#226 
 
Guidance for Industry 
 
Target Animal Safety Data Presentation 
and Statistical Analysis 
 
Submit comments on this guidance at any time.  Submit  electronic comments on the guidance to 
http://www.regulations.gov.   Submit written comments to the Division  of Dockets Management 
(HFA-305), Food and Drug Administration,  5630 Fishers Lane, Rm. 1061, Rockville,  MD 
20852.  All comments should be identified  with the Docket No. FDA-2015-D-0839. 
 
For further information  regarding this document, contact Virginia  Recta, Center for Veterinary 
Medicine (HFV-160), Food and Drug Administration,  7500 Standish Place, Rockville,  MD 
20855, 240-402-0840,  e-mail:  virginia.recta@fda.hhs.gov. 
 
Additional  copies of this guidance document may be requested from the Policy and Regulations 
Staff (HFV-6), Center for Veterinary Medicine, Food and Drug Administration,  7519 Standish 
Place, Rockville,  MD 20855, and may be viewed on the Internet at either 
http://www.fda.gov/AnimalVeterinary/default.htm  or http://www.regulations.gov. 
 
 
U.S. Department of Health and Human Services 
Food and Drug Administration 
Center for Veterinary Medicine 
January 2016 
    Contains Nonbinding Recommendations 
Table of Contents 
 
 
I.  INTRODUCTION ...................................................................................................... 3 
II.  PRESENTATION OF TARGET ANIMAL SAFETY DATA.................................... 4 
A.  Data Generated from Margin of Safety Studies.............................................. 4 
1.  General Animal Data ................................................................................... 4 
2.  Physical Examination Data .......................................................................... 5 
3.  Health Observations .................................................................................... 5 
4.  Clinical Pathology ....................................................................................... 6 
5.  Necropsy and Histopathology Examination Data .......................................... 7 
B.  Data Generated from Field Effectiveness Studies ........................................... 8 
1.  Adverse Event Data from Field Effectiveness Studies................................... 9 
2.  Monitored Health Outcomes from Field Effectiveness Studies ...................... 9 
C.  Data Generated from Other Types of Studies............................................... 11 
III.  STATISTICAL ANALYSIS ..................................................................................... 12 
A.  Blocking ........................................................................................................ 12 
B.  Baseline Covariates ....................................................................................... 12 
C.  Continuous Variables.................................................................................... 12 
1.  Continuous variables measured once ...........................................................13 
2.  Continuous variables measured repeatedly ..................................................15 
D.  Categorical Variables.................................................................................... 18 
1.  Dichotomization of categories ....................................................................18 
2.  Analysis methods .......................................................................................18 
3.  Presentation of frequency tables..................................................................19 
E.  Variables Measured but not Statistically Evaluated ..................................... 19 
 
   
2  Contains Nonbinding Recommendations 
Guidance for Industry 
 
Target Animal  Safety Data Presentation  
and Statistical Analysis 
 
This guidance represents the current thinking of the Food and Drug Administration (FDA or 
Agency) on this topic.  It does not establish any rights for any person and is not binding on FDA 
or the public.  You can use an alternative approach if it satisfies the requirements of the 
applicable statutes and regulations.  To discuss an alternative approach, contact the FDA office 
responsible for this guidance as listed on the title page. 
 
I.  INTRODUCTION 
 
This document provides recommendations to industry regarding the presentation and statistical 
analyses of target animal safety (TAS) data submitted to CVM as part of a study report to 
support approval of a new animal drug.  These recommendations  apply to TAS data generated 
from both TAS and field effectiveness studies conducted in companion  animals (e.g., dogs, cats, 
and horses) and food animals (e.g., swine, ruminants,  fish, and poultry). 
 
TAS studies are usually laboratory studies conducted in the target species at one site containing 
multiple  treatment groups under good laboratory practice (GLP) regulations.1  Measurements are 
often collected for a large number of variables using a small number of animals per treatment.  
Field effectiveness studies also provide useful safety information  because the drug is 
administered under proposed field conditions  of use in a large number of animals. 
 
Measurements of a large number of variables are typically  collected in clinical  studies. The 
examples in this document are based on data collected from typical margin of safety studies, 
such as those described in FDA Guidance for Industry (GFI) #185.2  Although there are many 
ways to organize and summarize these data, we have found that some methods enable us to 
review the data and results more efficiently.  These methods are described in this guidance.  
Presenting the data in the manner described in this document will facilitate our review of the 
study report. 
 
Example tables are used throughout  this document.  Only portions of each table are presented to 
maintain the conciseness of this document. 
 
Although this guidance proposes specific methods of data presentation and statistical analysis, 
alternative strategies may be appropriate.  We recommend that you discuss your proposed 
methods for data presentation and analysis before submitting  any study protocol.  We 
recommend obtaining  protocol concurrence before initiating  any studies that you intend to use to 
                                              
1 21 CFR part 58. 
2 CVM Guidance for Industry #185/VICH GL43, Target Animal Safety for Veterinary Pharmaceutical Products, 
April 24, 2009. 
3  Contains Nonbinding Recommendations 
support approval.  If alternate study designs for evaluating safety are used, the examples in this 
guidance may still  be useful to organize and summarize the data. 
 
In general, FDAs guidance documents do not establish legally  enforceable responsibilities.   
Instead, guidances describe the Agencys current thinking  on a topic and should be viewed only 
as recommendations,  unless specific regulatory or statutory requirements are cited.  The use of 
the word should in Agency guidances means that something  is suggested or recommended, but 
not required. 
 
II.  PRESENTATION OF TARGET ANIMAL SAFETY DATA 
 
Safety data should be presented in a clear and organized manner.  Tables serve this purpose well.  
Tabular data presentation enhances efficient summarization  of pre-treatment and post-treatment 
findings  and comparison of treated and control group(s) responses. 
 
Tables should be clear and concise.  They should be titled appropriately and column and row 
headings should accurately describe the data being presented.  Terms and abbreviations  not 
addressed elsewhere in the document should be defined.  If symbols or footnotes are used, these 
should be referenced. 
 
A.  Data Generated from Margin of Safety Studies 
 
Example tables are presented for various types of frequently submitted safety data.  These 
examples serve to illustrate our recommendations  but with the diversity of studies 
conducted, there are other possibilities.  Because these are examples, only partial tables 
are presented, as indicated by serrated borders. 
 
1.  General Animal Data 
 
General animal data commonly include the following:   treatment group, 
animal identification  (ID), sex, age, and breed.  Additional  information  (pen 
assignment, feeding regimen, replicate, site, etc.) should be provided  as 
appropriate.  Table 1 provides an example of a recommended presentation 
format, shown with data that might be collected from a typical equine safety 
study. 
 
Table 1.  General Animal Information 
Treatment  Animal ID  Sex  Age  Breed 
Group  (Male Castrate [MC],  (years)  (Quarter Horse [QH], 
Female [F])  Thoroughbred  [TB]) 
1  F  2  QH 
4  F  3  TB 
1 (0X)  5  F  2  QH 
13  MC  5  QH 
18  F  5  QH 
 
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2.  Physical Examination  Data 
 
Although the results from all physical examinations  should be collected on 
standardized forms, we recommend creation of a table that includes only 
those animals observed to have abnormal findings  during physical 
examinations  (scheduled or unscheduled).   To facilitate examination  of the 
data for any potential  treatment-related effects, we recommend organizing 
the data by treatment groups, including  information  such as treatment group, 
animal ID, study day, area examined, description of abnormal examination 
finding,  and medical treatment or further follow-up.  If the medical treatment 
or further follow-up  information  is too extensive to be included in the table, 
the location of that information  should be clearly identified.   Table 2 presents 
abnormal findings  from our example of a typical equine study in this format. 
 
Table 2.  Abnormal Physical Examination  Findings 
Treatment  Animal  Study  Area  Abnormality  Outcome 
Group  ID  Day  Examined  and severity 
2  -14  Oral  Dry, pale  An additional  Physical Exam 
mucous  CRTa 3  was conducted on Day 20 and 
membranes  secsb  the CRT was normal.  The 
2  -7  Oral  Dry, pale  sclera was still slightly  yellow; 
mucous  CRT 3 secs  however, the horse had 
membranes  resumed eating and was bright 
2  0  Oral  Dry, pale  and alert.  Clinical  pathology 
mucous  CRT 3 secs  and necropsy findings  were 
membranes  reviewed and no abnormalities 
1 (0X) 
2  0  Eyes  Sclera  were reported except that Total 
yellow  Bilirubin  was elevated on Days 
bilateral  0, 7, and 14.  An additional 
2  7  Eyes  Sclera  serum sample was taken on 
yellow  Day 20 and Total Bilirubin  had 
bilateral  returned to normal levels. 
2  14  Oral  Yellow, dry 
mucous 
membranes 
a Capillary  Refill Time 
b seconds 
 
3.  Health Observations 
 
In addition  to raw data on all health observations,  we also recommend a table 
including  only those animals observed to have abnormal health or behavior 
on one or more study days.  Appropriate data may include treatment group, 
animal ID, study day(s), description  of abnormal health observation(s), action 
taken, and outcome of the abnormal event. Table 3 provides an example 
presenting abnormal observations from a typical equine safety study. 
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In some cases, abnormal health observations include data from physical 
examinations.  In such cases, the information  in the two tables may overlap.  
Also note that in Table 2, findings  from each examination  are presented 
separately (by day), while in Table 3 results are combined across days.  Both 
methods are useful and the format should be chosen that best communicates 
the study findings. 
 
Table 3.  Abnormal Daily Health and Behavior Observations 
Treatment  Animal  Study  Abnormal  Description of  Action/Outcome 
Group  ID  Day(s)  Health  Abnormal 
Observation(s)  Observation(s) 
1 (0X)  1  -7 to -3  Not eating grain  Picks at grain,  Molasses added to 
and  paws, consumes  oats 
 5 to 25  approx. 14 of 
total amount 
grain fed 
4  -5 to 15  Ocular  Mucopurulent  Triple antibiotic 
discharge,  copious  with 
bilateral  discharge from  hydrocortisone, 
both eyes,  ophthalmic 
rubbing eyes on  ointment TID; 
stall door/walls  Resolution  by Day 
15 
 
4.  Clinical  Pathology 
 
Tables showing clinical  pathology  results should include all animals, 
regardless of whether abnormalities  were noted.  Normal reference ranges 
should be provided in a convenient location including  citation of the source 
of the range (literature, laboratory-specific,  etc.).  High-low flags are 
acceptable but the numerical reference ranges should be provided.  Any 
abnormal or out-of-reference range values should be clearly identified.   If the 
table spans multiple  pages, column headings and reference ranges should be 
included on each page.  Table 4a is an example of a table structure for 
hematology  variables from an equine study with laboratory specific reference 
ranges.  Table 4b is an example presentation of some blood chemistry results.  
These tables are not intended to provide guidance on the appropriate clinical 
pathology  parameters or reference ranges for any particular safety study; they 
merely provide examples of data organization. 
   
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Table 4a.  Hematology:  Complete Blood Count 
Treatment  Animal  Study  WBC  RBC  HGB  HCT  MCV  MCH 
Group  ID  Day  (103/L)  (106/L )  (g/dL)  (%)  (fL)  (pg) 
[xx-xx]a  [xx-xx]  [xx-xx]  [xx-xx]  [xx-xx]  [xx-xx] 
-28  14.8 (H)b  10.90  18.6  54.1  52  17.1 
-14  8.2  9.55  16.2  47.9  39  16.9 
-7  10.7  9.16  15.5  45.9  47  17.0 
1 (0X)  1 
0  4.4 (L)c  9.74  16.5  48.7  49  16.9 
7  7.6  8.5  14.1  42.5  51  16.5 
14  8.2  8.0  14.0  40.0  42  16.3 
a Reference range (citation of source) 
b Above reference range 
c Below reference range 
 
Table 4b.  Clinical  Chemistry 
Treatment  Animal  Study  Albumin  A/G  AP  AST  ALT  Calcium 
Group  ID  Day  (g/dL)  Ratio  (/L)  (/L)  (/L)  (mg/dL) 
[xx-xx]a  [xx-xx]  [xx-xx]  [xx-xx]  [xx-xx]  [xx-xx] 
-28  2.8  0.6  327  403  6  10.6 
-14  2.6  0.5  222  326  8  9.9 
-7  3.3  0.6  212  382  5  11.2 
1 
0  3.4  0.5  400  378  6  12.0 
7  3.5  0.6  433  357  7  10.3 
1 (0X) 
14  1.6  0.2  750  242  8  9.8 
-28  3.5  0.4  332  484  5  9.8 
-14  3.6  0.5  356  445  5  10.2 
4 
-7  2.8  0.5  545  465  7  11.0 
0  3.0  0.5  678  490  9  12.2 
aReference range (citation of source) 
 
5.  Necropsy and Histopathology  Examination  Data 
 
Gross and microscopic pathology  observations should be organized by 
treatment to help identify  treatment-related findings.   Table 5a is an example 
for presenting a summary of gross necropsy findings  for all animals, by 
treatment group.  Additionally,  another table which summarizes gross 
necropsy findings  and corresponding  histopathology  for any abnormalities 
will help to provide insight into the primary pathologic  processes underlying 
each finding.  Table 5b is an example of how these findings  might be 
presented. 
 
   
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Table 5a.  Gross  Necropsy:  Summary of Findings  by Organ/Treatment Group/Sex 
Organ and  Males   Females 
Finding 
Treatment Group   1 (0X)  2 (1X)  3 (3X)  4 (5X)  1 (0X)  2 (1X)  3 (3X)  4 (5X) 
Number of  n   n   n   n   n   n   n   n  
Animals  
Heart 
- dilated 
lymphatic 
1 (23)a  None  None  None  None  None  None  1 (6) 
- tissue 
discoloration 
- effusion 
Lungs 
-atelectasis, right  1 (23)  None  2 (7,2)  1(14)  None  None  None  None 
and left lobes 
Right Dorsal 
Colon 
None  None  1 (7)  2 (14,10)  1(43)  None  2 (22,3)  2 (6,8) 
-ulcers 
-erosions 
Right Ventral 
Colon  None  1 (12)  None  None  None  1 (19)  1 (22)  None 
-ulcer 
a Number of animals affected (animal IDs in parentheses). 
 
Table 5b.  Associated Gross and Microscopic Pathology 
Treatment  Animal  Sex  Gross  Description of gross  Corresponding 
Group  ID    Necropsy  findings  (number, severity  Microscopic 
Finding  and/or extent)  Finding 
43  F  None  N/A  N/A 
23  M  Pericardial  Clear, increased amount  No corresponding 
effusion  of pericardial fluid  microscopic finding 
1 (0X)  23  M  Atelectasis of  Pulmonary edema;  Compressed alveoli 
right and left  Mucopurulent  exudate in  and bronchioles, 
lung lobes  both lungs, lungs dull red  decreased perfusion 
in color 
 
B.  Data Generated from Field Effectiveness Studies 
 
Safety data from field effectiveness studies may be presented in tables similar  to those 
described in the previous section.  Additionally,  two example tables are given for 
summaries of other safety data that are generally collected from field effectiveness 
studies.  However, the design of field effectiveness studies is more varied than that of 
margin of safety TAS studies and therefore no particular strategy can be provided for the 
presentation of safety data from these studies.  The study protocol should address the 
collection  and analysis of safety data from field effectiveness studies. 
 
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1.  Adverse Event Data from Field Effectiveness Studies 
 
Field effectiveness studies are an important source of TAS data in the 
target population  under the proposed conditions  of use.  Adverse events 
should be classified by body system (musculoskeletal,  gastrointestinal, 
etc.) and tabulated to help identify  associations between adverse events, 
treatment groups, and other study factors.  Table 6 is an example of a way 
to summarize adverse events from a multi-site  effectiveness study.  More 
details on specific animals or body systems may also be presented as 
shown in previous tables. 
 
Table 6.  Adverse Events: Summary by Body System and Site 
Body  Site 1  Site 2  Site 3  All Sites 
System  Control  Treated  Control  Treated  Control  Treated  Control  Treated 
Neurologic  0  0  0  0  1  0  1  0 
Eyes  0  0  1  0  0  0  1  0 
(Discharge) 
Gastrointestinal  0  0  0  0  0  1  0  3 
(Diarrhea) 
General  16  4  11  1  4  0  37  8 
(Dead) 
Musculoskeletal  4  1  2  0  0  0  6  1 
And Feet 
(Lameness) 
 
2.  Monitored Health Outcomes from Field Effectiveness Studies 
 
In addition  to a general listing  of adverse events, monitoring  of certain 
specific health outcomes may be planned during effectiveness trials.  Data 
summaries should demonstrate the appropriate measures of the number, 
duration, and frequency of a specific health outcome. 
 
As applicable,  parameters using scoring or other numerical data should be 
captured by describing the values seen in each animal and each treatment 
group for each study phase when data was collected.  Values might be 
described using means, ranges or other descriptive summaries.  Examples 
would be somatic cell scores and severity scores for quarter health or milk 
quality,  as in Tables 7a and 7b below. 
   
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Table 7a:  Monitored Health Outcomes: Scored or Numerical Values 
Somatic Cell Counts (SCC) in 1000 cells/mL 
Treatment  Animal ID  Pre-Treatment  Treatment  Withdrawal  Post-Treatment 
Group  Period  Period  Period  Period 
Treated  101, range  30  34  43 - 58  55 - 72  42 - 54 
102, range  31  50  80 - 96  83 - 98  40 - 45 
103, range  59  62  90 - 112  78 - 100  50 - 73 
Group Mean  41  90  87  47 
Control  201, range  40  45  44 - 70  62  72  64 - 70 
202, range  35  47  51 - 60  55  74  53 - 57 
203, range  60  71  53 - 59  55 - 71  52 - 68 
Group Mean  48  55  65  56 
 
Animal observations, such as milk quality and quarter health, that are evaluated using 
presence/absence criteria could be summarized using this example format: 
 
Table 7b:  Monitored Health Outcomes: Presence/Absence Criteria 
Milk Quality Abnormalitiesa 
Treatment  Pre-Treatment  Treatment  Withdrawal  Post-Treatment 
Animal ID 
Group  Period  Period  Period  Period 
101  flakes 
(Day 1 AM 
None  None  None 
& PM, Day 
2 AM)b 
102  flakes 
None  None  None 
Treated  (Day 4 AM) 
103  None  None  None  None 
Total 
Number of 
0  3  1  0 
Abnormal 
Observations 
201  None  None  None  None 
202  None  None  None  None 
203  flakes 
None  None  None 
(Day 4 AM) 
Control 
Total 
Number of 
0  0  1  0 
Abnormal 
Observations 
a presence of clots, flakes, or stringy, watery, or bloody milk 
b observed change (days/observation periods in parentheses) 
 
A safety variable may be observed over time and in multiple  animals 
during an effectiveness study.  Table 8 displays an example showing the 
10  Contains Nonbinding Recommendations 
incidence and type of cardiac arrhythmias seen in dogs after treatment 
with one of two drugs. 
 
Table 8. Monitored Health Outcomes: Summary for Groups over Time 
Number (%) of dogs for each cardiac arrhythmia category before and following  treatment at 0 
minutes with Drug 1 or Drug 2. 
Treatment  Categorya  Timepoint  (minutes) After Treatmentb, c 
-10  5  15  30  60  90  120  180 
Drug 1    n=9  n=32  n=76  n=118  n=130  n=187  n=63  n=24 
  AVB  0  2  4  0  0  0  0  0 
(6%)  (5%) 
  AVD  4  10  19  17  6  3   9  2 
(44%)  (31%)   (25%)  (14%)  (5%)  (2%)  (14%)  (8%) 
  BRDY  1  15  32  29  35  52  18  13 
(11%)  (47%)  (42%)  (25%)  (27%)  (28%)  (29%)  (54%) 
  PC  0  2  7  5  10  0  3  1 
(6%)  (10%)   (4%)  (8%)  (5%)  (4%) 
  TCHY  3  7  0  0  0  0  0  0 
(33%)  (22%) 
Drug 2    n=9  n=34  n=65  n=115  n=128  n=95  n=62  n=30 
  AVB  0  4  18  0  0  3  0  0 
(18%)  (28%)  (3%) 
  AVD  0  2  1  29  15  12  4  2 
(6%)  (2%)  (25%)  (12%)  (13%)  (6%)  (7%) 
  BRDY  0  23  13  54  49  29  7  4 
(68%)  (20%)  (47%)  (38%)  (31%)  (11%)  (13%) 
  PC  0  0  5  11  20  7  2  0 
(8%)  (10%)  (16%)  (7%)  (3%) 
  TCHY  2  0  12  0  0  0    0 
(22%)  (18%) 
aAVB = atrioventricular  block; AVD = atrioventricular  dissociation  or ventricular or junctional 
escape rhythm; BRDY = bradycardia; PC = supraventricular or ventricular premature complexes; 
TCHY = tachycardia. 
b Treatment (Drug 1 or Drug 2) was administered at 0 minutes. 
c Number of dogs (% of total) with available ECG recordings varied across time points and 
treatment group. 
 
C.  Data Generated from Other Types of Studies 
 
The examples presented in this document are from studies that are commonly  used to 
generate animal safety data, such as a margin of safety study and field effectiveness 
study.  Other types of studies may also be used to generate safety data.  These may 
include injection  site or administration  site safety studies, reproductive safety studies, 
mammary gland safety studies, or others as needed.  The information  collected in these 
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specialized studies may differ from that presented above, but the general principles  of 
data presentation described still apply. 
 
III.  Statistical Analysis 
 
The following  recommendations  are intended to summarize important concepts in the statistical 
analysis of safety data and to help outline useful approaches for the analysis and presentation of 
results. They are not intended to be an all-inclusive  list or to address every situation. 
 
In the analysis of safety data, the interpretation of results will be based on both clinical relevance 
and statistical significance.  CVM generally considers a significance level of =0.10 useful as a 
conservative screen for identifying potential  treatment-related safety concerns among endpoints 
in TAS studies.  This significance level allows more safety variables to be flagged for further 
consideration  of clinical  relevance than =0.05 or =0.01.  Pairwise mean comparisons between 
each treatment against the control group are also performed using an unadjusted  = 0.10. 
 
To facilitate the review process, the sponsor should provide:  statistical programs with 
documentation,  all statistical output (e.g., analysis results), raw data in its original  form, and raw 
data in an electronic format.  Refer to GFI #197 for guidance on adequate documentation of 
statistical analysis in data submissions to the agency.3 
 
A.  Blocking and Stratification 
 
CVM does not generally recommend the use of blocks in the design of TAS studies 
unless adequate justification  is provided  at the protocol stage.  If blocking  is a component 
of the study design, blocks should be included in the statistical model as a random effect.  
If stratification was employed in the design, the stratification factor should be included  as 
a random or fixed effect as appropriate.  For example, margin of safety studies are 
typically  stratified by sex.  In this guidance, sex is used as an example of a fixed effect. 
 
B.  Baseline Covariates 
 
Often baseline or pre-treatment data for a given variable are collected and should be 
included in the statistical model as a covariate.  The covariate should be included as a 
linear effect assuming a common covariate slope.  If a pre-treatment covariate is 
proposed in the design, it should be retained in the model regardless of its statistical 
significance. 
 
C.  Continuous Variables 
 
Blood chemistry values, body weights, and organ weights are examples of continuous 
variables collected in TAS studies.  These data are collected once or repeatedly on the 
same subject throughout the study.  Data are usually analyzed using analysis of variance 
(ANOVA) or analysis of covariance (ANCOVA) where the statistical model reflects the 
experimental design. 
                                              
3CVM Guidance for Industry #197, Documenting Statistical Analysis Programs and Data Files, December 2015. 
12  Contains Nonbinding Recommendations 
1.  Continuous  variables measured once 
 
Examples of continuous  variables measured once are organ weights at necropsy.  
The statistical model for the ANOVA or ANCOVA should be consistent with the 
experimental design.  If the subjects are stratified by sex, then the model should 
evaluate treatment, sex, and the treatment-by-sex interaction as fixed effects.  
Summaries of the statistical decision process and the outcomes of the statistical 
evaluations should be presented in tabular form.  Recommended criteria for 
evaluating analysis results and making comparisons are detailed below. 
 
a.  Evaluate the treatment-by-sex interaction ( = 0.10) and present p-values and 
interpretation  in a summary table.  If significant,  calculate least squares (LS) 
means and perform pairwise comparisons between treatments and the control 
within sex. If not significant,  proceed to step b. 
 
Table 9 is an example of a way to present p-values from overall tests of 
treatment effects from the statistical analyses. Tables 10a and 10b are 
examples of ways to present least squares means (LS means) and p-values 
from pairwise comparisons of treatment to control. 
 
b.  Evaluate the main effect of treatment ( = 0.10) and present p-values and 
interpretation  in a summary table (Table 9). If significant  treatment effects 
are detected, calculate LS means and perform pairwise treatment 
comparisons with the control (Table 10b).  If treatment effects are not 
significant,  include the p-value and interpretation in the table but additional 
comparisons should not be performed. 
 
c.  In addition  to the model-based analysis in (a) and (b) above, you should also 
summarize data using descriptive statistics.  Descriptive statistics should 
include number of subjects, arithmetic mean, standard deviation,  minimum 
value, and maximum  value.  Tables of descriptive statistics by treatment and 
treatment by sex should be provided.  Summary tables of descriptive 
statistics should not include results from inferential analyses such as p-
values. Table 11 is an example of how to present descriptive statistics by 
treatment within sex.  A similar  table excluding  the column for sex should be 
provided for descriptive statistics by treatment combined across sex. 
 
   
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Table 9.  P-values for Evaluating  Treatment Effects:  Data from a Canine Safety Study 
Variable  Evaluation  of  Evaluation  of  Decisions 
Treatment-by-Sex  Treatment 
Effect  Effect 
Heart Weight  0.0246 b   c  1 
Kidney Weight  0.2186  0.4215  3 
Liver Weight  0.3577  0.0456  2 
a Decisions: 
1 = The treatment*sex interaction is significant.  Follow up treatment mean comparisons will 
be made within sex, Table 10a. 
2 = The treatment*sex interaction is not significant  but the main effect of treatment is 
significant.  Follow up treatment mean comparisons will be made averaged over sex, Table 
10b. 
3 = There are no significant  fixed effects. 
b P-values in bold-face indicate statistical significance  ( = 0.10). 
c P-values of the treatment effect are excluded for variables that had a significant  treatment*sex 
interaction. 
 
Table 10a.  LS Means and P-values from Treatment Comparisons within Sex for Variables with 
a Significant  Treatment-by-Sex Interaction:  Data from a Canine Safety Study 
Treatment  Females  Males 
Variable 
Group  LS Mean  P-valuea  LS Mean  P-valuea 
1  91.8    72.5   
Heart Weight  2  92.5  0.4521  80.5  0.0293 
(g)  3  93.0  0.3506  73.3  0.4542 
4  90.0  0.0892b  60.0  <0.0001 
a P-value for the difference between each treatment group and group 1 within sex.  
b P-values in bold-face indicate statistical significance  ( = 0.10). 
 
Table 10b.  LS Means and P-values from Treatment Comparisons for Variables with a 
Significant  Treatment Effect:  Data from a Canine Safety Study 
Variable  Treatment Group  LS Mean  P-valuea 
1  344.8   
2  346.1  0.7149 
Liver Weight (g) 
3  364.5  0.0021 b 
4  373.0  < 0.0001 
a P-value for the difference between each treatment group and group 1.  
b P-values in bold-face indicate statistical significance  ( = 0.10). 
 
   
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Table 11.  Descriptive Statistics for Raw Data 
Number 
Treatment  Arithmetic  Standard  Minimum  Maximum 
Variable  Sex  of 
Group  Mean  Deviation  Value  Value 
Subjects 
1  4  47.6  19.2  22.3  68.9 
2  4  49.0  25.8  27.6  80.3 
Females 
3  4  78.4  42.9  29.3  132.1 
Kidney 
4  4  40.4  20.1  13.1  55.9 
Weight 
1  4  80.9  34.6  29.6  104.7 
(g) 
2  4  62.8  29.1  24.2  89.1 
Males 
3  4  57.1  42.3  22.2  117.7 
4  4  74.4  40.3  33.01  122.2 
 
2.  Continuous  variables measured repeatedly 
 
Several measurements of the same variable may be taken repeatedly on the same 
experimental unit (animal, pen, or herd) over time.  For repeatedly measured 
variables, ANOVA or ANCOVA should also be used, using a statistical model 
that is consistent with the experimental design.  If the subjects are stratified by 
sex, then the statistical model should evaluate treatment, sex, and time as fixed 
effects and the two-way and three-way interactions.  Repeated measurements 
from a single subject tend to be correlated across time and the correlation 
structure can be incorporated into the repeated measures analysis.  CVM 
recommends investigating  the less complicated covariance structures and 
choosing the least complicated structure that is appropriate for the data.  If there 
are a large number of variables to analyze, a priori the variables may be divided 
into meaningful  groups and the most clinically  relevant variable selected to 
determine the covariance matrix structure for all the variables in the same group.  
The covariance matrix should be selected from the candidates investigated  using 
pre-specified criteria, such as the Akaike Information Criterion (AIC) score, 
corrected AIC (AICC) or Bayesian Information Criterion (BIC) as appropriate.4 
 
Criteria for evaluating analysis results and recommended comparisons are 
detailed below.  All fixed model effects are tested at =0.10 except the three-way 
treatment-by-sex-by-time  interaction,  which is tested at =0.05. Examples of 
tables for presenting results and summary statistics are also provided. 
 
a.  Evaluate the treatment-by-sex-by-time  interaction ( =0.05).  If significant, 
simply  provide summary statistics for each treatment group at each time point 
within each sex.  If not significant,  proceed to step b. 
 
b.  Evaluate the treatment-by-sex and the treatment-by-time interactions ( =0.10).  If 
the treatment-by-sex interaction is significant,  perform treatment mean 
                                              
4R.C. Littell, G.A. Milliken, W.W. Stroup, R.D. Wolfinger and O. Schabenberger.  SAS for Mixed Models, Second 
Edition (Cary, NC: SAS Institute, Inc., 2006), 183-184. 
15  Contains Nonbinding Recommendations 
comparisons for each sex.  If the treatment-by-time interaction is significant, 
perform treatment mean comparisons for each time point.  Mean comparison 
results may be presented similar to Table 10a.  If neither interaction is significant, 
proceed to step c. 
 
c.  Evaluate the main effect of dose ( =0.10).  If significant,  perform treatment mean 
comparisons.  Results may be presented as in Table 10b.  If not significant, 
conclude there is not enough evidence to reject the hypothesis of no difference in 
treatment group means. 
 
d.  Summarize the results of the statistical comparisons.  Summaries of the statistical 
decision process and the outcomes of the statistical evaluations should be 
presented in tabular form.  A table similar to Table 9, adding columns for the 
treatment-by-time and treatment-by-sex-by-time  interactions,  should be provided.  
Tables for summarizing  mean comparisons are described for each of steps a, b, 
and c above with examples of presenting the results shown in Tables 10a and 10b. 
 
e.  In addition  to the model-based summaries described above, you should also 
summarize data using descriptive statistics.  Tables of descriptive statistics by 
treatment, similar  to those described for continuous variables measured once, 
should be provided.  For variables measured repeatedly, treatment summaries by 
time and by sex and time should be provided in addition  to those described for 
continuous  variables measured once.  Table 11 can be modified  for all descriptive 
statistics tables. 
 
f.  Provide profile plots.  For continuous  variables measured repeatedly, profile plots 
display changes across time for each animal.  These plots aid in exploring 
response differences across time between sexes and among treatments as well as 
values outside the normal range. For a single variable, all dose groups should be 
displayed on one page in ascending or descending dose order with all animals 
from a given dose group depicted in one plot.  The same scale should be used on 
all axes (vertical and horizontal)  for all plots for a given variable. Line segments 
should connect the values between observations for each animal, and different 
line types should be used for each sex.  Horizontal  reference lines signifying  the 
upper and lower boundaries for the normal range of the variable should be 
included.  An example of a profile plot is provided in Figure 1.  It may also be 
appropriate to generate other graphical displays that explore relevant trends in the 
data. 
 
   
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Figure 1:  Hemoglobin  (mg/dL) over time by subject in each treatment group. 
 
 
 
   
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D.  Categorical Variables 
 
Clinical  observations are examples of categorical data collected in TAS studies.  Clinical 
variables often rate the severity of a clinical  condition  such as diarrhea or vomiting  and 
are considered ordered categorical variables.  Other categorical data may be unordered or 
nominal  in nature, e.g., presence/absence or normal/abnormal.   The data may be collected 
once or repeatedly on the same subject throughout  the study. 
 
Dichotomization  of categories, analysis methods, and presentation of frequency tables are 
discussed in general for both singly  and repeatedly measured variables.  Evaluation 
criteria are discussed separately for singly  and repeatedly measured variables. 
 
1.  Dichotomization  of categories 
 
For analysis, CVM recommends that the ordered categorical data be dichotomized 
using some clinically  meaningful  way.  For example, categories could be 
dichotomized  into normal versus abnormal or normal/mild  response versus 
moderate/severe response. 
 
2.  Analysis methods 
 
Because of the difficulties  in analyzing  categorical data when the sample size is 
small, CVM recommends that categorical data be analyzed across sex.  Several 
methods for analysis of categorical variables are available and appropriate but no 
single analysis approach is recommended.  Many chi-squared based methods such 
as the Cochran-Mantel-Haenszel test and generalized linear mixed models 
(GLMM) methods are available.  CVM recommends statistical analyses only 
when there are sufficient numbers of observations to make statistical comparisons 
meaningful. 
 
For categorical variables measured once, the criteria for evaluating the treatment 
effect and pair-wise comparisons to the control are the same as described for 
continuous  variables measured once. 
 
Categorical variables measured repeatedly may be analyzed using GLMM 
methods.  For example, if the sponsor chooses to use these methods on a 
dichotomous  variable, the statistical model should assume a binomial  distribution, 
include treatment, time, and the treatment-by-time interaction as fixed effects, and 
include random effects as appropriate for the experimental design.  The criteria 
for evaluating the fixed effects and pair-wise comparisons to the control are the 
same as described for continuous  variables repeatedly measured excluding 
interactions with sex.   Dichotomized  data could be plotted over time with 
frequency of the predominant  response on the y-axis. 
 
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3.  Presentation of frequency tables 
 
Frequency or percent frequency tables should be prepared for the raw categorical 
data as well as the dichotomized  data.  The raw data and dichotomized  data 
should be summarized for each treatment by sex and combined over sex.  
Repeatedly measured variables should be summarized similarly  at each time 
point. 
 
E.  Variables Measured but not Statistically Evaluated 
 
There may be additional  information  on other variables for which statistical analysis is 
not necessary.  These variables may be summarized using the examples described 
previously in Section II of this document. 
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