.7506 8 583.5013 

 A3 -289.6198 6 591.2396 

 R -384.9128 2 773.8255 

 2 -326.1689 4 660.3377 

 3 -13081.25 4 26170.51 

 4 -326.1689 4 660.3377 

 5 -289.6202 5 589.2405 

 Additive constant for all log-likelihoods = -36.76. This constant added to the 

 above values gives the log-likelihood including the term that does not 

 depend on the model parameters. 
Page 410 of 438 

 Explanation of Tests 

 Test 1: Does response and/or variances differ among Dose levels? (A2 vs. R) 

 Test 2: Are Variances Homogeneous? (A2 vs. A1) 

 Test 3: Are variances adequately modeled? (A2 vs. A3) 

 Test 4: Does Model 2 fit the data? (A3 vs. 2) 

 Test 5a: Does Model 3 fit the data? (A3 vs 3) 

 Test 5b: Is Model 3 better than Model 2? (3 vs. 2) 

 Test 6a: Does Model 4 fit the data? (A3 vs 4) 

 Test 6b: Is Model 4 better than Model 2? (4 vs. 2) 

 Test 7a: Does Model 5 fit the data? (A3 vs 5) 

 Test 7b: Is Model 5 better than Model 3? (5 vs. 3) 

 Test 7c: Is Model 5 better than Model 4? (5 vs. 4) 

 Tests of Interest 

 Test -2*log(Likelihood Ratio) D. F. p-value 

 -------- ------------------------ ------ -------------- 

 Test 1 202.3 6 < 0.0001 

 Test 2 16.16 3 0.001052 

 Test 3 11.74 2 0.002825 

 Test 4 73.1 2 < 0.0001 

 Test 5a 2.558e+004 2 < 0.0001 

 Test 5b -2.551e+004 0 N/A 

 Test 6a 73.1 2 < 0.0001 

 Test 6b 1.137e-013 0 N/A 

 Test 7a 0.0008484 1 0.9768 

 Test 7b 2.558e+004 1 < 0.0001 

 Test 7c 73.1 1 < 0.0001 

 The p-value for Test 1 is less than .05. There appears to be a 

 difference between response and/or variances among the dose 

 levels, it seems appropriate to model the data. 

 The p-value for Test 2 is less than .1. A non-homogeneous 

 variance model appears to be appropriate. 

 The p-value for Test 3 is less than .1. You may want to 

 consider a different variance model. 

 The p-value for Test 4 is less than .1. Model 2 may not adequately 

 describe the data; you may want to consider another model. 

 The p-value for Test 5a is less than .1. Model 3 may not adequately 

 describe the data; you may want to consider another model. 

 Degrees of freedom for Test 5b are less than or equal to 0. 

 The Chi-Square test for fit is not valid. 

 The p-value for Test 6a is less than .1. Model 4 may not adequately 

 describe the data; you may want to consider another model. 

 Degrees of freedom for Test 6b are less than or equal to 0. 

 The Chi-Square test for fit is not valid. 

 The p-value for Test 7a is greater than .1. Model 5 seems 
Page 411 of 438 

 to adequately describe the data. 

 The p-value for Test 7b is less than .05. Model 5 appears 

 to fit the data better than Model 3. 

 The p-value for Test 7c is less than .05. Model 5 appears 

 to fit the data better than Model 4. 

 Benchmark Dose Computations: 

 Specified Effect = 0.100000 

 Risk Type = Relative deviation 

 Confidence Level = 0.950000 

 BMD and BMDL by Model 

 Model BMD BMDL 

 ------- ------------ ---------- 

 2 0.43926 0.3305 

 3 0.107828 Bad completion 

 4 0.43926 0.3305 

 5 0.686213 0.614588 
Page 412 of 438 

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Page 413 of 438 

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Page 415 of 438 

Female Brain ChE – 28-day Inhalation – MRID 48146702 

==================================================================

== 

 Exponential Model. (Version: 1.7; Date: 12/10/2009) 

 Input Data File: C:/Documents and Settings/jliccion/My 

Documents/BMDS220/Data/exp_UntitledData_Setting.(d) 

 Gnuplot Plotting File: 

 Mon Jul 30 14:22:33 2012 

==================================================================

== 

 BMDS Model Run 

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Page 416 of 438 

~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

~~~ 

 

 The form of the response function by Model: 

 Model 2: Y[dose] = a * exp{sign * b * dose} 

 Model 3: Y[dose] = a * exp{sign * (b * dose)^d} 

 Model 4: Y[dose] = a * [c-(c-1) * exp{-b * dose}] 

 Model 5: Y[dose] = a * [c-(c-1) * exp{-(b * dose)^d}] 

 Note: Y[dose] is the median response for exposure = dose; 

 sign = +1 for increasing trend in data; 

 sign = -1 for decreasing trend. 

 Model 2 is nested within Models 3 and 4. 

 Model 3 is nested within Model 5. 

 Model 4 is nested within Model 5. 

 Dependent variable = Mean 

 Independent variable = Dose 

 Data are assumed to be distributed: normally 

 Variance Model: exp(lnalpha +rho *ln(Y[dose])) 

 The variance is to be modeled as Var(i) = exp(lalpha + log(mean(i)) * rho) 

 Total number of dose groups = 4 

 Total number of records with missing values = 0 

 Maximum number of iterations = 250 

 Relative Function Convergence has been set to: 1e-008 

 Parameter Convergence has been set to: 1e-008 

 MLE solution provided: Exact 

 Initial Parameter Values 

 Variable Model 2 Model 3 Model 4 Model 5 

 -------- ------- ------- ------- ------- 

 lnalpha 1.49669 1.49669 1.49669 1.49669 

 rho 1.20941 1.20941 1.20941 1.20941 

 a 17653.1 17653.1 25651.5 25651.5 

 b 0.156663 0.156663 0.942468 0.942468 

 c -- -- 0.560257 0.560257 

 d -- 1 -- 1 
Page 417 of 438 

 Parameter Estimates by Model 

 Variable Model 2 Model 3 Model 4 Model 5 

 -------- ------- ------- ------- ------- 

 lnalpha -8.24114 1.49669 -8.11446 -8.11446 

 rho 2.23232 1.20941 2.21045 2.21045 

 a 23640.9 17653.1 23955.3 23955.3 

 b 0.156554 0.156663 0.453224 0.453224 

 c -- -- 0.494794 0.494794 

 d -- 1 -- 1 

 Table of Stats From Input Data 

 Dose N Obs Mean Obs Std Dev 

 ----- --- ---------- ------------- 

 0 10 2.443e+004 1667 

 0.097 10 2.284e+004 613.8 

 0.73 10 2.068e+004 616 

 2.9 10 1.509e+004 843.7 

 Estimated Values of Interest 

 Model Dose Est Mean Est Std Scaled Residual 

 ------- ------ ---------- --------- ---------------- 

 2 0 2.364e+004 1236 2.018 

 0.097 2.328e+004 1216 -1.157 

 0.73 2.109e+004 1088 -1.185 

 2.9 1.501e+004 744.9 0.3235 

 3 0 0.01028 0.1327 5.822e+005 

 0.097 0.01024 0.1324 5.456e+005 

 0.73 0.00998 0.1303 5.018e+005 

 2.9 0.009139 0.1236 3.862e+005 

 4 0 2.396e+004 1197 1.254 

 0.097 2.343e+004 1169 -1.61 

 0.73 2.055e+004 1010 0.4187 

 2.9 1.51e+004 719.1 -0.06267 

 5 0 2.396e+004 1197 1.254 

 0.097 2.343e+004 1169 -1.61 

 0.73 2.055e+004 1010 0.4187 

 2.9 1.51e+004 719.1 -0.06267 

 Other models for which likelihoods are calculated: 
Page 418 of 438 

 Model A1: Yij = Mu(i) + e(ij) 

 Var{e(ij)} = Sigma^2 

 Model A2: Yij = Mu(i) + e(ij) 

 Var{e(ij)} = Sigma(i)^2 

 Model A3: Yij = Mu(i) + e(ij) 

 Var{e(ij)} = exp(lalpha + log(mean(i)) * rho) 

 Model R: Yij = Mu + e(i) 

 Var{e(ij)} = Sigma^2 

 Likelihoods of Interest 

 Model Log(likelihood) DF AIC 

 ------- ----------------- ---- ------------ 

 A1 -295.3979 5 600.7958 

 A2 -287.8865 8 591.7731 

 A3 -293.7705 6 599.5409 

 R -348.2795 2 700.5589 

 2 -298.286 4 604.5721 

 3 -1364.391 4 2736.782 

 4 -296.4755 5 602.9511 

 5 -296.4755 5 602.9511 

 Additive constant for all log-likelihoods = -36.76. This constant added to the 

 above values gives the log-likelihood including the term that does not 

 depend on the model parameters. 

 Explanation of Tests 

 Test 1: Does response and/or variances differ among Dose levels? (A2 vs. R) 

 Test 2: Are Variances Homogeneous? (A2 vs. A1) 

 Test 3: Are variances adequately modeled? (A2 vs. A3) 

 Test 4: Does Model 2 fit the data? (A3 vs. 2) 

 Test 5a: Does Model 3 fit the data? (A3 vs 3) 

 Test 5b: Is Model 3 better than Model 2? (3 vs. 2) 

 Test 6a: Does Model 4 fit the data? (A3 vs 4) 

 Test 6b: Is Model 4 better than Model 2? (4 vs. 2) 
Page 419 of 438 

 Test 7a: Does Model 5 fit the data? (A3 vs 5) 

 Test 7b: Is Model 5 better than Model 3? (5 vs. 3) 

 Test 7c: Is Model 5 better than Model 4? (5 vs. 4) 

 Tests of Interest 

 Test -2*log(Likelihood Ratio) D. F. p-value 

 -------- ------------------------ ------ -------------- 

 Test 1 120.8 6 < 0.0001 

 Test 2 15.02 3 0.001797 

 Test 3 11.77 2 0.002784 

 Test 4 9.031 2 0.01094 

 Test 5a 2141 2 < 0.0001 

 Test 5b -2132 0 N/A 

 Test 6a 5.41 1 0.02002 

 Test 6b 3.621 1 0.05705 

 Test 7a 5.41 1 0.02002 

 Test 7b 2136 1 < 0.0001 

 Test 7c -1.137e-013 0 N/A 

 The p-value for Test 1 is less than .05. There appears to be a 

 difference between response and/or variances among the dose 

 levels, it seems appropriate to model the data. 

 The p-value for Test 2 is less than .1. A non-homogeneous 

 variance model appears to be appropriate. 

 The p-value for Test 3 is less than .1. You may want to 

 consider a different variance model. 

 The p-value for Test 4 is less than .1. Model 2 may not adequately 

 describe the data; you may want to consider another model. 

 The p-value for Test 5a is less than .1. Model 3 may not adequately 

 describe the data; you may want to consider another model. 

 Degrees of freedom for Test 5b are less than or equal to 0. 

 The Chi-Square test for fit is not valid. 

 The p-value for Test 6a is less than .1. Model 4 may not adequately 

 describe the data; you may want to consider another model. 

 The p-value for Test 6b is greater than .05. Model 4 does 

 not seem to fit the data better than Model 2. 
Page 420 of 438 

 The p-value for Test 7a is less than .1. Model 5 may not adequately 

 describe the data; you may want to consider another model. 

 The p-value for Test 7b is less than .05. Model 5 appears 

 to fit the data better than Model 3. 

 Degrees of freedom for Test 7c are less than or equal to 0. 

 The Chi-Square test for fit is not valid. 

 Benchmark Dose Computations: 

 Specified Effect = 0.100000 

 Risk Type = Relative deviation 

 Confidence Level = 0.950000 

 BMD and BMDL by Model 

 Model BMD BMDL 

 ------- ------------ ---------- 

 2 0.672996 0.624367 

 3 0.672528 0.00931339 

 4 0.48667 0.381665 

 5 0.48667 0.381665 
Page 421 of 438 

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10:36 08/20 2012

BMDL BMD

 

Exponential

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10:36 08/20 2012

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Exponential
Page 422 of 438 

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Exponential
Page 423 of 438 

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Exponential
Page 424 of 438 

Male RBC ChE – 28-day Inhalation – MRID 48146702 

 ==================================================================== 

 Exponential Model. (Version: 1.7; Date: 12/10/2009) 

 Input Data File: C:/Usepa/BMDS212/Data/exp_Testrunscontinuous_Setting.(d) 

 Gnuplot Plotting File: 

 Wed Apr 11 13:03:04 2012 

 ==================================================================== 

 BMDS Model Run 

~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ 

 

 The form of the response function by Model: 

 Model 2: Y[dose] = a * exp{sign * b * dose} 

 Model 3: Y[dose] = a * exp{sign * (b * dose)^d} 

 Model 4: Y[dose] = a * [c-(c-1) * exp{-b * dose}] 

 Model 5: Y[dose] = a * [c-(c-1) * exp{-(b * dose)^d}] 

 Note: Y[dose] is the median response for exposure = dose; 

 sign = +1 for increasing trend in data; 

 sign = -1 for decreasing trend. 

 Model 2 is nested within Models 3 and 4. 

 Model 3 is nested within Model 5. 

 Model 4 is nested within Model 5. 

 Dependent variable = Mean 

 Independent variable = Dose 

 Data are assumed to be distributed: normally 

 Variance Model: exp(lnalpha +rho *ln(Y[dose])) 

 rho is set to 0. 

 A constant variance model is fit. 

 Total number of dose groups = 4 

 Total number of records with missing values = 0 

 Maximum number of iterations = 250 

 Relative Function Convergence has been set to: 1e-008 

 Parameter Convergence has been set to: 1e-008 

 MLE solution provided: Exact 

 Initial Parameter Values 

 Variable Model 2 Model 3 Model 4 Model 5 

 -------- ------- ------- ------- ------- 

 lnalpha 11.0663 11.0663 11.0663 11.0663 

 rho(S) 0 0 0 0 

 a 1202.55 1202.55 2063.25 2063.25 

 b 0.246761 0.246761 0.429754 0.429754 

 c -- -- 0.226827 

0.226827 

 d -- 1 -- 1 

 (S) = Specified 

 Parameter Estimates by Model 

 Variable Model 2 Model 3 Model 4 Model 5 

 -------- ------- ------- ------- ------- 
Page 425 of 438 

 lnalpha 11.0874 11.0874 11.0798 11.0798 

 rho 0 0 0 0 

 a 1910.86 1910.86 1928.22 1928.22 

 b 0.251628 0.251628 0.432846 0.432846 

 c -- -- 0.281002 0.281002 

 d -- 1 -- 1 

 Table of Stats From Input Data 

 Dose N Obs Mean Obs Std Dev 

 ----- --- ---------- ------------- 

 0 10 1965 290.6 

 0.097 10 1826 249 

 0.73 10 1562 338.9 

 2.9 10 936 151.8 

 Estimated Values of Interest 

 Model Dose Est Mean Est Std Scaled Residual 

 ------- ------ ---------- --------- ---------------- 

 2 0 1911 255.6 0.6698 

 0.097 1865 255.6 -0.4798 

 0.73 1590 255.6 -0.3489 

 2.9 921.1 255.6 0.1841 

 3 0 1911 255.6 0.6698 

 0.097 1865 255.6 -0.4798 

 0.73 1590 255.6 -0.3489 

 2.9 921.1 255.6 0.1841 

 4 0 1928 254.7 0.4568 

 0.097 1871 254.7 -0.5615 

 0.73 1553 254.7 0.1166 

 2.9 937 254.7 -0.01189 

 5 0 1928 254.7 0.4568 

 0.097 1871 254.7 -0.5615 

 0.73 1553 254.7 0.1166 

 2.9 937 254.7 -0.01189 

 Other models for which likelihoods are calculated: 

 Model A1: Yij = Mu(i) + e(ij) 

 Var{e(ij)} = Sigma^2 

 Model A2: Yij = Mu(i) + e(ij) 

 Var{e(ij)} = Sigma(i)^2 

 Model A3: Yij = Mu(i) + e(ij) 

 Var{e(ij)} = exp(lalpha + log(mean(i)) * rho) 

 Model R: Yij = Mu + e(i) 

 Var{e(ij)} = Sigma^2 

 Likelihoods of Interest 

 Model Log(likelihood) DF AIC 

 ------- ----------------- ---- ------------ 

 A1 -241.3258 5 492.6517 

 A2 -238.2695 8 492.539 

 A3 -241.3258 5 492.6517 
Page 426 of 438 

 R -266.0157 2 536.0313 

 2 -241.7475 3 489.495 

 3 -241.7475 3 489.495 

 4 -241.5965 4 491.1929 

 5 -241.5965 4 491.1929 

 Additive constant for all log-likelihoods = -36.76. This constant added to the 

 above values gives the log-likelihood including the term that does not 

 depend on the model parameters. 

 Explanation of Tests 

 Test 1: Does response and/or variances differ among Dose levels? (A2 vs. R) 

 Test 2: Are Variances Homogeneous? (A2 vs. A1) 

 Test 3: Are variances adequately modeled? (A2 vs. A3) 

 Test 4: Does Model 2 fit the data? (A3 vs. 2) 

 Test 5a: Does Model 3 fit the data? (A3 vs 3) 

 Test 5b: Is Model 3 better than Model 2? (3 vs. 2) 

 Test 6a: Does Model 4 fit the data? (A3 vs 4) 

 Test 6b: Is Model 4 better than Model 2? (4 vs. 2) 

 Test 7a: Does Model 5 fit the data? (A3 vs 5) 

 Test 7b: Is Model 5 better than Model 3? (5 vs. 3) 

 Test 7c: Is Model 5 better than Model 4? (5 vs. 4) 

 Tests of Interest 

 Test -2*log(Likelihood Ratio) D. F. p-value 

 -------- ------------------------ ------ -------------- 

 Test 1 55.49 6 < 0.0001 

 Test 2 6.113 3 0.1063 

 Test 3 6.113 3 0.1063 

 Test 4 0.8433 2 0.656 

 Test 5a 0.8433 2 0.656 

 Test 5b 0 0 N/A 

 Test 6a 0.5412 1 0.4619 

 Test 6b 0.3021 1 0.5826 

 Test 7a 0.5412 1 0.4619 

 Test 7b 0.3021 1 0.5826 

 Test 7c -5.684e-014 0 N/A 

 The p-value for Test 1 is less than .05. There appears to be a 

 difference between response and/or variances among the dose 

 levels, it seems appropriate to model the data. 

 The p-value for Test 2 is greater than .1. A homogeneous 

 variance model appears to be appropriate here. 

 The p-value for Test 3 is greater than .1. The modeled 

 variance appears to be appropriate here. 

 The p-value for Test 4 is greater than .1. Model 2 seems 

 to adequately describe the data. 

 The p-value for Test 5a is greater than .1. Model 3 seems 

 to adequately describe the data. 
Page 427 of 438 

 Degrees of freedom for Test 5b are less than or equal to 0. 

 The Chi-Square test for fit is not valid. 

 The p-value for Test 6a is greater than .1. Model 4 seems 

 to adequately describe the data. 

 The p-value for Test 6b is greater than .05. Model 4 does 

 not seem to fit the data better than Model 2. 

 The p-value for Test 7a is greater than .1. Model 5 seems 

 to adequately describe the data. 

 The p-value for Test 7b is greater than .05. Model 5 does 

 not seem to fit the data better than Model 3. 

 Degrees of freedom for Test 7c are less than or equal to 0. 

 The Chi-Square test for fit is not valid. 

 Benchmark Dose Computations: 

 Specified Effect = 0.100000 

 Risk Type = Relative deviation 

 Confidence Level = 0.950000 

 BMD and BMDL by Model 

 Model BMD BMDL 

 ------- ------------ ---------- 

 2 0.418716 0.341868 

 3 0.418716 0.341868 

 4 0.345981 0.207226 

 5 0.345981 0.207226 
Page 428 of 438 

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Female RBC ChE – 28-day Inhalation – MRID 48146702

 ==================================================================== 

 Exponential Model. (Version: 1.7; Date: 12/10/2009) 

 Input Data File: C:/Usepa/BMDS212/Data/exp_Testrunscontinuous_Setting.(d) 

 Gnuplot Plotting File: 

 Mon Apr 16 10:04:10 2012 

 ==================================================================== 

 BMDS Model Run 

~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ 

 

 The form of the response function by Model: 

 Model 2: Y[dose] = a * exp{sign * b * dose} 

 Model 3: Y[dose] = a * exp{sign * (b * dose)^d} 

 Model 4: Y[dose] = a * [c-(c-1) * exp{-b * dose}] 

 Model 5: Y[dose] = a * [c-(c-1) * exp{-(b * dose)^d}] 

 Note: Y[dose] is the median response for exposure = dose; 

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 sign = +1 for increasing trend in data; 

 sign = -1 for decreasing trend. 

 Model 2 is nested within Models 3 and 4. 

 Model 3 is nested within Model 5. 

 Model 4 is nested within Model 5. 

 Dependent variable = Mean 

 Independent variable = Dose 

 Data are assumed to be distributed: normally 

 Variance Model: exp(lnalpha +rho *ln(Y[dose])) 

 rho is set to 0. 

 A constant variance model is fit. 

 Total number of dose groups = 4 

 Total number of records with missing values = 0 

 Maximum number of iterations = 250 

 Relative Function Convergence has been set to: 1e-008 

 Parameter Convergence has been set to: 1e-008 

 MLE solution provided: Exact 

 Initial Parameter Values 

 Variable Model 2 Model 3 Model 4 Model 5 

 -------- ------- ------- ------- ------- 

 lnalpha 11.9528 11.9528 11.9528 11.9528 

 rho(S) 0 0 0 0 

 a 1549.38 1549.38 2233.35 2233.35 

 b 0.141393 0.141393 0.935475 0.935475 

 c -- -- 0.5791 

0.5791 

 d -- 1 -- 1 

 (S) = Specified 

 Parameter Estimates by Model 

 Variable Model 2 Model 3 Model 4 Model 5 

 -------- ------- ------- ------- ------- 

 lnalpha 11.9887 11.9887 11.9602 11.9602 

 rho 0 0 0 0 

 a 2027.8 2027.8 2086.01 2086.01 

 b 0.147815 0.147815 0.922103 0.922103 

 c -- -- 0.626407 0.626407 

 d -- 1 -- 1 

 Table of Stats From Input Data 

 Dose N Obs Mean Obs Std Dev 

 ----- --- ---------- ------------- 

 0 10 2127 425.4 

 0.097 10 1967 282.5 

 0.73 10 1718 321.6 

 2.9 10 1358 570.8 

 Estimated Values of Interest 
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 Model Dose Est Mean Est Std Scaled Residual 

 ------- ------ ---------- --------- ---------------- 

 2 0 2028 401.2 0.782 

 0.097 1999 401.2 -0.2517 

 0.73 1820 401.2 -0.8071 

 2.9 1321 401.2 0.2927 

 3 0 2028 401.2 0.782 

 0.097 1999 401.2 -0.2517 

 0.73 1820 401.2 -0.8071 

 2.9 1321 401.2 0.2927 

 4 0 2086 395.5 0.3278 

 0.097 2019 395.5 -0.4184 

 0.73 1704 395.5 0.1101 

 2.9 1360 395.5 -0.0195 

 5 0 2086 395.5 0.3278 

 0.097 2019 395.5 -0.4184 

 0.73 1704 395.5 0.1101 

 2.9 1360 395.5 -0.0195 

 Other models for which likelihoods are calculated: 

 Model A1: Yij = Mu(i) + e(ij) 

 Var{e(ij)} = Sigma^2 

 Model A2: Yij = Mu(i) + e(ij) 

 Var{e(ij)} = Sigma(i)^2 

 Model A3: Yij = Mu(i) + e(ij) 

 Var{e(ij)} = exp(lalpha + log(mean(i)) * rho) 

 Model R: Yij = Mu + e(i) 

 Var{e(ij)} = Sigma^2 

 Likelihoods of Interest 

 Model Log(likelihood) DF AIC 

 ------- ----------------- ---- ------------ 

 A1 -259.0561 5 528.1123 

 A2 -256.0633 8 528.1267 

 A3 -259.0561 5 528.1123 

 R -267.7197 2 539.4394 

 2 -259.7748 3 525.5497 

 3 -259.7748 3 525.5497 

 4 -259.2042 4 526.4084 

 5 -259.2042 4 526.4084 

 Additive constant for all log-likelihoods = -36.76. This constant added to the 

 above values gives the log-likelihood including the term that does not 

 depend on the model parameters. 

 Explanation of Tests 

 Test 1: Does response and/or variances differ among Dose levels? (A2 vs. R) 

 Test 2: Are Variances Homogeneous? (A2 vs. A1) 

 Test 3: Are variances adequately modeled? (A2 vs. A3) 

 Test 4: Does Model 2 fit the data? (A3 vs. 2) 
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 Test 5a: Does Model 3 fit the data? (A3 vs 3) 

 Test 5b: Is Model 3 better than Model 2? (3 vs. 2) 

 Test 6a: Does Model 4 fit the data? (A3 vs 4) 

 Test 6b: Is Model 4 better than Model 2? (4 vs. 2) 

 Test 7a: Does Model 5 fit the data? (A3 vs 5) 

 Test 7b: Is Model 5 better than Model 3? (5 vs. 3) 

 Test 7c: Is Model 5 better than Model 4? (5 vs. 4) 

 Tests of Interest 

 Test -2*log(Likelihood Ratio) D. F. p-value 

 -------- ------------------------ ------ -------------- 

 Test 1 23.31 6 0.0006982 

 Test 2 5.986 3 0.1123 

 Test 3 5.986 3 0.1123 

 Test 4 1.437 2 0.4874 

 Test 5a 1.437 2 0.4874 

 Test 5b -4.093e-012 0 N/A 

 Test 6a 0.2961 1 0.5863 

 Test 6b 1.141 1 0.2854 

 Test 7a 0.2961 1 0.5863 

 Test 7b 1.141 1 0.2854 

 Test 7c 0 0 N/A 

 The p-value for Test 1 is less than .05. There appears to be a 

 difference between response and/or variances among the dose 

 levels, it seems appropriate to model the data. 

 The p-value for Test 2 is greater than .1. A homogeneous 

 variance model appears to be appropriate here. 

 The p-value for Test 3 is greater than .1. The modeled 

 variance appears to be appropriate here. 

 The p-value for Test 4 is greater than .1. Model 2 seems 

 to adequately describe the data. 

 The p-value for Test 5a is greater than .1. Model 3 seems 

 to adequately describe the data. 

 Degrees of freedom for Test 5b are less than or equal to 0. 

 The Chi-Square test for fit is not valid. 

 The p-value for Test 6a is greater than .1. Model 4 seems 

 to adequately describe the data. 

 The p-value for Test 6b is greater than .05. Model 4 does 

 not seem to fit the data better than Model 2. 

 The p-value for Test 7a is greater than .1. Model 5 seems 

 to adequately describe the data. 

 The p-value for Test 7b is greater than .05. Model 5 does 

 not seem to fit the data better than Model 3. 

 Degrees of freedom for Test 7c are less than or equal to 0. 

 The Chi-Square test for fit is not valid. 
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 Benchmark Dose Computations: 

 Specified Effect = 0.100000 

 Risk Type = Relative deviation 

 Confidence Level = 0.950000 

 BMD and BMDL by Model 

 Model BMD BMDL 

 ------- ------------ ---------- 

 2 0.712784 0.491602 

 3 0.712785 0.491602 

 4 0.337843 0.113152 

 5 0.337843 0.113152 

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