Appendix G: Statistical Procedures

Appendix G, Section 1: Expanded Summary

All statistics for the MMT protocols were done with StatCHARRMS (Statistical analysis of Chemistry, Histopathology, And Reproduction endpoints including Repeated measures and Multi

generation Studies), a SAS (SAS Institute, Cary, NC)

- based program developed for the Mid

Continent Ecology Division of the USEPA, NHEEL b y John Green (Dupont Applied Statistics Group, Newark, DE). Generally speaking, StatCHARRMS analyzes data of three different types: 1) continuous data that is not a repeated measure (fecundity and fertility are the only repeated measures) hereby termed ‘e ndpoint’ data, 2) fecundity and fertility data which is a repeated measure, and 3) histopathology data, which usually is in the form of severity scores. For these three types, StatCHARRMS analyzes the data in a distinct fashion for each.

In a multi

- gener ation study for a continuous response measured only once per subject, such as liver weight, there are only two main effects, treatment and generation. If the generation

- by

treatment interaction is significant at the 0.05 level, then generations are compare d within treatments and treatments are compared within generations at the 0.05 significance level through contrasts without further adjustment to the p

- values. If the interaction is not significant, then such contrasts

are still tested but Bonferroni

- Holm adjusted p

- values are used, with separate adjustments for generation and treatment comparisons. A main effects test for generation is done with no adjustment and there is only one comparison made unless the F0 generation is included. Main effects tests fo r treatment use Dunnett’s test, which requires no further adjustment.

In a one

- generation study, treatment is the only factor and Dunnett’s test can be used to test for treatment effects for a continuous response. Alternatively, the step

- down Jonckheere

- T erpstra
test can be used. The Jonckheere

- Terpstra test is appropriate if the data are consistent with a

monotone concentration

- response. Otherwise, Dunnett’s test is used. Egg production can be analyzed as a continuous response by a repeated measures ANO VA, with week (or day) the repeated factor and treatment and generation as fixed effect factors. Generation is not a repeated factor for two primary reasons. First, no individual is measured in more than one generation. Second, while in the abstract it mig ht be possible to make the vessel the unit of analysis if the same vessel were used for the F0 generation and for all progeny of the F0 parents in that vessel and all progeny of the resulting F1 generation, the logistics of such a design are

inefficient. I n fact, the eggs from the F0 generation are randomized into new vessels to form the F1 generation and the eggs from F1 are randomized to form F2. Thus, there is no discernible experimental unit that is being measured repeatedly over generations. Usually, only the F1 and F2 generations are analyzed, as both of these generations were exposed to the chemical throughout their entire lives, whereas the F0 generation was first exposed in the egg state or later. Tests for differences across generations and diff erences across weeks within each generation, as well as treatment effects within and across generations are done using contrasts. The analysis begins with evaluations of the requirements for ANOVA, namely normality (Shapiro

- Wilk

and Anderson

- Darling tests) and variance homogeneity (Levene test). Transformations of variables are applied to achieve these requirements where the original data do not. Where no other transformation is found, analysis is based on a normalized rank

- order transform. The analysis wi th all generations included tests for differences (1) between generations averaged across weeks and groups, (2) between groups averaged across generations and weeks, (3) between weeks averaged across generations and groups, (4) between groups within the sa me generation, (5) between groups within the same generation and week, (6) between weeks within the same group and generation, and (7) between generations within the same group and week. If every such comparison is made with a 5% false positive rate, ther e is a very high probability of some false positives occurring. To control the false positive rate, some adjustment is needed for the number of comparisons. However, it is not appropriate to use a crude Bonferroni or Bonferroni

- Holm type adjustment for al l comparisons simultaneously, for this would grossly inflate the false negative rate.The adjustments are primarily based on the ANOVA F

- tests, except main effects of treatment are tested using the Dunnett test, which adjusts for the number of comparisons independent of any F

- test. For the main effect of generation or week, there is no “control” level and every pair of levels is a comparison of possible interest. For these two main effects, if the F

- test for the main effect is significant at the 0.05 lev el, then the pairwise comparisons across levels of that factor can then be tested at the 0.05 level without further adjustment. However, the model includes two

- and three

- factor interactions, so that a main effect for, say, generation, may not be signific ant even though generation has a significant impact on results. Thus, if a two

- or three

- factor interaction involving generation is significant at the 0.05 level, then comparisons of levels of generation at the 0.05 significance level are done without furt her adjustment. Similar statements can be made for week.

Next are F

- tests for significance of treatment within weeks and generations, the so

- called ‘slices’ in the ANOVA table. If, for example, the slice for treatment within generation F1 and week

12 is significant at the 0.05 level, then the pairwise comparisons for treatment within that generation and week are made at the 0.05 level without further adjustment. Similar statements apply to tests for week within a generation and treatment and for generatio n within a week and treatment. Finally, for comparisons not covered by a significant slice F

- test, comparisons are adjusted using the Bonferroni

- Holm adjustment to p

- values. Separate adjustments are made for tests of each

factor.

The model includes all m ain effects, two

- factor interactions, and the three

- factor interaction. It is quite likely that some of these interactions and main effects will not be significant. It is possible to implement a scheme that would first eliminate the three

- factor interact ion if it is not significant, then eliminate the least significant two

- factor interaction (if not significant at the .05 level) and continue in sequential fashion until only significant factors and interactions remain in the model. Only if no interaction involving a factor is significant, would a non

- significant main effect

be eliminated. There are several problems with the above procedure. First, eliminating a term at step 3, for example, might make a term eliminated at step 2 now significant. Thus a st epwise procedure might be adopted, such as used in stepwise regression. This can be done if the model is formulated carefully, though it can be tedious and can produce misleading results. There is another problem that arises from the need to make multiple comparisons beyond the ANOVA F

- tests. It can be quite challenging to determine how to test a comparison of interest when some interactions are eliminated from the model. The most straightforward procedure is to leave all terms in the model so as to

accomm odate the various comparisons of interest. There is nothing wrong with retaining a non

significant term in the final model. This could be a problem with a very complex model with a large number of potential explanatory variables that should be reduced to a manageable number. In the present case, there are only three basic model terms, generation, week, and treatment, all of which have very clear meaning and do not overlap. Moreover, it is entirely plausible for all factors to be important, and for egg pr oduction to vary across weeks within the same treatment group and for different generations to show different responses to the treatments. It thus is justifiable to retain all model terms. This will also allow easier comparisons of chemicals, as all are m odeled in the same

way. The same general procedure can be followed if a single generation is analyzed. In this case, there are two main effects, treatment and week, and a single two

- factor interaction. Dunnett’s test still controls the false positive rate for comparisons of treatment main effects, i.e., treatments averaged across weeks. For main effects of weeks, i.e., comparisons of weeks averaged across treatments, if either the week main effect or the week

- by

- treatment interaction is significant at the 0.05 level, then the comparisons of weeks averaged across treatments or within a treatment are done at the 0.05 level with no further adjustment. Similarly, if the two

- factor interaction is significant at the 0.05 level, then treatments within the same wee k are compared at the 0.05 level without further adjustment to the p

- values. If the two

- factor interaction is not significant, then comparisons of weeks within a treatment or treatments within a week are compared using Bonferroni

- Holm adjusted

p

- values.T he histopathological data is analyzed by StatCHARRMS with a novel approach termed RSCABS (Rao

- Scott Cochran

- Armitage by Slices) developed by John Green (DuPont), Timothy Springer (Wildlife International, Easton, MD), and Amy Saulnier (The Franklin Company, City, State). RSCABS incorporates the replicate vessel (aquarium) experimental design and the biological expectation that severity of effect will tend to increase with increasing concentrations of an EDC. In addition, RSCABS retains the individual subje ct scores (severity score) thus revealing the severity of any effect found statistically significant. RSCABS can be implemented for both single and multiple generational comparisons. The advantages of RSCABS over the typical histopathological analysis do ne in aquatic toxicology tests is 2

- fold: 1) it provides an objective, defensible analysis of the data determining effect concentrations, and 2) it provides information on the severity of the effect at any particular concentration of EDC, and therefore a c ontext to the effect(s) observed. RSCABS involves, iteratively, the following steps for each pathological observation: Step 1: For each replicate, count the number of fish in that replicate with severity 1 or greater and the number with score 0 (no pat hology), thereby changing the score to an incidence table. Apply the

step

- down Rao

- Scott Cochran

- Armitage test to these counts. This will establish a LOEC 1 and

NOEC 1 for increased severity, where both exceed the highest test concentration if no effect is f ound. Step 2: For each replicate, count the number of fish in that replicate with severity 2 or greater and the number with score <2, thereby changing the score to a new incidence table. Apply step

- down

Rao

- Scott Cochran

- Armitage test to these counts. Thi s will establish a LOEC 2 and NOEC 2 for test concentrations producing severity level 2 or higher, where both exceed the highest test concentration if no effect is found. Step K: For each replicate, count the number of fish in that replicate with severity K or greater and the number with score <K, thereby changing the score to a new incidence table. Apply stepdown Rao

- Scott Cochran

- Armitage test to these counts. This will establish a

LOEC K and NOEC K for test concentrations producing severity level K or highe r, where both exceed the highest test concentration if no effect is found. The lowest test concentration which is LOECK for some K>0 is the LOEC for the response under investigation and the value of K indicates the severity of the effect at that concentra tion. Furthermore, individual LOEC K values indicate where effects of severity K are observed.

Appendix G, Section 2: Detailed Statistics Description

The following was provided by John Green, Dupont Biostatistics group as a description of the methodolog y used within the StatCHARRMS program.

Statistical Analysis of Responses from Toxicity Experiments A basic principle in selecting statistical methods is to attempt to use underlying statistical models that are consistent with the actual experimental desi gn and underlying biology. This principle has historically been tempered by widely adopted conventions. For example, it is traditional in ecotoxicological studies to analyze the same response measured at different time points separately by time point, alth ough in many cases unified analysis methods may be available. The appropriateness of this convention will not be pursued in this discussion, but it does underlie the methodology in the ANOVA component of the EPA catalog. Instead, discussion will be restri cted to the most appropriate analysis of a response at a single time point. Attention is further restricted to situations where all other relevant factors, such as sex, are fixed at a single value. It is entirely possible to use a statistical model that i ncorporates and accounts for differences in sex, time of measurement, and other pertinent factors. However, this is not common in ecotoxicity and is not pursued it in this software.

The ANOVA procedure is restricted to hypothesis testing methods to determine a so

- called No Observed Effects Concentration (NOEC).The NOEC is defined in terms of the LOEC, or Lowest Observed Effects Concentration. The LOEC

is the lowest tested concentr ation that results in a statistically significant adverse effect. The NOEC is defined as the highest test concentration below the LOEC. Thus, the LOEC and NOEC are tested concentrations and experiments with different sets of tested concentrations will pr oduce different NOEC and LOEC values. The ANOVA component of the EPA catalog does not provide LOEC values directly, though they are readily discernable from the NOEC and tested concentrations. A related concept that is used in risk assessment is the MATC or Maximum Allowable Toxic Concentration, which is the geometric mean of the NOEC and LOEC. That is,

Equivalently, the MATC is the antilog of the mean of the logarithms of the NOEC and LOEC. This concept can probably best be understood from the fact that traditionally, the effect of a toxic chemical was expected to increase linearly with the logarithm of concentration. On a log scale, the MATC is midway between a NOEC and a LOEC and MATC is a compromise between those wanting to base risk assessment on the lowest observed effects concentration and those wanting to base risk assessment on the highest concentration in which no adverse effect was observed. The

idea is that since the NOEC and LOEC are tested concentrations subject to the “whim” of experimental design, there may be concentrations below the LOEC that produce adverse effects and there may be concentrations above the NOEC that do not. The midpoint (on the log scale) is a best

guess at where the “true” dividing point is between adverse effect and non

- adverse effect.

How accurate is the NOEC? It must be understood that the NOEC is not an estimate of the threshold of toxicity. One cannot provide a confidence interval for the NOEC. It is possible to provide a confidence interval for the absolute or percent effect at the NOEC. The ANOVA procedure does not do this directly, but the standard error of the treatment means is reported, so that the user can construct simple confidence intervals should that be needed.

The statistical test that is used depends on the hypothesis tested, the associated statistical model, and the distribution of the values (e.g., are data normally distributed?). Thus, it is necessary to determine the appropriate stat istical model and specify the hypotheses to be tested before selecting the test procedure.

All methods of statistical assessment (regression and hypothesis testing alike) assume a statistical model. The hypothesis testing approach to evaluation of toxici ty data is based in part on keeping to a

reasonable number the untestable or difficult

- to

- test assumptions, particularly those regarding the statistical model, that will be used in reaching conclusions. The models used in regression

- based

methods use stron ger assumptions (such as specifing a definite parametric form for the dose

response shape) than the models used in the hypothesis testing approach, but result in more quantitative conclusions.

Statistical Model for NOEC The simplest statistical model gen erally used in hypothesis testing assumes only that the distributions of responses within these populations are identical except for a location parameter (e.g., the mean or median of the distribution of values from each group). The basic hypothesis (in 1

-

s ided form) can be stated as follows:

H 0 : μ 0 = μ 1 = μ 2 =...= μ k vs H 1 : μ 0 > μ i for at least one i, (1)

where μ i, i=0, 1, 2, 3, ..., k denote the means (or medians) of the control and test populations

, respectively. Thus, one tests the null hypothesis of no differen ces among the population means against the alternative that at least one population mean is smaller than the control mean. There is no investigation of differences among the treatment means, only whether treatment means differ from 

### LOEC NOEC MATC * =the control mean. To ap ply this procedure, one tests the null hypothesis of no trend against the alternative at the 0.05 level. If the null hypothesis is not rejected, then testing stops and the NOEC exceeds the highest concentration. If the alternative hypothesis is accepted, then the high concentration is omitted and the test is repeated (still at the 0.05 significance level) with the remaining concentrations. This process continues until the null hypothesis of no trend is first not rejected. The NOEC is the highest concent ration remaining at this stage. All of these tests are conducted at the 0.05 significance level, which is the false positive rate for the entire procedure. Further discussion of this is given in Hochberg and Tamhane (1987), Westfall (1999), Westfall et al

. (1999), Marcus et al. (1976 ), Rom et al. (1995).

In this formulation, an adverse effect is one that reduces the population mean response. This will generally be appropriate for responses such as size, biomass, and reproduction. Where an adverse effect is indicated by an increase in the population mean r esponse, the inequality (1) is reversed.

In some situations, any change, i.e., either an increase or decrease, in the mean response is indicative of an adverse effect. In these situations, a 2

- sided alternative hypothesis is appropriate. Then the

altern ative is

H 1 : μ 0 ≠ μ i for at least one i.

The test statistics for the above hypotheses are based on comparing each treatment to the control, independent of the other treatments. Many tests have been developed for this approach, some of

which will be discus sed in this course. Most such tests were developed for experiments in which treatments are qualitatively different, as, for example, in comparing various new therapies or drug

formulations to a standard.

In toxicology, the treatment groups generally differ only in the exposure concentration (or dose) of a single chemical. It is further often true that biology and experience suggest that if the chemical is toxic, then as the level of exposure is increased, the magnitude of effect will tend to increase. Depending on what response is measured, the effect of increasing exposure may show up as an increase or as a decrease in the measured response, but not both. The statistical model underlying this biological expectation is what will be called a trend model or a model assuming monotonicity of the population means:

μ 0 ≥ μ 1 ≥ μ 2 ≥ μ 3 ≥

. . . ≥ μ k (or with inequalities reversed). (Model 2)

The null and alternative hypotheses can then be stated as

H 02 : μ 0 = μ 1 = μ 2 =...= μ k vs H 12 : μ 0 ≥ μ 1 ≥ μ 2 ≥ μ 3 ≥

. . . ≥ μ k , with μ 0 > μ k

.

Note that μ 0 > μ k is equivalent, under the alternative, to μ 0 > μ i for at least one i . If this monotone model is accepted as representing the true responses of test organisms to exposure to toxicants, it is not possible for, say, μ 3 to b e smaller than μ 0 and μ 6 not to be smaller.

Under the trend model and tests designed for that model, if tests of hypotheses H 02 vs H 12 reveal that

μ 3 is different from μ 0 , but μ 2 is not, the NOEC has been determined (i.e. it is the test concentration

ass ociated with μ 2) , and there is no need to test whether μ 1 differs from μ 0

. Also, finding that μ 3 differs from μ 0 implies that a significant trend exists across the span of doses including μ 0 and μ 3 , the span including μ 0 and μ 4 , and so on. For the majority of toxicological studies, a test of the trend hypothesis based on model (2) is consistent with the basic expectations for a model for dose

response. In addition, statistical tests for trend tend to be more powerful than altern ative non

- trend tests, and should be the preferred tests if they are applicable. Thus, a necessary early step in the analysis of results from a study is to consider each endpoint, decide whether a trend model isappropriate, and then choose the initial sta tistical test based on that decision. Only after it is concluded trend is not appropriate do specific pairwise comparisons make sense to illuminate sources of variability.

Toxicologists sometimes do not know whether a compound will cause measurements of c ontinuous variables such as growth or weight to increase or decrease, but they are confident it will act in only one direction. For such endpoints, the 2

- sided trend test is appropriate. One difference between implementing step

- down procedures for quantal data and continuous data is that two

- sided tests are much more likely to be of interest for continuous variables. Such a model is rarely appropriate for quantal data, as only increased incidence rate above background (control) incidence are of interest in toxicology. The two

- sided version of the step

- down procedure is based on the underlying model:

μ 0 ≥ μ 1 ≥ μ 2 ≥ μ 3 ≥

. . . ≥ μ k

or

μ 0 ≤ μ 1 ≤ μ 2 ≤ μ 3 ≤ . . . ≤ μ k.

Under this model, in testing the hypothesis that all population means are equal against the

a lternative that at least one inequality is strict, one first tests separately each 1

- sided alternative at

the 0.025

- level of significance with all doses present. If neither of these tests is significant, the NOEC is higher than the highest concentration. I f both of these tests are significant, a trend

- based procedure should not be used, as the direction of the trend is unclear. If exactly one of these tests with all the data is significant, then the direction of all further tests is in the direction of the significant test with all groups. Thereafter, the procedure is as in the 1

- sided test, except all tests are at the 0.025 significance level to maintain the overall 0.05 false positive rate.

Where it is biologically sensible, it is preferable to test the one

- sided hypothesis, because random variation in one direction can be ignored, and as a result, statistical tests of the one

- sided hypothesis are more powerful than tests of the two

- sided hypothesis.

Note that a hypothesis test based on model 2 assumes only a monotone dose

- response rather than a precise mathematical form, such as is required for regression methods.

Comparisons of single

- step (pairwise comparisons) to step

- down trend tests to determine the

NOEC In general, determining the NOEC for a stu dy involves multiple tests of hypotheses (i.e., a family of hypotheses is tested), either pairwise comparisons of treatment groups, or a sequence of tests of the significance of trend. Statisticians have developed tests to control the family

- wise error rat e, FWE, (the probability that one or more of the null hypotheses in the family will be rejected incorrectly) in the multiple comparisons performed to identify the NOEC. The method of controlling the family

wise error rate has important implications for the power of the test. There are two general approaches that will be discussed: single

- step procedures and step

- down procedures.

Single

- step procedures amount to performing all possible comparisons of treatment groups to the control. Multiple comparisons to the control may be made, but there is no ordered set of hypotheses to test, and no use of the sequence of outcomes in deciding which comparisons to make. Examples of the single

- step approach include the use of the Fisher’s exact test, the Mann

- Whitney, Du nnett and Dunn tests. Since many comparisons to the control are made, some adjustment must be made for the number of such comparisons to keep the family

- wise error (FWE) rate at a fixed level, generally 0.05.

With tests that are inherently single comparison tests, such as Fisher’s exact and Mann

- Whitney, a Bonferroni adjustment can be made: a study with k treatment levels would be analyzed by performing the pair

- wise comparisons of each of the treatment groups to the control group, eachperformed at a significance level of α/k instead of α. (This is the Bonferroni adjustment.) The Bonferroni adjustment is generally overly conservative, especially for large k. Modifications reduce the conservatism while preservin g the FWE at 0.05 or less.

Bonferroni

- Holm adjustment to p

- values For the Holm modification (Holm, ( 1979) ) of the Bonferroni adjustment, arrange the k unadjusted p

values for all comparisons of treatments to control in rank order, i.e., p (1) ≤ p (2) ≤ p (3) ≤

... ≤ p (k)

. Beginning with p (1) , compare p (i) with α/(k

– i+1) , stopping at the first non

- significant comparison. If

the smallest i for which p (i) exceeds α/(k

– i+1) is i=j, then all comparisons with i>j are judged non

significant without further comparison s. It is helpful (Wright (1992)) to report adjusted p

- values rather than the above comparisons. Thus, report p* (1) = p (1) *(k

- i+1) and then compare each adjusted

p

- value to α . In the case of ties among the unadjusted p

- values, all adjustments are the same a s for the smallest rank of those p

- values. Table 1 illustrates the advantage of the Bonferroni

- Holm method. In this hypothetical example, only the comparison of treatment 4 with the control would be significant it the Bonferroni adjustment is used, whereas all comparisons except the comparison of the Control with treatment 1 would be significant if the Bonferroni

- Holm adjustment is used. Holm modification to the Bonferroni is implemented in the ANOVA and Quantal NOEC procedures for

the Mann

- Whitney of Fishe r Exact tests. Dunn’s test, used in ANOVA, uses a Bonferroni adjustment. The paper by Dunn (1964) uses that approach applied only to the specific comparisons of interest to toxicity studies.

Comparison Unadjusted p value Bonferroni

- Holm Adjusted p value

p * (i) α/k (Bonferroni Adjustment of α)

Control

– Treatment 4 p (1) =0.002 0.002*4=0.008 0.05/4=0.0125

Control

– Treatment 2 p (2) =0.013 0.013*3=0.039 0.05/4=0.0125

Control

– Treatment 3 p (3) =0.020 0.020*2=0.040 0.05/4=0.0125

Control

– Treatment 1 p (4) =0.310 0.310*1=0.310 0.05/4=0.0125

Table 1 Comparison of Adjusted and Unadjusted P

- Values

The Bonferroni and Bonferroni

- Holm adjustment guarantee that the family

- wise error rate is less than α, but they are conservative.

The Dunnett test, has a “built

- in” adjustment for the number of comparisons made and are less conservative (hence, more powerful). There is no additional adjustment for the number of comparisons. Step

- down tests, discussed below, do not require an adjustment for the numbe r of comparisons.

Step

- down procedures are generally preferred where they are applicable. All step

- down procedures discussed are based on a sequential process consisting of testing an ordered set of hypotheses concerning means, ranks, or trend. A step

- dow n procedure based on trend (for example) works as follows: First, the hypothesis that there is no trend in response with increasing dose is tested when the control and all dose groups are included in the test. Then, if the test for trend is significant, th e high dose group is dropped from the data set, and the hypothesis that there is no trend in the reduced data set is tested. This process of dropping treatment groups and testing is continued until the first

time the trend test is non

- significant. The high est dose in the reduced data set at that stage is then declared to be the NOEC. Distinguishing features of step

- down procedures are that the tests of hypothesis must be performed in a given order, and that the outcome of each hypothesis test is

evaluated b efore deciding whether to test the next hypothesis in the ordered sequence of hypotheses. It is these two aspects of these procedures that account for controlling the family

- wise error (FWE)

rate.A step

- down method typically uses a critical level larger than that used in single

- step procedures, and seeks to limit the number of comparisons that need to be made. Indeed, the special class of

“fixed

- sequence” tests we will describe fix the critical level at 0.05 for each comparison but bound

the FWE rate at 0 .05. Thus, step

- down methods are generally preferable to the single

- step methods as long as the response means are monotonic.

Tests based on trend are logically consistent with the anticipated monotone pattern of responses in toxicity tests. Step

- down pr ocedures make use of this ordered alternative by ordering the tests of hypotheses. This minimizes the number of comparisons that need to be made, and in all the methods discussed here, a trend model is explicitly assumed (and tested) as a part of the proce dure.

Procedures that employ step

- down trend tests have more power than procedures that rely on multiple pairwise comparisons when there is a monotone dose

- response because they make more use of the biology and experimental design being analyzed. When the re is a monotone dose

response, procedures that compare single treatment means or medians against the control, independent of the results in other treatments (i.e. single

- step procedures), ignore important and relevant information, and suffer power loss as a result.

The trend models used in the step

- down procedures do not assume a particular precise mathematical relationship between dose and response, but rather use only monotonicity of the dose

- response relationship. The underlying statistical model assum es a monotone dose

- response in the population means, not the observed means.

Rejection of the null hypothesis (i.e., rejecting the hypothesis that all group means, or medians, or distributions are equal) in favor of the stated alternative implies that the high dose is significantly different from the control. The same logic applies at each stage in the step

- down application of the test to imply, whenever the test is significant, that the high dose remaining at that stage is significantly different from the control. These tests are all applied in a 1

- sided manner with the direction of the alternative hypothesis always the same. Moreover, this methodology is general, and applies to any legitimate test of the stated hypotheses under the stated model. That is, one can use this fixed

- sequence approach with the Cochran

- Armitage test on quantal data or the Jonckheere

Terpstra ( Jonckheere (1954)) test of trend on continuous data. Other tests of trend can also be used in this manner, but are not implemented in the AN OVA component of the EPA catalog.

Deciding between pairwise and trend

- based comparisons The first consideration is biological. Do we expect a monotone dose

- response? If not, then tests based on monotonicity should not be used. USEPA (1995) recommends t he use of step

- down trend tests to establish NOECs for both quantal (incidence) and continuous responses. (USEPA (1995)

recommends the Mantel

- Haenszel test instead of the almost equivalent Cochran

- Armitage test for

incidence data and Jonckheere

- Terpstra te st for continuous data to establish the NOEC.)

Even if we expect a monotone dose

- response, it is important to assess the data for consistency with that judgment. There are several reasons for this. Bauer (1997) has shown that certain tests based

on a mo notone dose

- response can have poor power properties or error rates when the monotone assumption is wrong. Davis and Svendsgaard (1990) and others have suggested that departures from monotonicity may be more common than previously thought. These results sug gest that a need for

caution exists.

On the positive side, Odeh (1972), Odeh (1971), Poon (1980), Potter and Sturm (1981), Potthoff and Whittinghill (1966) and Puri (1965) all discuss the favorable performance of this test compared to

various others and USEPA (1995) recommends this test.Both formal tests and visual inspection are recommended to determine whether there is significant departure from monotonicity. The ANOVA component of the EPA catalog uses such a “negative” test for departure from monoto nicity (sets of orthogonal contrasts for continuous responses and a decomposition of the chi

- square test of independence for quantal responses) and proceeds unless

there is evidence of non

- monotonicity. Details on these procedures are given later. This app roach is grounded in the idea that monotonicity is the rule and that it should take strong evidence to depart from this rule. However, the procedures in the ANOVA component of the EPA catalog allow expert judgment to override this presumption of monotonici ty. This is done by selecting pairwise comparisons only.

Formal tests for monotonicity are especially desirable in a highly automated test environment. The ANOVA procedure uses linear and quadratic contrasts of normalized rank statistics (to avoid the

co mplications that can arise from non

- normal or heterogeneous data). If the linear contrast is not significant and the quadratic contrast is significant, there is evidence of possible non

- monotonicity that calls for closer examination of the data or pairwise comparison methods. By default, the program refuses to do the Jonckheere test under this condition, though the user can force that action. Otherwise, a trend

- based analysis is used.

Where expert judgment is used, formal tests for monotonicity or its lack may be replaced by visual inspection of the data, especially of the mean or median responses. The same concept applies to assessing normality and variance homogeneity. The ANOVA proce dure does not provide graphical displays of the data.

Normality

Non

- monotonic dose

- response data should be assessed using an appropriate pairwise comparison procedure. These can be categorized according whether they require the data be normally distribu ted or homogeneous. Dunnett’s test is appropriate if the data are normally distributed with homogeneous variance. For normally distributed but heterogeneous data, the Tamhane

- Dunnett (T3) method (Hochberg and Tamhane (1988); Tamhane (1979); Dunnett (1980); Tamhane and Dunnett, (1998)) can be used. Non

- normal data can be analyzed by using Dunn or Mann

- Whitney tests with

Bonferroni

- Holm adjustment. Of course, a variance stabilizing or normalizing transform, if found, can be employed to make the response fit one of tests requiring these features. Normality is formally assessed in the ANOVA procedure using the Shapiro

- Wilk test (Shapiro and Wilk ( 1965).

Parametric versus non

- parametric tests

– (Continuous data) Dunnett’s test requires the data to be approxima tely normally distributed with homogeneous within

treatment variances. The Tamhane

- Dunnett test is for normally distributed data with heterogeneous

within

- treatment variances. Parametric tests have desirable properties when these assumptions are

met. Fail ure of the data to meet assumptions can sometimes be corrected by transforming the data and transformations are available in the ANOVA procedure and discussed below. Some non

parametric tests are almost as powerful as their parametric counterparts when th e assumptions of normality and homogeneity of variances are met and may be much more powerful if the assumptions are not met. Furthermore, a test based on trend is generally more powerful than a pairwise test. A decision to use a parametric or non

- parametr ic test should be based on which best describes the physical, biological and statistical properties of a given experiment.

If the data are found non

- normally distributed, then the ANOVA procedure uses a non

- parametric test. Ordinarily, for pairwise compa risons, this is Dunn’s test configured only to compare groups to control. Under some conditions, Mann

- Whitney tests with a Bonferroni

- Holm adjustment to the p

values is used. This is discussed further under Exact Permutation Tests below.

Regardless of n ormality and variance homogeneity considerations, if a step

- down trend test is used, the ANOVA procedure uses the Jonckheere

- Terpstra test. The primary parametric alternative to theJonckheere test is Williams’ test, which is not implemented in the ANOVA component of the EPA catalog at this time but is expected to be available in the next release. Power simulation studies carried out at DuPont and some publications (see Poon (1980), Potter and Sturm (1981), Puri (1965), for example) indicate these two tests have similar power characteristics under normal, homogeneous conditions and the Jonckheere test tends to be superior to Williams’ when these

conditions are not met.

Outliers Outliers that relate to an experim ental unit may arise due to the fact that such a unit differed systematically from the other groups by some (usually unknown) experimental factor(s). For instance, the organisms in the various experimental units were held in different aquaria, and one of

t hem contained an infection. Or, the organisms in the different experimental units were treated in a specific order (with respect to feeding, time of observation, etc). Detection of this type of outliers typically cannot be enforced by any formal statistica l method, and one has to rely on visual inspection, judgment and experience.

Obviously, experimental unit or treatment group outliers are highly undesirable, since they directly interfere with the effect that one wishes to measure, thereby increasing the probability of both false positive and false negative results. For example, a NOEC may be assessed at a level where substantial effects do occur, or a LOEC may be assessed at a level without real effects (i.e. from the chemical). The best way to reduce th e chance of an outlier at the group or replicate level is a design that is perfectly randomized with respect to all experimental actions that may potentially influence the (observed response of) the biological system. In practical biological studies, howev er, perfect randomization is hard to realize. Therefore, it is important to make the study design relatively insensitive to potential outliers, i.e., by randomized replicated dose groups, and/or by increasing the

number of different doses.

Outliers at the individual level can be detected in continuous data. When a particular distribution is assumed for the scatter in the data, the judgment of outliers may be based on a specific, small probability that any single data point could occur. This implies that th e judgment of outliers can depend quite strongly on the assumed distribution. For example, values that appear to be extremely high when assuming a normal distribution may be judged as non

- extreme when assuming a lognormal distribution. On the other hand, l ow values may be judged as extremes when assuming a lognormal distribution, but not so when a normal distribution is assumed.

The statistical analysis of the data is sensitive to individual outliers, although less dramatically than to group outliers. On t he one hand, individual outliers may result in biased estimates of the effect (either too small or too large). On the other hand, the estimate of the residual variance (the “noise”) will be increased, implying that statistical tests tend to be less powerfu l, and estimated parameters (e.g. ECx) less precise. Therefore, if biological reasons can be found explaining the outliers, it is preferable to delete them from the analysis. As a reality check, one can also repeat the analysis with

the outliers omitted. If this does not change the conclusions, one may have greater confidence in the result if no biological justification for omitting the outliers is found. This is a helpful step before expending great effort to find a reason for omitting an observation. I n the ANOVA procedure, the repetition of analysis with outliers removed is done automatically and is shown in the output Lis file described below. The outlier omitted analysis is not reflected in the summary table, also described

below.

In addition to pl ots and visual inspection, there are formal outlier rules that are useful, particularly when a large volume of data must be processed in a short period of time. The Tukey (1977) outlier rule (described in the next paragraph) is practical, useful and imple mented in the ANOVA procedure. Furthermore, it is not itself sensitive to the effects outliers, which makes it a decided improvement over methods based on standard deviations, which are quite sensitive to the effects ofoutliers. Tukey’s outlier rule is u sed as a formal test with outliers being assessed from residuals (results of subtracting treatment means from individual values) to avoid confounding outliers and

treatment effects.

Any response more than 1.5 times the interquartile range above the third quartile (75 th percentile) or below the first quartile (25 th percentile) is considered an outlier by Tukey’s rule. Such outliers are reported with the results of the analysis. The entire analysis of a given endpoint is repeated with

outliers omitted to de termine whether the outliers affected the conclusion. While it is true that nonparametric analyses are less sensitive to outliers than parametric analyses, omission of outliers can still change conclusions, especially when sample sizes are small or outlier s are numerous.

Although non

- detectable, individual outliers can also occur in quantal data and affect the analysis. For example, when just one of the individuals in the controls shows a response, but is in fact an outlier, this outlier may have quite an impact on the statistical analysis. Being non

- detectable, individual outliers are a larger problem in quantal than in continuous data.

Heterogeneous variances Visual inspection may also be used to explore the general pattern of the scatter around (conti nuous) data. Thus, one may find out if the scatter around the mean response appears to be symmetrical or skew, and if the scatter is more or less homogenous. Heterogeneity of variance (scatter) may have a biological basis i.e. the individual organisms (uni ts) respond differently to the chemical. However, an apparent increase of decrease in the scatter may also be related to the statistical distribution of the data, e.g. the scatter increases with the mean response. This distinction is important, both for th e analysis, and for the interpretation of results, and some further clarification is given below.

When the plotted data show scatter that is correlated with the mean response, such pattern may be related to the underlying distribution of the data. Such plots are provided by the ECx procedure. For example, in lognormally or gamma distributed data it may be expected theoretically that the standard deviation increases proportionally with the mean (or, equivalently, the Coefficient of Variation, CV, is homogenous). When a particular dataset (such as weights, concentrations) show

scatter that increases with the mean, one may plot such data on the log

- scale, which may make the scatter independent from the means. In addition, when the scatter is relatively large (say, CV larger than 20%), the scatter may be skewed on the original scale, but not on the log

- scale . The latter would confirm that the pattern in the scatter is a result from the underlying distribution.

As another example, counts (such as number of live young) may follow a Poisson distribution. Here the variances are expected to be equal to the means (or, proportional to them). Such a pattern should vanish when the data are plotted on square root scale.

Finally, in quantal data with replicated dose groups, it is expected a priori that the scatter between the replicates depend on the mean response (th is follows directly from the properties of frequencies). Here, one may plot the frequencies after the transformation arcsin( √ p ), where p is the observed frequency (fraction). Such a transform should remove the (theoretical) relationship between the

varianc e and the mean frequency (assuming a binomial distribution).

Heterogeneity in the scatter might be caused by the treatment (the applied chemical) itself, i.e. some individuals exhibit greater responses to the chemical than others. In most toxicity tests, however, the organisms used are genetically homogenous, and real (biological) heterogeneity in response to the chemical will, in those cases, not be very likely.

Residual plots (plot of the residuals vs. the predicted values) and formal tests of heterogen eity of variance can be used to find heterogeneity or dismiss it as a problem. Of the latter, the ANOVAprocedure utilizes Levene’s test (Box, 1953), since it is not as sensitive to outliers and other data

issues such as non

- normality as some alternatives such as those of Bartlett or Hartley.

When heterogeneity of variances appears to be due to real biological heterogeneity in responses among individual organisms, then more care must be taken. For example, when the organisms (or experimental units) consi st of two distinct subpopulations, one responding, and the other not, an estimated change in mean response has no useful meaning unless the subpopulations can be distinguished from known causes, such as sex. In that event, the appropriate way to proceed is to analyze the subpopulations separately, or using the subpopulation identity or value as a covariate.

Heterogeneity of variances may be a matter of scaling that can be removed by the right transformation. Another approach is to omit the transformation, and use methods that are directly based on the assumed distribution (i.e. generalized linear models). If no such distribution or transformation can be found or is readily available, then nonparametric procedures can be used.

Transformation of data Many st andard parametric methods assume normally distributed data and homogenous variances. Data often deviate from these assumptions, and if so, the inferences resulting from these standard

methods can be unreliable. If a variance

- stabilizing transformation can be found, then the statistical analysis can be done on the transformed data.

For a variance

- stabilizing transformation to exist, there must be a relationship between the population means and variances. In many cases, the theoretical distribution of the response variable can guide the choice of a transformation. For example, if the underlying distribution is assumed to be Poisson, t he square root transformation, y i ’ = (y i 1⁄2 ) or y i ’ = ((y i +1) 1⁄2 ) or [y i ’=(y i +1) 1⁄2 + (y i 1⁄2 ) ], might be used. If the underlying data are lognormal, the log

- transformation, y i ’ = log(y i ), is often used. For proportions with binomial distributions, the arc

- sin sq uare

- root [y i ’=arcsin(y i 1⁄2 )] and

Freeman

- Tukey transformations are often used. If the underlying theoretical distribution is unknown, a data

- based procedure (Box

- Cox transformation) can be used (Box and Cox, 1964).

The use of transformations will often si mplify the data analysis, in that the more familiar and traditional data analysis methods can be used, but care must be taken in interpreting the results of this data analysis.

If a transformation is used, it is also necessary to back

- transform the means and confidence intervals back to the original scale, when reporting results. As to confidence intervals, one must back

transform the bounds of the interval, not back

- transform the standard errors. It must also be

understand that the back

- transformed means generally differ from the arithmetic means of the original data. These back

- transformed means should be interpreted as estimates of the median of the underlying data distribution, if the transformed data are normally (or at least symmetrically)

distribute d. In the special case of a log

- transformation, the back

- transformed mean is the geometric mean of the original data, and this value estimates the median of the underlying lognormal

distribution.

When a transformation is not used in the data analysis, th e difference in the means is a logical measure for the size of an effect. This difference is interpreted as an absolute change in the original units (e.g., a decrease of 1.2 grams). The back

- transformed difference in means (of the transformed data) however has another, usually more difficult, interpretation. In the special case of a log

transformation, the difference between the back

- transformed means does allow a simple interpretation: it estimates the ratio (or percent change) of the median responses. 

### 1 1 1

,


### + + + +

=

i i i

i i i

i 

### n y n arsin

n y n arsin yIn addition, transformations may not maintain additivity of effects (interactions among factors, e.g., test substance, sex, age, in the experiment). Other possible consequences of using transformations are that they change the interpretation of outliers an d that they affect the value of r (Pearson correlation coefficient) and R 2

.

Transformation of the response especially complicates regression methods to estimate an ECx, since an x% change in the untransformed response does not correspond to an x% change in the transformed response. Great care must be taken to estimate a meaningful quantity after a transform. This is less of a problem with hypothesis testing, since we are only inquiring whether a response mean or median (or other measure of location) diff ers from the control. It is not necessary to quantify the amount of change to determine the NOEC.

All of the transformations discussed above and others can be peformed in the ANOVA procedure, using one of two methods which are described in the program sy ntax below. References on transformations include Box and Cox (1964), Box and Hill (1974), Box and Tidwell (1962), Draper and Cox (1969).

Exact Permutation Tests Not all data problems can be fixed by transformation of the response. For example, if a large percentage of the responses have the same measured value (ties), no transformation will address that issue. In the case of very small samples, say less than three per concentration for most concentrations, it is may not be possible to assess in a meaningf ul way the distribution of responses. One can either use an assumed distribution on faith or treat this situation in the same way as data with massive ties. Exact permutation methodology is an appropriate tool to use in these cases. The ANOVA component of the EPA catalog automatically uses exact methods when deemed appropriate. In particular, the Jonckheere

- Terpstra, Mann

- Whitney and Cochran

- Armitage tests discussed later all have exact versions that are implemented in the ANOVA component of the EPA

cat alog. In the ANOVA procedure, there are two conditions under which exact methods are used: (1) There are no more than three experimental units in any treatment group and non

- parametric procedures are found appropriate. (2) There are “massive ties” in the responses, i.e., 25% or more of the experimental units have the same response or 40% or more have one of only two response values or 65% or more have one of only three response values. An exact version of the Jonckheere

Terpstra test is used for step

- down trend test and an exact version of the Mann

- Whitney test with

Bonferroni

- Holm adjusted p

- values is used in place of Dunn’s test.

Weighted ANOVA There are two selections that affect whether and how a weighted analysis is done in the ANOVA procedure. Th ese are the Pick weight/frequency variable button and the Var(btwn)/Var(wthn) Ratio scroll menu. Weighted analysis is generally meaningful only when there two or more sources of variability in the data, typically resulting from a nested experimental desig n, with multiple replicates(tanks, beakers, pots, plots) per treatment and multiple subjects (fish, daphnid, plants, plants) per replicate.

When there are two (or more) sources of variability in the data, some caution is needed to ensure a proper analysis . A situation common in ecotoxicity studies is to have replicate vessels each comprised of multiple organisms. For example, in a fish or daphnid experiment, at each concentration tested, there may be two or more tanks or beakers, each with 10

- 20 subjects . In a non

target plant study, there may be replicate tubs or plots, each containing 5

- 10 individual plants. Usually, the vessel (tank, tub, beaker, plot) is considered the experimental unit in this experimental

set

- up.It is possible to analyze such data in several ways. Some statistical models use only the replicate means, justifying this on the theory that the replicate is the experimental unit. However, as demonstrated below, this is only appropriate when the number of subjects in all replicates i s the same in every replicate vessel. While experiments are often designed with such equal sample sizes, differential mortality often occurs during the course of the experiment, so that at study end, when data are collected for analysis, there are unequal numbers of surviving subjects in different replicate vessels. If the disparities in number of survivors is small, differing by no more than 10

- 20%, there is usually little penalty in modeling replicate means without further refinement. However, when

fin al sample sizes differ more than that, substantial error can be incurred when following this practice. For this reason, the best practice is to analyze the individual subjects using an appropriate model, such as described below.

Alternatively, if individ ual data are not available or there is limited ability to carry out such an analysis, then a weighted ANOVA can be done. First, the need for a weighted ANOVA will be shown. Then, a method for carrying out such an analysis will be described. References o n weighted ANOVA include Milliken and Johnson (1984), Hocking (1985). The second of these is also valuable for the following discussion on variance ratios.

An appropriate model incorporating terms for subject and replicate as well as concentration is simp le to state. In this analysis, the ANOVA model can be written as follows:

ijk i j i ijk 

### e Conc rep Conc y + + = ) ( ) (

where the e ijk correspond to individual subjects and



### * * * * * * * * 0 )

, cov(

0 12 12 * * *



### k j i ijk if k k and j i ij if j j or i i if y y k j i ijk



### = + = ≠ = = ≠ ≠ =

φ

φ φ

Variance Ratio The variance of a replicate mean is given by



###    

   

+ =    

   

+ = + =

ij ij ij ij 

### n r

n n y 1 1 ) var( 0 0 12 0 0 12

. 

### φ φ φ φ φ φ

where n ij is the number of subjects in rep j of concentration i . Thus, the variance of the rep mean is a function of both the rep sample size and the ratio of variance components.

If there is little variation in sample sizes, then the variance ratio and weights can probably be ignored. If the between

- rep variance component, φ 12 , is large compared to the within

- rep contribution, φ 0 /n ij , then weighted ANOVA is of little value. This happens, for example, when the variance ratio is larger than 1.5, that is, the between

- rep variance is 50% larger than the within

- rep variance. From the table, it also happens when the minimum sample size exce eds 5. In other situations, both the variance ratio and sample size can have significant effects on the variance of the replicate means and should not be ignored.

TABLE: Effect of Sample Size and Variance ratio on Variance of Replicate mean

Value in table is variance of replicate mean.

Size 1 2 3 4 5 6 7 8 9 10

VRatio

0 1.00 0.50 0.33 0.25 0.20 0.17 0.14 0.13 0.11 0.100.1 1.10 0.60 0.43 0.35 0.30 0.27 0 .24 0.23 0.21 0.20

0.2 1.20 0.70 0.53 0.45 0.40 0.37 0.34 0.33 0.31 0.30

0.3 1.30 0.80 0.63 0.55 0.50 0.47 0.44 0.43 0.41 0.40

0.4 1.40 0.90 0.73 0.65 0.60 0.57 0.54 0.53 0.51 0.50

0.5 1.50 1.00 0.83 0.75 0.70 0.67 0.64 0.63 0.61 0.60

0.6 1.60 1.10 0.93 0.85 0.80 0.77 0.74 0.73 0.71 0.70

0.7 1.70 1.20 1.03 0.95 0.90 0.87 0.84 0.83 0.81 0.80

0.8 1.80 1.30 1.13 1.05 1.00 0.97 0.94 0.93 0.91 0.90

0.9 1.90 1.40 1.23 1.15 1.10 1.07 1.04 1.03 1.01 1.00

1 2.00 1.50 1.33 1.25 1.20 1.17 1.14 1.13 1.11 1.10

1.1 2 .10 1.60 1.43 1.35 1.30 1.27 1.24 1.23 1.21 1.20

1.2 2.20 1.70 1.53 1.45 1.40 1.37 1.34 1.33 1.31 1.30

1.3 2.30 1.80 1.63 1.55 1.50 1.47 1.44 1.43 1.41 1.40

1.4 2.40 1.90 1.73 1.65 1.60 1.57 1.54 1.53 1.51 1.50

1.5 2.50 2.00 1.83 1.75 1.70 1.67 1.64 1.63 1.61 1.60

1.6 2.60 2.10 1.93 1.85 1.80 1.77 1.74 1.73 1.71 1.70

1.7 2.70 2.20 2.03 1.95 1.90 1.87 1.84 1.83 1.81 1.80

1.8 2.80 2.30 2.13 2.05 2.00 1.97 1.94 1.93 1.91 1.90

1.9 2.90 2.40 2.23 2.15 2.10 2.07 2.04 2.03 2.01 2.00

2 3.00 2.50 2.33 2.25 2.20 2.17 2.14 2.13 2.11 2.10

That is, it is clear that the variances of the sample means can vary greatly if there are great variations in sample size and the variation is greater as the variance ratio increases. It is not immediately

obvious from the formula above that conclusions f rom the ANOVA can vary significantly as a result of these unequal variances. However, such is indisputably true.

If we have the individual data, then the nested variance component structure is no problem. If we have only the rep means, we cannot estima te the two variance components. However, if the sample

sizes n ij are all the same, there is no problem is analyzing replicate means, since in this instance, the (population) variances of all replicate means are the same. If the sample sizes are not all th e same, then one should use weights proportional to the inverse of the variance. Thus, weights should be proportional to

ij ij

ij 

### rn n

n r + =

+ 1 1 1

To use weights proportional to n ij alone is seen to be equivalent to assuming r=0, which is

unrealistic.

B y using historical data to estimate the variance ratio, one can give a better weight. This can be verified by running two models, one with individual observations and the indicated nested variance structure and the other by using the indicated weights. V alidation of this approach to weighted ANOVA included using this idea.

Historical data was used in developing the ANOVA component of the EPA catalog. For non

- target plants, the median value of the variance ratio r was found to be 0.33. However, a default variance ratio of zero is set in the ANOVA program. The reason is the expectation that the ANOVA program will be used for a greater variety of datasets and a default value of zero will correspond to what many kno wledgeable users will expect. Other values of r can be selected by the user if, for a particular species or study, one has a better estimate for this ratio.

Appendix , Section 3: Analysis of Continuous Responses, Repeated Measure (Fecundity/Fertility)Th e following was provided by John Green, Dupont Biostatistics group as a description of the methodology used within the StatCHARRMS program.

Egg production can be analyzed as a continuous response by a repeated measures ANOVA, with week (or day) the repeat ed factor and treatment and generation as fixed effect factors. Generation is not a repeated factor for two primary reasons. First, no individual is measured in more than one generation. Second, while in the abstract it might be possible to make the vessel the unit of analysis if the same vessel were used for the F0 generation and for all progeny of the F0 parents in that vessel and all progeny of the resulting F1 generation, the logistics of such a design are inefficient. In fact, the eggs from the F0 gene ration are randomized into new vessels to form the F1 generation and the eggs from F1 are randomized to form F2. Thus, there is no discernible experimental unit that is being measured repeatedly over generations.

Usually, only the F1 and F2 generations a re analyzed, as both of these generations were exposed to the chemical throughout their entire lives, whereas the F0 generation was first exposed in the egg

state or later.

Tests for differences across generations and differences across weeks within each generation, as well as treatment effects within and across generations are done using contrasts. The analysis begins with evaluations of the requirements for ANOVA, namely normality (Shapiro

- Wilk and Anderson

Darling tests) and variance homogeneity (Levene test). Transformations of variables are applied to achieve these requirements where the original data do not. Where no other transformation is found, analysis is based on a normalized rank

- order transform.

The analysis with all generations included tests for differences (1) between generations averaged across weeks and groups, (2) between groups averaged across generations and weeks, (3) between weeks averaged across generations and groups, (4) between groups within the same generation, (5) between groups within the same generation and week, (6) between weeks within the same group and generation, and (7) between generations within the same group and week. If every such comparison is made with a 5% false positive rate, there is a very high probability of s ome false positives occurring. To control the false positive rate, some adjustment is needed for the number of comparisons. However, it is not appropriate to use a crude Bonferroni or Bonferroni

- Holm type adjustment for all comparisons simultaneously, for this would grossly inflate the false negative rate.

The adjustments are primarily based on the ANOVA F

- tests, except main effects of treatment are tested using the Dunnett test, which adjusts for the number of comparisons independent of any F

test. For the main effect of generation or week, there is no “control” level and every pair of levels is a comparison of possible interest. For these two main effects, if the F

- test for the main effect is significant at the 0.05 level, then the pairwise comparisons across levels of that factor can then be tested at the 0.05 level without further adjustment.

However, the model includes two

- and three

- factor interactions, so that a main effect for, say, generation, may not be significant even though generation has a significant impact on results. Thus,

if a two

- or three

- factor interaction involving generation is significant at the 0.05 level, then comparisons of levels of generation at the 0.05 significance level are done without further adjustment. Similar statemen ts can be made for week.

Next are F

- tests for significance of treatment within weeks and generations, the so

- called ‘slices’ in the ANOVA table. If, for example, the slice for treatment within generation F1 and week 12 is significant at the 0.05 level, th en the pairwise comparisons for treatment within that generation and week are made at the 0.05 level without further adjustment. Similar statements apply to tests for week within a generation and treatment and for generation within a week and treatment.Finally, for comparisons not covered by a significant slice F

- test, comparisons are adjusted using the

Bonferroni

- Holm adjustment to p

- values. Separate adjustments are made for tests of each factor.

The model includes all main effects, two

- factor interact ions, and the three

- factor interaction. It is quite likely that some of these interactions and main effects will not be significant. It is possible to implement a scheme that would first eliminate the three

- factor interaction if it is not significant, th en eliminate the least significant two

- factor interaction (if not significant at the .05 level) and continue in sequential fashion until only significant factors and interactions remain in the model. Only if no interaction involving a factor is significan t, would a non

- significant main effect be eliminated.

There are several problems with the above procedure. First, eliminating a term at step 3, for example, might make a term eliminated at step 2 now significant. Thus a stepwise procedure might be adopte d, such as used in stepwise regression. This can be done if the model is formulated carefully, though it can be tedious and can produce misleading results. There is another problem that arises from the need to make multiple comparisons beyond the ANOVA F

- tests. It can be quite challenging to determine how to test a comparison of interest when some interactions are eliminated from the model. The most straightforward procedure is to leave all terms in the model so as to accommodate the various comparisons of interest. There is nothing wrong with retaining a non

significant term in the final model. This could be a problem with a very complex model with a large number of potential explanatory variables that should be reduced to a manageable number. In the

pre sent case, there are only three basic model terms, generation, week, and treatment, all of which have very clear meaning and do not overlap. Moreover, it is entirely plausible for all factors to be important, and for egg production to vary across weeks wi thin the same treatment group and for different generations to show different responses to the treatments. It thus is justifiable to retain all model terms. This will also allow easier comparisons of chemicals, as all are modeled in the same

way.

The sta tistics flow

- chart for multi

- generation egg production studies are captured in Figures 1a

- 1c.

Analysis of egg production in one generation The same general procedure can be followed if a single generation is analyzed. In this case, there are

two main effe cts, treatment and week, and a single two

- factor interaction. Dunnett’s test still controls the false positive rate for comparisons of treatment main effects, i.e., treatments averaged across weeks. For main effects of weeks, i.e., comparisons of weeks ave raged across treatments, if

either the week main effect or the week

- by

- treatment interaction is significant at the 0.05 level, then the comparisons of weeks averaged across treatments or within a treatment are done at the 0.05 level with no further adjustm ent. Similarly, if the two

- factor interaction is significant at the 0.05 level, then treatments within the same week are compared at the 0.05 level without further adjustment to the p

- values. If the two

- factor interaction is not significant, then compariso ns of weeks within a treatment or treatments within a week are compared using Bonferroni

- Holm adjusted p

- values.Appendix , Section 4: Analysis of Histopathology Data via RSCABS

The following is a detailed description of the RSCABS procedure and its rationale including example data and analysis provided by John Green. In addition but not included in this ISR, John Green (Du Pont), Timothy Springer (Wildlife International) and Amy Saulnier (The Franklin Company) are preparing a publication detailing the statistical analysis of histopathology endpoints. However at this time, it is not ready for distribution.



### Histopathology data require statistical m ethods different from those used for continuous responses, such as growth or VTG, and f r om those used for quantal data, such as mortality or sex rever s al, since for a histopathology response, an ani m al is graded on an ordered but non

- nu m eric scale, usually 0

- 5, with 0 corresponding to no finding, 1 for

m i n i m al, 5 for very severe and 2, 3, and 4 i n creasing degrees of severity between 1 and 5.

The scores 0

- 5 carry no nu m erical implications other than order.

Histopath o l ogy respon s es can co m e from a s i ngle generation or from a multi g enerati o n study. The Cochran

- Mantel

- Haenszel and Chi

- S quare tests can be done to test for generational differences. Cochran

- Mantel

- Haen s zel (CMH) tests can b e d one for generational difference s across the study and w i thin treat m ent and for treat m ent effe c t s across generations. For a single generation or for treat m ent e f fects within a generation, a

Chi

- Square test can be done. An exact per m u t ation version of the Chi

- Square test with a

Monte Carlo si m ulation to esti m ate v aria n ce is preferred due to low and uneven

f requency of observatio n s. If there a r e m ultiple f ish per re p li c ate ( m uch the pre f err ed study design), then a CMH test can be done for between

- rep heterogeneity within

### generations. To illu s t rate this ap p roach, an experi m ent with medaka expo s ed to

trenb o lone f rom the USEPA Duluth m ulti

- ge n e r ation rese a r c h work will be used.

### Table 1: Experi m ental Design for 8

- W eek Study

As is evident from Table 1, the experi m ental setup is highly unequally replicated. W hile there are, with only a few exceptions, six rep l icates per treat m ent per generation, the total

nu m ber of f i sh and the nu m bers of males a nd fe males per re pl icate are h i g hly variable.

That has i mplications for how the data should be analyzed.

So m e CMH results are given in Table 2. The first row, for f e male Asynchronous Develop m ent/Malfor m ation, Gonad or Gon_Asynch_Dev_Mal, gives the test for

“General A s sociation” a f ter a d just m ents are m a de for concentration differences. This test

indicates that there are no differences b e tween t h e generatio n s except th o se that are

tre a t m ent related. This c a n be int e rpr e ted as a che c k again s t n o n

- tr e at m ent rel a ted e ff ects. The second row shows that there were no sig n ificant treat m e nt ef f

ects within generation F2. Since nothing is given for the F1 gener a tion, there were evidently no data or the severity was constant across all treat m ent groups and control (and the latter was in fact

the ca s e). T he third row shows there was no significant difference between the 13 ug/L group and the control in the F2 generation and the fourth row shows there was no significant difference between the generations for f e m ales at the 13 ug/L exposure group.

The re m aining table entries are of one of these types. To exp l ore this a bit m ore, consider gonadal Increased Oocyte Atresia or Gon_Incr_Oocyte_Atresia. There are significant

differences between each of the treat m ent groups and con trol wit h in each generation, except for the two highest test concentrat i ons (32 and 84 ug/ L ) in the F1 generation.

What the CMH test does not indicate is wheth e r the sev e rity scores in the tre a t m ent groups tend to be higher or lower than those in the contr ol. So the CMH results are

so m e what analogous to ANOVA F

- t ests: They indicate that differences exist but not in which direction or by how m uch. A visual inspection of summary data, such as given in Table 3, would m ake clear, at least in the si m plest cases, what the direction of change i s

.

### Where the distribution of scores is less t r ansparent, visual inspection m ay be an unsatisfactory way to understand t h e i mplication of a CMH test.

Screening tests are perfor m ed to deter m ine whe t her a che m ical is an endocrine disruptor, and in principle, a NOEC is not needed. So one m ay inquire why not perform a s i m ple F

test for a continuous endpoint or Chi

- square test for inciden c e or score data? Such tests detect differences between any of the treat m en t groups, ignoring direction. Only

co mparisons of treat m ent groups to control are of interest. Tests for significant trend are consistent w ith the expectation of a m onotonic dose response. I m proved power from trend tests, with no power penalty f or ide n ti f ying the NOEC.

This can be explored further by using the Mann

- Whitney (or, equivalently, the W ilcoxon) test. This test requires only that the responses be ordered, which is true for histopathology scores. It does not require t h at the treat m ents be nu m ber s in any m eaningful way other than being ordered (e.g., score 3 indicates g r eater severity than scores 0, 1, or 2). Technically, the responses m ust be m easu r ed on an ordinal scale but need not be

m easured on a ratio or e v en an inter v al scale.

Table 3 gives Mann

- W hitney test results for gonadal Increased Oocyte Atresia.

The Mann

- W hitney te s t results t h emselves s t ill do not provide the direction of change

and the tables of incidences m ust be cons u lted f or that. It is interesting that the Mann

W hitney te s t is m ore sensiti v e than the CMH in that is d ecl ar es signi fi ca n t the d i ff erence between the high test concent r ation and the control in the F1 generation. However, the frequency table shows that the high test concentration has le s s severe scores than the

contr o l, whereas all the o t her s t atistically significant treat m ent effects show more severe scores in the treat m ent groups relative to the contr o l. Clearly, so m e caution is required to

int e rpr e t th e se st a ti s ti c al f i ndings. It is possible to present the Man n

- W hitney results differently so as m ake clear the direction of effect.

There are is s ues with t h e CMH, Chi

- Squar e , and Mann

- W hitney tests that m ake th e m less t h an id e al.

### Table 2: Cochran

- Mantel

- Heans z el Test Results, Part 1

If fish are grouped m ore than one to a tank in m ulti p le tanks per te s t concentration a n d control, as is the case with the trenbolone exa mple data, then the CMH and chi

- Square tests ignore the replicate nature of the experi m ent. This m ay also be true of the Mann

Whitney, depending on whether individual fish a r e counted or replicate medians are used. That is, the r e is no a llo w ance f or re pl icate

- t o

- re pl icate va r iability in addition to f i sh

- t o

f ish within

- r epli c ate v a riability. This is the s a m e issue as arises with probit analysis (and

### so m e other m ethods) for analyzing quantal responses. It is an issue with the standard

Cochran

- Ar m itage test as well. One could base the Mann

- Whitney test on replicate

m edian score, but this would ignore variability w ithin repli c ate and also ignore differences in replicate size (nu m ber of fish per repli c at e ). W here t h ere is little v ariation

in re p lic a te s izes, this latter poi n t is n ot se r ious. T he replic a te is the p roper unit of analysis, so loosing the between

- rep variation, as with CMH and Chi

- Square and one

version of Mann

- W hitney, is proble m atic.

A 1

- generational Kruskal

- W allis test for treat m ent effec t s can also be done for each generation. The Kruskal

- W allis te s t is another om nibus test for wh ether there are

differences a m ong the treat m ent groups and c ontrol within a gener a tion. If significant, it does not indicate which differences are sig n i f ic a nt, only t h at there are s i g ni f icant

differences. Si m ilar to the ANOVA F

- test, the Kruskal

- W allis tes t is not a good indicator of whether toxicity effects exi s t. A s i gni f icant K

- W m ay m ean so m e two tre a t m ent groups differ from each other witho u t any treat m ent group di ffering significantly fr o m the control. On the other hand, a significant difference between a treat m ent group and the

contr o l can exist witho u t the K

- W test being s ig n i f icant b e c a use th a t te s t guards ag a i n st

co mparisons of no interest to toxicologists and thereby looses power to d e tect the sp ec i f ic differences that are of interest. So, regardl e ss of whether the K

- W test is significant,

Mann

- W hitney tests should be done to deter m ine which, if any, treat m ent groups differ signi f i ca n tly f r om the control. E ith e r an adj u st m e nt to the p

- values for the nu m ber of

co mparisons m ade per generation is done or no adjust m ent of p

- values is m ade for the

nu m ber of co mparisons m ade of treat m ents to controls. Adjusted p

- values m ay cause

so m e differences of interest to be m i ssed, which unadjusted p

- values would cause an

elevated fal s e positive rate. The rec o mmendat i on is to use Bonferroni

- Holm adjusted p

values, as a reasona b le co m pro m ise between the extre m es of no adjustement and a very crude adjust m ent such as Bonferroni (without the Ho l m m od i fi cation).

Table 3: Mann

- W hitney Test Results for F e m ale Gonadal Increased Oocytes Atresia

Table of Conc by Gon_Incr_Oo c yte_Atre s ia

- Gener a tio n =F1

### Table 4: Conc by Gon_ I ncr_Oo c yte_Atre s ia

- Gener a tio n =F2

Table 5: Summary Statistics for Gon_Incr_Oocyte_Atresia from
TREN03MEDAKA_8_FEMALES

1

- Generati o n Kruskal

- Wallis Te s t s for Trea t m ent Effects on Gon_Incr

_ Oocyte_Atresia

Generation response p_KWTest Signif

F1 Gon_Incr_Oocyte_Atresia 0.000101 *

F2 Gon_Incr_Oocyte_Atresia 0.017904 *



### Table 6: 1

- Gen Mann

- Whitney Tests for Treat m ent Effects on Gon_Incr_Oocyte_Atresia

One proposal being investigated by USEPA Dulu t h is to rank

- order a n i m als for each histopathology response, that is score the fish 1

- N with N the total nu m ber of fish in the study and a score of 1 corresponds to the lea s t severe and a score on N corresponds to the most severe finding of that particular histopathology endpo i nt. If this procedure is followed, the analysis is by the ANOV A bas e d on the indicated rank

- order. A concern with this approach is that a statistically significant effect m i ght be found that corresponds

### X 

# ( X

− X 

# ) to a very m i ld increase in severity of no biological i m portance. T h e likelihood of this is

still under investigat i on. Further detail and examples will be presented to the OECD

VMG

- eco Fish Drafting Group at the June, 2010 m eeting.

There are concerns with all the m ethods discussed up to this point. As pointed out above, one is ignoring either the repl icate tank design of the experiment or the unequal sa m ple

sizes. Further m ore, what none of these t e sts does is m ake use of any expected monotonicity or trend in the concentration

- response. This is a fund a m ental biological issue and ignoring it is not sound science. An alternative procedure is discussed next to

handle t h ese concerns a n d also to q ua nti f y the se v erity of e ffect in a clear m anner. The procedure to be described does that and, as a result, has greater power to detect true

tre a t m ent ef f

ects while a t the s a m e ti m e redu c ing the chance of a declari n g significa n t a spurious effect at a lower conce n tration that is not seen at h i gher test concentrations. T his is analogous to the i m p r ove m ents in analyzing c o ntinuous re s ponses by t h e W illia m s or Jonckheere tests co m pared to Dunnett or anoth e r pairwise m ethod and is derived from the

Cochran

- Ar m itage test for quantal data.

Histopath Scores Compared to Incidence Data Score data can be converted to incidence data by recording just presence or absence.
However, important in f or m ation m ay be lost in the process. The basic concepts of

incidence data are shown in the f oll o wing table.

Table 7A: General Form of Incidence Table

A si mple chi

- square stati s tic tests for independence of the cell counts and dose by

stand ar d i z i n g the comparison of the predicted cell cou n t under independence to what is

observed

. T he predicted value and test statistic are given by the

 X ij 

### = R i 

### * C j 

### N 

### χ 2



### = 

# ∑ ij

i

, j 

### ˆ 2

ij



### ˆ

ij 

### It is helpful to express the t e st statistic in ter m s of the p r edicted proportion of responders. This allows the expression of the m o del for this proportion, here shown as a constant.j 1 

# ( p

j j j j 

### Table 7B: General Form of Inciden c e Table Expressed as Proportions

The predi c t e d value and test s t a ti s tic, with k

- 1 degrees of freedo m , are given by

p  = R N

, p ij 

### = X ij 

### / C j



### χ 2



### = 

# ∑ ij

j 

### − p  

# ) 2



### V

j 

### , where V j 

### = p  j 

### ( 1

− p  )

C j



### The si mple chi

- square st ati s tic can b e significant because of change in within

- colu m n variance, or a single unusual value. It takes no account of any concentration

- response relationship. The Cochran

- Ar m itage test is built on a presumed linear relationship between dose and row proportions. T heory indic a tes linearity is not i m portant. Instead of modeling t h e proportion in each test concent r ati o n affected as consta n t , t h e Cochran

A r m itage test uses

p  = a + b * D

, where Dj is so m e m easure of concentration, and a and b are esti m ated from the data. Dj can be actual conc, log(conc), or si m ple equal spacing (0, 1, 2, 3, ...). This last option

is consi d er e d best but diff ers little fr o m using l o g(conc). The Chi

- Square statistics is then partitio n ed into a 1 deg r ee of f reedom test f or t r end and a k

- 2 degree of f reedom f or lack

of fit to this trend.

Standard tests for incidence data include (1) step

- down Cochran

- Ar m itage test, w/ or w/o

the Rao

- Sc o tt a d just m ent f or ext r a

- b etween

- r ep varia n ce (h e t eroge n eit y ) and (2) Fis h er

Exact test.

Example 1: Gonadal Increased O o cyte Atre s ia in F1 Fem a les

Consider the standard Cochran

- Ar m itage test for gonadal Increased Oocyte Atresia that treats any severity score grea t er t h an 0 as an inci d ence. Thus, the analy s is co m pares incidences of any degree of severity aga i nst complete absence (score 0) of gonadal Increased Oocyte Atresia.Heterogeneity Chec k of Between

- Replicate Variances

- Doses=6 For Response=Gon_Incr_Oocyte_Atresia with Severity Score >=1 Female data for generation F1

STATISTIC VALUE Conc DF PROB DOSES SIGNIF

Chi

- Square 11.2 0 5 0.047556 6 *

Chi

- Square 7.366667 32 5 0.194767 6

Table of Gon_Incr_Oocyte_Atresia1P by Conc

Gon_Incr_Oocyte_Atresia1P

Conc

Frequency| 0| 2| 5| 13| 32| 84| Total

--------- +

------ +

------ +

------ +

------ +

------ +

------ + 0 | 15 | 25 | 25 | 27 | 15 | 16 | 123

--------- +

------ +

------ +

------ +

------ +

------ +

------ + 1 | 6 | 0 | 0 | 0 | 2 | 0 | 8

--------- +

------ +

------ +

------ +

------ +

------ +

------ +
Total 21 25 25 27 17 16 131

STANDARD COCHRAN

- ARMITAGE TEST FOR Gon_Incr_Oocyte_Atresia in F1

Generation RSCORE DOSES T PROBT SIGNIF

F1 1 6

- 2.41223 .007927702 *

F1 1 5

- 2.08832 0.018384 *

F1 1 4

- 3.64173 .000135408 *

F1 1 3

- 3.36797 .000378625 *

F1 1 2

- 2.86606 .002078094 *



### So, by the step

- down Cochran

- Ar m itage test, there is a significant reduction in the proportion of f e m ale fish with severity score 1 or greater of gonadal Increased Oocyte

Atresia in e v ery te s t con c entr a tion c om pared to t h e contr o l. Note th a t the Mann

- W hitney

test did not f ind a significant reduction in the 32 ug/L concentration. However, the Chi

Square te s t f or betwee n

- r epli c ate h e t e rogeneity was m ildly si g ni f icant in t h e contr o l a n d

the Cochran

- Ar m itage test ignores that. Table 4 shows the control r e plicate infor m ation.

The table m akes clear t h at the d istri b ution of scores in re p s E and F are di f f

erent from

that in reps A

- D.

Table 8: F1 Controls by Replicate and Gon_Incr_Oocyt e _Atresia Score

The Rao

- Scott m odification of the C ochran

- Ar m itage te s t is designed to take account of

hetero g ene o us between

- rep variance as well as d ifferent re p licate s izes, as is also evident in Table 4. The analy s is is repeated using the R a o

- Scott Co c hran

- Ar m it a ge te s t

.RAO

- SCOTT COCHRAN

- ARMITAGE TEST for F1 Gon_Incr_Oocyte_Atresia SEVERITY

SCORE >= 1 FROM DATA TREN03MEDAKA_8_FEMALES

Generation RSCORE DOSES T PROBT SIGNIF

F1 1 6

- 2.38372 .008569268 *

F1 1 5

- 2.07268 0.019101 *

F1 1 4

- 3.60662 .000155103 *

F1 1 3

- 3.34664 .000408987 *

F1 1 2

- 2.86039 .002115616 *



### The differences with the standard C o chran

- Ar m itage te s t in t h is exa mple are m inor.

There is s till a proble m , however. Both the s tandard and Rao

- Scott a d justed Cochran

A r m itage test ignore i m portant infor m ation, n a mely that the severity of the histopathology finding. These tes t s treat a severity score of 1 the sa m e as a score of 5,

which m ak e s the analysis inco mplete, at best. Histopathology responses have the following structure.

Table 9: General Form of Table of Severity Scores

The proportion of responders in each severity level and dose are to be analyzed. Many approaches are pos s i ble. So m e, such as li n ear

- by

- linear association depend on rather

co mplex m o dels and are rather rigid in ter m s of the relationship between exposure concentration and score. Others ignore reps or rep sizes. The recom m enda t ion put

forward here is to use as m uch i m po r tant infor m a tion from the experi m ent as possible.

The Rao

- Scott Cochra n

- Ar m itage by Slices (RS C ABS) test is a serio u s atte mpt to do this.

The test can be desc r ibed si m ply as follows:

Rao

- Scott C ochran

- Ar m itage b y S li c es (RSCABS) test

• Step 1: For each rep, c o unt the nu m ber of fish in rep with s everity 1 or greater

– Apply stepdown Rao

- Scott Cochran

- A r m itage test to these counts

• Step 2: For each rep, c o unt the nu m ber of fish in rep with s everity 2 or greater

– Apply stepdown test Rao

- Scott Cochran

- Ar m itage to these counts

•

. . .

–

• Step K: For each rep, c o unt the nu m ber of fish in rep with s everity K or greater

– Apply stepdown Rao

- Scott Cochran

- A r m itage test to these counts

### Step 1 has already been de m onstrated above. It found every treat m ent group had significantly reduced incidence of severity score 1 or gre a ter gonadal Increased Oocyte

Atresia compared to the contr o l. Below are the re m aining steps.

Step 2

RAO

- SCOTT COCHRAN

- ARMITAGE TEST FOR F1 Gon_Incr_Oocyte_Atresia SEVERITY

SCORE >= 2 FROM DATA TREN03MEDAKA_8_FEMALES

Generation RSCORE DOSES T PROBT SIGNIF

F1 2 6

- 3.26758 .000542349 *

F1 2 5

- 3.30818 .000469527 *

F1 2 4

- 3.27534 .000527673 *

F1 2 3

- 3.03267 .001212007 *

F1 2 2

- 2.58008 .004938894 *



### Conclusions for Example 1 for Gonadal Increased Oocyte Atresia Thus, at step 2, there is a significant reduction in the propo r tion of f e m ale fish with severity score 2 or greater of gonadal I n creased Oocyte Atresia in every test

concentrati o n co mpared to the co n t r o l. There were no severity score 3 or greater in any

test concent r ation in the F1 generati o n, so the test stops here. There are t w o conclusions. First, there is a significantly reduced inc i de n ce o f positive se verity scores for gonadal Increased Oocyte Atresia in every test co n centration co m pared to the control. Second, there is a significantly reduced incidence of severity score 2 or greater for gonadal Increased Oocyte Atresia in every test conce n tration co m pared to the co nt rol. Since t h ere is no severity score of 3 or greater, the stati s tic a l signi f ica n ce indic a tes a m ini m al to m ild

treat m ent associated change in gonadal Increa s ed Oocyte Atresia, but one which appears in every te s t concentra tion. This si g nificance arises from two control replicates, E and F,

and m ay not be of real biological i mportance.

### Example 2: Gonad Stage Male F1

Table 10: Data for Go n ad Stage Male F1

1

- Generation Kruskal

- Wallis Tests for Treatment Effects on Gon_Stage

Generation response p_KWTest Signif

F1 Gon_Stage 0.00672 *



### The Kruskal

- W allis te s t indicates there are s ignificant differe n ces a m ong the contr o l and

treat m ent groups.

1

- Generation Mann

- Whitney Tests for Treatment Effects on Gon_Stage

Generation Test p2_Wil Signif Response

F1 2 vs 0 0.30043 Gon_Stage

F1 5 vs 0 0.00852 * Gon_Stage

F1 13 vs 0 0.96477 Gon_Stage

F1 32 vs 0 0.26381 Gon_Stage

F1 84 vs 0 0.32873 Gon_S tage



### The Mann

- Whitney tests indicate the 5 ug/L c o ncentration group tends to have a different gonad stage than the controls. An inspection of the data indicates t h e fish exposed to 5 ug/L trenbolone tend to have a later gonad stage than the controls. S i nce the higher

concentrati o ns are not significantly d ifferent from the control, the result at 5 ug/L is possibly spurious.

Next, consi d er the RSCABS analysis.

Heterogeneity Check of Between

- Replicate Variances

- Doses=6

For Response=Gon_Stage with Severity Score >=1 Male data for generation F1

STATISTIC VALUE Conc DF PROB DOSES SIGNIF
Chi

- Square 2.236667 0 5 0.815522 6

Chi

- Square 4.210526 5 5 0.519521 6

Chi

- Square 3.958333 32 5 0.55543 6



### There is no significa n t evidence of h eter og eneo u s between

- rep varia n ce. There are

di ff erences in replicate sizes. RSCABS will be d o ne.

Percent Go n _Stage with Severity > = 1 is constan t . (The lowest sta g e is 1.) No test can be

done.Heterogeneity Check of Between

- Replicate Variances

- Doses =6 For Response=Gon_Stage with Severity Score >=2 Male data for generation F1

STATISTIC VALUE Conc DF PROB DOSES SIGNIF
Chi

- Square 6.341176 0 5 0.274422 6

Chi

- Square 4.095076 2 5 0.53581 6

Chi

- Square 5.884615 13 5 0.317609 6

Chi

- Square 5.62963 32 5 0.343941 6



### There is no significa n t evidence of h eter og eneo u s between

- rep varia n ce. There are differences in replicate sizes.

MALE Generation F1 with Scores>=2
Table of Gon_Stage2P by Conc

Gon_Stage2P

Conc

Frequency| 0| 2| 5| 13| 32| 84| Total

--------- +

------ +

------ +

------ +

------ +

------ +

------ + 0 | 5 | 8 | 0 | 4 | 1 | 0 | 18

--------- +

------ +

------ +

------ +

------ +

------ +

------ + 1 | 17 | 11 | 20 | 13 | 18 | 16 | 95

--------- +

------ +

------ +

------ +

------ +

------ +

------ +
Total 22 19 20 17 19 16 113

RAO

- SCOTT COCHRAN

- ARMITAGE TEST FOR Doses=6

Generation RSCORE DOSES T PROBT SIGNIF

F1 2 6 2.73001 .003166604 *

F1 2 5 1.87718 0.030246 *

F1 2 4 0.99012 0.16106



### The 84 and 32 ug/L groups have significa n tly greater proportion of gonad stage 2 and higher than the controls. The 5 ug/L group is not found to have a significantly greater proportion of gonad stage 2 and higher than the controls. (Note: In the Table of Gon_Stage2P by Conc, the Row labeled 0 is tho s e fish with stage less than 2 and the row labeled 1 is for those fish with stage 2 or higher.) It m ight seem strange that the proportion of stage 2 or higher f i sh in the 5 ug/L group is hig h er than that in the 32 and 84 ug/L groups but is not significant, while the rates in 32 and 84 ug/L groups are signi f ica n t. This is t h e r esult of applying a trend

- based test such as Rao

- S cott Cochran

Ar m itage which ta k es all the d a ta up to the h igh e st in c luded c oncent r ation into a c cou n t. The incidence rate at 13 ug/L is relatively low, so the trend through that c o ncentration is not significant and testing stops. The high incidence at 5 u g /L is not follo w ed by a high

incide n ce at the next te st ed concentration and is thus considered insufficient evide n ce of

a tr e at m ent

- rel a ted e ff ect.Heterogeneity Check of Between

- Replicate Variances

- Doses=6 For Response=Gon_Stage with Severity Score >=3 Male data for generation F1

STATISTIC VALUE Conc DF PROB DOSES SIGNIF
Chi

- Square 6.337222 0 5 0.274774 6

Chi

- Square 2.454167 2 5 0.783381 6

Chi

- Square 6.27451 5 5 0.280417 6

Chi

- Square 6.296296 13 5 0.278446 6

Chi

- Square 3.80406 32 5 0.577957 6

Chi

- Square 1.733333 84 4 0.784654 6



### Again, there is no sig n ificant e v idence of between

- rep h e tero g eneity. Th er e are s till differences in replicate sizes.

MALE Generation F1 with Scores>=3
Table of Gon_Stage3P by Conc

Gon_Stage3P

Conc

Frequency| 0| 2| 5| 13| 32| 84| Total

--------- +

------ +

------ +

------ +

------ +

------ +

----- + 0 | 12 | 11 | 3 | 8 | 6 | 8 | 48

--------- +

------ +

------ +

------ +

------ +

------ +

----- + 1 | 10 | 8 | 17 | 9 | 13 | 8 | 65

--------- +

------ +

------ +

------ +

------ +

------ +

----- +
Total 22 19 20 17 19 16 113

RAO

- SCOTT COCHRAN

- ARMITAGE TEST FOR F1 Gon_Stage SEVERITY SCORE >= 3

FROM DATA TREN03MEDAKA_8_MALES

Generation RSCORE DOSES T PROBT SIGNIF

F1 3 6 0.87533 0.19070



### There is no significant concent r ation

- response trend in the i n cidence of gonad stages 3 and higher.

Heterogeneity Check of Between

- Replicate Variances

- Doses=6

For Response=Gon_Stage with Severity Score >=4 Male data for generation F1

STATISTIC VALUE Conc DF PROB DOSES SIGNIF
Chi

- Square 3.289815 0 5 0.655401 6

Chi

- Square 2.955556 2 5 0.706838 6

Chi

- Square 7.986111 5 5 0.157003 6

Chi

- Square 4.476667 13 5 0.483011 6

Chi

- Square 8.382353 32 5 0.136384 6

Chi

- Square 5.511111 84 4 0.238755 6 

### Again, there is no sig n ificant e v idence of between

- rep h e tero g eneity. Th er e are s till differences in replicate sizes.MALE Generation F1 with Scores>=4
Table of Gon_Stage4P by Conc

Gon_Stage4P

Conc

Frequency| 0| 2| 5| 13| 32| 84| Total

--------- +

------ +

------ +

------ +

------ +

------ +

------ + 0 | 18 | 18 | 12 | 15 | 17 | 12 | 92

--------- +

------ +

------ +

------ +

------ +

------ +

------ + 1 | 4 | 1 | 8 | 2 | 2 | 4 | 21

--------- +

------ +

------ +

------ +

------ +

------ +

------ +
Total 22 19 20 17 19 16 113

RAO

- SCOTT COCHRAN

- ARMITAGE TEST FOR F1 Gon_Stage SEVERITY SCORE >= 4
FROM DATA TREN03MEDAKA_8_MALES

Generation RSCORE DOSES T PROBT SIGNIF

F1 4 6 0.21230 0.41594 

### There is no significant concent r ation

- response trend in the i n cidence of gonad stages 4 and higher.

Conclusions for Example 2 for G onad Stage in Male F1 Medaka There was a significant tendency for an inc r ease in gonad stage 2 fish at 32 ug/L and above, but no indication of an i n crease in stages 3 or 4 fish at these exposure levels. This is quite di ff erent f rom what is o b tai n ed f rom the Mann

- W hitney te s t which indi c ated a significant increase in gon a d stage in the 5 ug/L group but not in any higher

concentrati o n. Also, the Mann

- W hitney te s t gave no indication of how s evere the

incr e ase wa s
, m erely th a t it was sig n i f icant. The p erce n tages o f stage 1 f i sh were 23, 42, 0, 24, 5, and 0 at 0, 2, 5, 13, 32, and 84 ug/L, re s pecti v ely. No trend is evi dent in t h ese percentages below 32 ug/L.

Example 3 from Trenbolone Exposures for 16 Weeks

The results and data for this exa m ple ca m e from a different e x peri m e nt with trenbolone

on 16 week old ani m als.

Table 11: R SCABS for F e m ale F1 Kidn e y Tubules, Epitheliu m , Hypereosinophilia

### Table 12: MW for F e m a l e F1 Kidney Tubules, Epitheliu m , Hypereosinophilia

Table 13: D ata for Fe m a le F1 Kidney Tubules, E pitheliu m , Hypereosinophilia

So M W finds significantly increased F e m ale F1 Kidney Tubules, Epitheliu m
, Hypereosinophilia only at 84 ug/L exposure. RSCABS finds significant increases at 84, 32, and 13 ug/L. RSCABS is picking up on t h e trend towards affects in higher exposure

levels. MW considers evide n ce only from the control and a single exposure group and thus loses power. RSCABS does not find the five score 2 or 3 fish at 13 ug/L significant

at the slice S core ≥ 2 because the next higher concentrat i on has no fish with scores higher

than 1.

Summary o f All Results for Med a k a Exposed to Trenbolone for 8 W eeks

In the tabl e s below, su m m aries of the results of the Mann

- Whitney and R SCABS analyses are given, so t h at direct co m parisons can be m ade to the r e lative perfor m ance of

these two t e sts. Only the statistica lly significa n t f i ndings are shown.

### Table 14: F1 Female Mann

- Whitney Test Results

Table 15: F1 Female R SCABS Te s t Results

Discussion

The KW test found significant differences a m ong the groups f or Gon_Decr_Yolk_Formation , but the MW test failed to find any treat m ent groups

### different from the control. RSCABS found the 32 and 84 ug/L groups to differ signi f ica n tly f rom the control. The R SCABS results a re gi v en below.

Heterogeneity Check of Between

- Replicate Variances

- Doses=6 For Response=Gon_Decr_Yolk_Formation with Severity Score >=1 Female data for generation F1

STATISTIC VALUE Conc DF PROB DOSES SIGNIF
Chi

- Square 3.811508 32 5 0.576861 6



### No variance heterogeneity found.

Table of Gon_Decr_Yolk_Formation1P by Conc

Gon_Decr_Yolk_Formation1P

Conc

Frequency| 0| 2| 5| 13| 32| 84| Total

--------- +

-------- +

-------- +

-------- +

-------- +

-------- +

-------- + 0 | 21 | 25 | 25 | 27 | 14 | 16 | 128

--------- +

-------- +

-------- +

-------- +

-------- +

-------- +

-------- + 1 | 0 | 0 | 0 | 0 | 3 | 0 | 3

--------- +

-------- +

-------- +

-------- +

-------- +

-------- +

-------- +
Total 21 25 25 27 17 16 131

F1 Gon_Decr_Yolk_Formation SEVERITY SCORE >= 1 FROM DATA

TREN03MEDAKA_8_FEMALES

RAO

- SCOTT COCHRAN

- ARMITAGE TEST FOR Doses=6

Generation RSCORE DOSES T PROBT SIGNIF

F1 1 6 1.79990 0.035938 *

F1 1 5 2.66316 .003870540 *

RAO

- SCOTT COCHRAN

- ARMITAGE TEST FOR Doses=4 No Gon_Decr_Yolk_Formation with Score>=1

No test can be done.



### It is d i fficult to acce p t t h e significant effect a ttri b uted to the h igh te s t concentration. W hat has happened is that the increase in severity 1 or greater at 32 ug/L has made the trend through 84 ug/L significant. This is not typical of step

- down trend tes t s, but clearly this

exa m ple shows it can happen. This is the flip s i d e of the earlier exa m ple where the M ann

Whitney test found an inter m ediate test co n centration significant but not the two higher

test concentrations and RSCABS found significant the two h i gher te s t co n cent r atio n s but

not the int er m ediate. A reasonable i n terp r et a tion of the data af ter this f irst step in RSCABS is that for this endpoint would be a possible treatment effect at 32 ug/L, but questionable since it is not observed at a higher concentration. As indicated below, there was one fish in the 32 ug/L group with severity score 3, so an effect at this exposure

concentration cannot be dis m i ssed without further scientific judg m ent being brought to

bear.Heterogeneity Check of Between

- Replicate Variances

- Doses=6 For Response=Gon_Decr_Yolk_Formatio n with Severity Score >=2 Female data for generation F1

STATISTIC VALUE Conc DF PROB DOSES SIGNIF
Chi

- Square 7.96875 32 5 0.157966 6 

### No variance heterogeneity found.

Table of Gon_Decr_Yolk_Formation2P by Conc

Gon_Decr_Yolk_Formation2P

Conc

Frequency| 0| 2| 5| 13| 32| 84|Total

--------- +

------ +

-------- +

-------- +

-------- +

-------- +

-------- + 0 | 21 | 25 | 25 | 27 | 16 | 16 | 130

--------- +

------ +

-------- +

-------- +

-------- +

-------- +

-------- + 1 | 0 | 0 | 0 | 0 | 1 | 0 | 1

--------- +

------ +

-------- +

-------- +

-------- +

-------- +

-------- +
Total 21 25 25 27 17 16 131

F1 Gon_Decr_Yolk_Formation SEVERITY SCORE >= 2 FROM DATA

TREN03MEDAKA_8_FEMALES

RAO

- SCOTT COCHRAN

- ARMITAGE TEST FOR Doses=6

Generation RSCORE DOSES T PROBT SIGNIF

F1 2 6 1.03157 0.15114

Heterogeneity Check of Between

- Replicate Variances

- Doses=6 For Response=Gon_Decr_Yolk_Formation with Severity Score >=3 Female data for generation F1

STATISTIC VALUE Conc DF PROB DOSES SIGNIF
Chi

- Square 7.96875 32 5 0.157966 6


### No variance heterogeneity found.

Table of Gon_Decr_Yolk_Formation3P by Conc

Gon_Decr_Yolk_Formation3P

Conc

Frequency| 0| 2| 5| 13| 32| 84|Total

--------- +

------ +

-------- +

-------- +

-------- +

-------- +

-------- + 0 | 21 | 25 | 25 | 27 | 16 | 16 | 130

--------- +

------ +

--- ----- +

-------- +

-------- +

-------- +

-------- + 1 | 0 | 0 | 0 | 0 | 1 | 0 | 1

--------- +

------ +

-------- +

-------- +

-------- +

-------- +

-------- +
Total 21 25 25 27 17 16 131

F1 Gon_Decr_Yolk_Formation SEVERITY SCORE >= 3 FROM DATA

TREN03MEDAKA_8_FEMALES

RAO

- SCOTT COCHRAN

- ARMITAGE TEST FOR Doses=6

Generation RSCORE DOSES T PROBT SIGNIF

F1 3 6 1.03157 0.15114Heterogeneity Check of Between

- Replicate Variances

- Doses=4 For Response=Gon_Decr_Yolk_Formation with Severity Score >=1

Female data for generation F2

STATISTIC VALUE Conc DF PROB DOSES SIGNIF
Chi

- Square 5.971154 13 5 0.309036 4



### Both M W and RSCABS found signif i cant treat m ent related results for Gon_Incr_Oocyte_Atresia . The only difference in concl u s io ns was that the latter fo u nd

the 32 ug/L group signi f icant but the M W did not. All other conclusions were the sa m e. This was di s cussed in d e tail in Exa m ple 1 and will not be repeated here.

KW found s ignificant differ e nces among the groups for Gon_Perifolli c ular_Cell

_

HH

but the MW and RSCAB S tests found no tre a t m ent signi fi ca n tly di ff erent f rom the

control.

M W found s ignificant differences in Gonad Ph e notype in the two high test

concentrati o ns co mpared to the co n t rol. RSCABS provides m uch m ore detailed

in f or m ation. From Table 15, it is evident th a t th e re a r e sig n i f icantly reduced inci d enc e s of gonad phenotypes 2 and greater in these two high test concentration compared to the

contr o l. RSCABS also indicates in c reased incidence of gonad phenotypes 4 and 5 at 13 and 5 ug/L, though the detailed output provided below shows that the s e latter two results are driven by 2 fish in the control. T hat a nd the change in dire c tion suggest the 5 and 13 ug/L results are very likely spurious.

Gon_Phenotype4P

Conc FEMALE Generation F1 with Scores>=4
Table of Gon_Phenotype4P by Conc

Frequency| 0| 2| 5| 13| 32| 84|Total

--------- +

------ +

-------- +

-------- +

-------- +

-------- +

-------- + 0 | 2 | 0 | 0 | 0 | 7 | 16 | 25

--------- +

------ +

-------- +

-------- +

-------- +

-------- +

-------- + 1 | 19 | 25 | 25 | 27 | 10 | 0 | 106

--------- +

------ +

-------- +

-------- +

-------- +

-------- +

-------- +
Total 21 25 25 27 17 16 131

F1 Gon_Phenotype SEVERITY SCORE >= 4 FROM DATA
TREN03MEDAKA_8_FE MALES

RAO

- SCOTT COCHRAN

- ARMITAGE TEST FOR Doses=6

Generation RSCORE DOSES T PROBT SIGNIF

F1 4 6

- 6.97158 1.567E

- 12 *

F1 4 5

- 2.66207 .003883107 *

F1 4 4 2.04015 0.020668 *

F1 4 3 1.87736 0.030234 *

### M W reports no significant effect on gonad sta g e whereas RSCABS finds a significant in stage 4 in the high test concentratio n . The data b elow show a 12.5 % increase in s tage 4

f ish in the hi gh test con c entr a tion c om pared to t h e contr o l.

Table of Gon_Stage4P by Conc

Gon_Stage4P

Conc

Frequency| 0| 2| 5| 13| 32| 84|Total

--------- +

------ +

-------- +

------ +

-------- +

-------- +

-------- + 0 | 21 | 25 | 25 | 27 | 16 | 14 | 128

--------- +

------ +

-------- +

------ +

-------- +

-------- +

-------- + 1 | 0 | 0 | 0 | 0 | 1 | 2 | 3

--------- +

------ +

-------- +

--- --- +

-------- +

-------- +

-------- +
Total 21 25 25 27 17 16 131

F1 Gon_Stage SEVERITY SCORE >= 4 FROM DATA TREN03MEDAKA_8_FEMALES

RAO

- SCOTT COCHRAN

- ARMITAGE TEST FOR Doses=6

Generation RSCORE DOSES T PROBT SIGNIF

F1 4 6 2.54551 .005455849 *

F1 4 5 1.52472 0.063664



### Both MW and RSCABS report sig n i f icant in c re a se in testicular degeneration in the h i gh

test concentration. RSCABS m akes clear t h at t h is is an i n cre as e f rom total absence to

m in i m al.

M W and RSCABS report significant effects in Tubules, Epit h elium, Hyp e reosinophilia in the 32 and 84 ug/L test concentrations. RSCABS shows that these are due to increases in severity scores 1, 2, and 3, so the effects are m ild to m oderate.

M W and RSCABS report significant effects in Tubules, Epit h elium, Hyp e trophy in t h e 32 and 84 ug/L test concentrations. RSCABS s h ows that t h ese are d u e to increases in severity scores 1, 2, and 3, so the effects are m ild to m oderate.

M W and RSCABS report significant effects in Tubules, Epit h elium, Vac u olization in

the 84 ug/L test concentrations. RSCABS shows that these are due to i n creases in severity sc or es 1, so the effects are m i n i m al.

RSCABS reports a s ignificant effect in Tubules, Protein at the 82 ug/L test

concentration. M W does not. RSCABS m akes clear t h at t h is is a m i ni m al (seve r ity sc ore 1) effect.

M W and RSCABS report significant effects in li v er Eosinophilia in the 32 and 84 ug/L

test concentrations. RSCABS s hows that these are due to i n c r eases in se v erity scores 1,

so the effects are m inimal.

### Table 16: F2 Female Mann

- Whitney Test Results

Table 17: F2 Female R SCABS Te s t Results

Discussion

M W and RSCABS report significant effects in Increased Oocyte Atresia in the 13, 5, and 2 ug/L test concentrations. RSCABS m akes clear that these are all d u e to increa s es in severity sc or e 1, so the e f fects are m i ni m al.

The Kruskal

- W allis te s t found diffe r ences a m ong the groups in Gonad stage but neither

M W nor RSCABS found any treated effects.

M W found s ignificant effects in kidney Glomer u li, Min e rali z ation at 2 and 5 ug/L but not at higher test concentra t ion. RSCABS found no significant effects. The tables below

shows al m o st no difference in t h e i n cidence of score 1 or greater in these two test

concentrations. It is difficult to understand why M W finds significance.

FEMALE Generation F1 with Scores>=1 Table of Kid_Tub_Mineralization1P by Conc

Kid_Tub_Mineralization1P

Conc Frequency| 0| 2| 5| 13| 32| 84|Total

--------- +

------ +

------ +

-------- +

-------- +

-------- +

------- - + 0 | 16 | 19 | 19 | 22 | 12 | 14 | 102

--------- +

------ +

------ +

-------- +

-------- +

-------- +

-------- + 1 | 5 | 6 | 6 | 5 | 5 | 2 | 29

--------- +

------ +

------ +

-------- +

-------- +

-------- +

-------- +
Total 21 25 25 27 17 16 131FEMALE Generation F1 with Scores>=2 Table of Kid_Tub_Mineralization2P by Conc

Kid_Tub_Mineralization2P

Conc

Frequency| 0| 2| 5| 13| 32| 84| Total

--------- +

------ +

-------- +

-------- +

------ +

-------- +

-------- + 0 | 20 | 25 | 25 | 27 | 17 | 16 | 130

--------- +

------ +

-------- +

-------- +

------ +

-------- +

-------- + 1 | 1 | 0 | 0 | 0 | 0 | 0 | 1

--------- +

------ +

-------- +

-------- +

------ +

-------- +

-------- +
Total 21 25 25 27 17 16 131



### MW finds a significa n t effect in Tubules, Cell u lar Debris/ C asts in the 2 ug/L te s t concentration but not at any h i gher test concentration. W hile there is an increa s e of 21% in the severeity score 1 fish at the 2 ug/L e xposure, the fact that t h is is not observed at higher test concentrations suggests it is a spurious finding. There is clearly no

concentration

- response trend in the data. W ith one exception in the 2 ug/L test

concentrati o n, all findi n gs are m i ni m al (have severity score 1).

FEMALE Generation F2 with Scores>=1 Table of Kid_Tub_Cellulardebris_casts1P by Conc

Kid_Tub_Cellulardebris_casts1P

Conc Frequency| 0| 2| 5| 13| Total

--------- +

-------- +

-------- +

-------- +

-------- + 0 | 23 | 11 | 22 | 26 | 82

--------- +

-------- +

-------- +

-------- +

-------- + 1 | 0 | 3 | 2 | 1 | 6

--------- +

-------- +

-------- +

-------- +

-------- +
Total 23 14 24 27 88

F2 Kid_Tub_Cellulardebris_casts SEVERITY SCORE >= 1 FROM DATA

TREN03MEDAKA_8_FEM

RAO

- SCOTT COCHRAN

- ARMITAGE TEST FOR Doses=4

Generation RSCORE DOSES T PROBT SIGNIF

F2 1 4 0.13894 0.44475

FEMALE Generation F1 with Scores>=2

Table of Kid_Tub_Cellulardebris_casts2P by Conc

Kid_Tub_Cellulardebris_casts2P

Conc Frequency| 0| 2| 5| 13| 32| 84|Total

--------- +

------ +

-------- +

------ +

-------- +

-------- +

-------- + 0 | 20 | 25 | 25 | 27 | 17 | 16 | 130

--------- +

------ +

-------- +

------ +

-------- +

-------- +

-------- + 1 | 1 | 0 | 0 | 0 | 0 | 0 | 1

--------- +

------ +

-------- +

------ +

-------- +

-------- +

-------- +
Total 21 25 25 27 17 16 131F1 Kid_Tub_Cellulardebris_casts SEVERITY SCORE >= 2 FROM DATA

TREN03MEDAKA_8_FEM

RAO

- SCOTT COCHRAN

- ARMITAGE TEST FOR Doses=6

Generation RSCORE DOSES T PROBT SIGNIF

F1 2 6

- 1.43958 0.074994



### M W found a significant effect in Tubules, Epit h elium, Hyp e rplasia in the 5 ug/L exposure group but not at any higher exposure level, whereas RSCABS found nothing significant. As shown below, there is evidence of heterogeneity in the 5 ug/L exposure group, and this m ay affect the MW t est.

Heterogeneity Check of Between

- Replicate Variances

- Doses=4 For Response=Ki d_Tub_Epithe_Hyperplasia with Severity Score >=1 Female data for generation F2

STATISTIC VALUE Conc DF PROB DOSES SIGNIF
Chi

- Square 5.833333 2 4 0.211946 4

Chi

- Square 13.44 5 5 0.019587 4 *



### There is significant a m ong

- rep heterogeneity in the 5 ug/L group, as evident from the following data table, where Rep E has a different distribution of seve r ity s cores f rom the other reps. T he MW test m ay be overly sensitive to the sin g le severity sc or e 2 fish in rep

E. Table 18: Severity Sc o res for Ki d _Tub_Epithe_Hyperplasia in F2 F emales at 5 ug/L

FEMALE Generation F2 with Scores>=1 Table of Kid_Tub_Epithe_Hyperplasia1P by Conc

Kid_Tub_Epithe_Hyperplasia1P

Conc Frequency| 0| 2| 5| 13| Total

--------- +

-------- +

-------- +

-------- +

-------- + 0 | 23 | 12 | 20 | 27 | 82

--------- +

-------- +

-------- +

-------- +

-------- + 1 | 0 | 2 | 4 | 0 | 6

--------- +

-------- +

-------- +

-------- +

-------- +
Total 23 14 24 27 88F2 Kid_Tub_Epithe_Hyperplasia SEVERITY SCORE >= 1 FROM DATA

TREN03MEDAKA_8_FEMAL

RAO

- SCOTT COCHRAN

- ARMITAGE TEST FOR Doses=4

Generation RSCORE DOSES T PROBT SIGNIF

F2 1 4 0.095535 0.46195



### The relatively high incidence of severity scores o f 1 or greater at 5 u g /L is evidence of an effect at this one te s t co n centration. T he fact that th ere are no fish with ad v erse findin g s at the higher 13 ug/L test concentration sugges t s a possibly spurious effect at 5 ug/L and accounts for the RSCABS approach not finding this significant.

RSCABS found a significant increase in Gonad Proteinaceous Fluid se v erity score 1 fish at 13 ug/L, while MW found no t hing significant. So, because the 7% increase in m in i m al effects occured in the high test concentration, RSCABS found it significant. Had this

o c cured in an i n ter m ediate te s t c oncent r ation, it would not have been significa n t. The effect, if real, is m ini m a l

.

FEMALE Generation F2 with Scores>=1 Table of Gon_Proteinaceous_fluid1P by Conc

Gon_Proteinaceous_fluid1P

Conc

Frequency| 0| 2| 5| 13| Total

--------- +

-------- +

-------- +

-- ------ +

-------- + 0 | 23 | 14 | 24 | 25 | 86

--------- +

-------- +

-------- +

-------- +

-------- + 1 | 0 | 0 | 0 | 2 | 2

--------- +

-------- +

-------- +

-------- +

-------- +
Total 23 14 24 27 88

F2 Gon_Proteinaceous_fluid SEVERITY SCORE > = 1 FROM DATA
TREN03MEDAKA_8_FEMALES

RAO

- SCOTT COCHRAN

- ARMITAGE TEST FOR Doses=4

Generation RSCORE DOSES T PROBT SIGNIF

F2 1 4 1.67093 0.047368 *

### Table 19: F1 Male Mann

- Whitney Test Results

Table 20: F1 Male RSCABS Test Results

Discussion

The discussion of Gonad stage was g iven in Example 2 above and will n ot be re p eated

here.

M W found a significant effect in Tubules, Dil a tation in the 5 ug/L exposure group but not at any higher test concentration. S ince there w as no apparent concentration

- response trend in the data, RSCABS d i d not find this significant.

KW found s ignificant differences among groups in both T u bules, Epit h elium, Hypereo s i n ophilia and Tubules, Epithelium, Hypetrophy , but neither MW nor RSCABS found any significant treat m ent effe c t co mpared to the control.

M W found a significant effect in Kidney Tubules, Mineraliz a tion at the 2 ug/L exposure group but not at any higher test concentrat i on. RSCABS found significant, but m in i m a l, decreases in this histopath o l ogy finding at the two high test concentrations but not at 2 ug/L. Exa m i nation of the incidence of severity sc ore 1 below shows an i n crea s e in fi s h

with this sc o re in the two low e st test concentrations but no s u ch fish in t h e three hig h est

test concent r ation. For t h is re a son, RSCABS finds no significant upward trend in the data. On the other hand, RSC A BS does find evidence of a decrease in score 1 or higher fish in the highest two test concentrations. T a ken together, it would appear t hat t here is

no trea t m ent related effect on Kidney Tubules, Mineralization.

MALE Generation F1 with Scores>=1 Table of Kid_Tub_Mineralization1P by Conc

Kid_Tub_Mineralization1P

Conc Frequency| 0| 2| 5| 13| 32| 84|Total

--------- +

------ +

------ +

-------- +

-------- +

-------- +

-------- + 0 | 19 | 11 | 16 | 17 | 19 | 16 | 98

--------- +

------ +

------ +

-------- +

-------- +

-------- +

-------- + 1 | 1 | 7 | 4 | 0 | 0 | 0 | 12

--------- +

------ +

------ +

-------- +

-------- +

-------- +

-------- +
Total 20 18 20 17 19 16 110F1 Kid_Tub_Mineralization SEVERITY SCORE >= 1 FROM DATA

TREN03MEDAKA_8_MALES

RAO

- SCOTT COCHRAN

- ARMITAGE TEST FOR Doses=6

Generation RSCORE DOSES T PROBT SIGNIF

F1 1 6

- 2.51499 .005951835 *

F1 1 5

- 1.88798 0.029514 *

F1 1 4

- 0.72743 0.23348

MALE Generation F1 with Scores>=2
Table of Kid_Tub_Mineralization2P by Conc

Kid_Tub_Mineralization2P

Conc Frequency| 0| 2| 5| 13| 32| 84| Total

--------- +

------ +

------ +

-------- +

-------- +

-------- +

-------- + 0 | 19 | 18 | 20 | 17 | 19 | 16 | 109

--------- +

------ +

------ +

-------- +

-------- +

-------- +

-------- + 1 | 1 | 0 | 0 | 0 | 0 | 0 | 1

--------- +

------ +

------ +

-------- +

-------- +

-------- +

-------- +
Total 20 18 20 17 19 16 110

F1 Kid_Tub_Mineralization SEVERITY SCORE > = 2 FROM DATA
TREN03MEDAKA_8_MALES

RAO

- SCOTT COCHRAN

- ARMITAGE TEST FOR Doses=6

Generation RSCORE DOSES T PROBT SIGNIF

F1 2 6

- 1.40962 0.079326



### Table 21: F2 Male Mann

- Whitney Test Results

Table 22: F2 Male RSCABS Test Results

MW and RSCABS find significa n t effects at 13 u g/L. RSCABS m akes clear t h at t h is arises

from an increase in fish with s everity s c ores up to 4 and so is likely a real effect of int e rest. An inspe c tion o f the data s h ows that there are 4 fish out of 20 in the 13 ug/L concen trati o n with seve r ity sc o re 4 and 1 with severity score 1.