Guidance for Industry  
 
 
Clinical Studies Section of Labeling 
for Human Prescription Drug and  
Biological Products  Content and 
Format
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
U.S. Department of Health and Human Services 
Food and Drug Administration 
Center for Drug Evaluation and Research (CDER) 
Center for Biologics Evaluation and Research (CBER) 
January 2006 
Labeling
 
 
  Contains Nonbinding Recommendations 
 
Guidance for Industry  
 
Clinical Studies Section of Labeling 
for Human Prescription Drug and  
Biological Products  Content and 
Format
 
 
Copies of this guidance are available from: 
 
Office of Training and Communications 
Division of Drug Information, HFD-240 
Center for Drug Evaluation and Research 
Food and Drug Administration 
 5600 Fishers Lane, Rockville, MD  20857  
(Tel) 301-827-4573 
(Internet)  http://www.fda.gov/cder/guidance/index.htm. 
 
or 
 
 Office of Communication, Training, and  
Manufacturers Assistance, HFM-40  
Center for Biologics Evaluation and Research 
 Food and Drug Administration 
1401 Rockville Pike, Rockville, MD 20852-1448  
(Tel) 800-835-4709 or 301-827-1800. 
(Internet)  http://www.fda.gov/cber/guidelines.htm.   
 
 
 
 
U.S. Department of Health and Human Services 
Food and Drug Administration 
Center for Drug Evaluation and Research (CDER) 
Center for Biologics Evaluation and Research (CBER) 
January 2006 
Labeling
 
  Contains Nonbinding Recommendations 
TABLE OF CONTENTS 
 
 
 
I.  INTRODUCTION.................................................................................................................1 
II.  IDENTIFYING STUDIES FOR INCLUSION IN THE CLINICAL STUDIES 
SECTION.......................................................................................................................................2 
A.  Studies To Include in the Clinical Studies Section......................................................................2 
B.  Studies Not To Include in the Clinical Studies Section...............................................................3 
III.  DESCRIBING STUDIES IN THE CLINICAL STUDIES SECTION............................3 
A.  General Principles..........................................................................................................................3 
B.  Describing the Study Design.........................................................................................................6 
C.  Summarizing Study Findings........................................................................................................7 
D.  Presenting Data for Different Types of Outcomes......................................................................9 
E.  Implied Claims and Advertising and Promotional Considerations........................................10 
F.  Updating the Clinical Studies Section........................................................................................11 
APPENDIX..................................................................................................................................12 
INTRODUCTION.......................................................................................................................12 
GRAPHS......................................................................................................................................12 
TABLES.......................................................................................................................................22 
 
 
 
 
 
 
 
  Contains Nonbinding Recommendations 
 
Guidance for Industry1 
 
Clinical Studies Section of Labeling for Prescription Drug  
and Biological Products  Content and Format 
 
 
 
This guidance represents the Food and Drug Administration's (FDA's) current thinking on this topic.  It 
does not create or confer any rights for or on any person and does not operate to bind FDA or the public.  
You can use an alternative approach if the approach satisfies the requirements of the applicable statutes 
and regulations.  If you want to discuss an alternative approach, contact the FDA staff responsible for 
implementing this guidance.  If you cannot identify the appropriate FDA staff, call the appropriate 
number listed on the title page of this guidance.  
 
 
 
I.  INTRODUCTION 
 
This guidance is intended to assist applicants in deciding (1) what studies should be included in 
the CLINICAL STUDIES section of prescription drug2 labeling, (2) how to describe individual 
studies, and (3) how to present study data, including presentation of data in graphs and tables.  
This guidance is intended to make the CLINICAL STUDIES section of labeling, as described in 
the final rule amending the requirements for the content and format of labeling for human 
prescription drug and biological products (21 CFR 201.56 and 201.57),3 more useful, and to 
promote consistency in the content and format of the section across drug product classes and 
within drug classes and indications.  This guidance also calls attention to the advertising and 
promotional implications of data and statements contained in the CLINICAL STUDIES section. 
 
The principal objective of labeling is to provide the information that is most useful to prescribers 
in treating their patients.  In some cases, making the information in the CLINICAL STUDIES 
section of labeling more useful to prescribers could warrant significant departures from past 
labeling practices.   
 
FDA's guidance documents, including this guidance, do not establish legally enforceable 
                                                 
1 This guidance has been prepared by the Medical Policy Coordinating Committees in the Center for Drug 
Evaluation and Research (CDER) and the Center for Biologics Evaluation and Research (CBER) at the Food and 
Drug Administration.  
2 This guidance applies to drug products, including biological drug products.  For purposes of this guidance, drug or 
drug product will be used to refer to human prescription drug and biological products that are regulated as drugs. 
 
3 See the final rule Requirements on Content and Format of Labeling for Human Prescription Drug and Biological 
Products published in the Federal Register in January 2006. 
 
 
  Contains Nonbinding Recommendations 
responsibilities.  Instead, guidances describe the Agency's 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.  IDENTIFYING STUDIES FOR INCLUSION IN THE CLINICAL STUDIES 
SECTION 
 
The CLINICAL STUDIES section of labeling must discuss those clinical studies that facilitate 
an understanding of how to use the drug safely and effectively (21 CFR 201.57(c)(15)).  This is 
usually accomplished by providing concise, accurate summaries of information from studies 
concerning a drugs effectiveness (and sometimes safety) that practitioners consider important to 
clinical decision making.  Generally, this should include information from the adequate and 
well-controlled studies that demonstrate the effectiveness of the drug for its approved indication. 
 This section of the labeling is not intended to describe all available effectiveness data.  
Additional studies that reach the same conclusion should be omitted or described briefly without 
detail. If there are multiple studies that address the same effectiveness issue, the subset selected 
for presentation should ordinarily reflect the overall conclusions derived from the database as a 
whole (e.g., not suggest a larger treatment effect than the database as a whole).   
 
A.  Studies To Include in the Clinical Studies Section 
 
The following are the types of adequate and well-controlled studies4 that should usually 
be included in the CLINICAL STUDIES section. 
 
1.  Clinical studies that provide primary support for effectiveness 
 
2.  Clinical studies that provide other important information about a drugs  
effectiveness not furnished by the studies that provide primary support for 
effectiveness, such as: 
 
  Studies that suggest differential effects in population subsets (e.g., women vs. 
men, presence or absence of concomitant illness or medications) 
  Studies that suggest lack of effectiveness in a clinical situation or lack of 
effect on a particular endpoint where the drug might have been expected to 
work 
  Studies that provide information relevant to dose selection or adjustment (e.g., 
dose-response studies or studies in nonresponders to a particular dose) 
  Studies that provide information about the nature and size of the treatment 
effect, particularly where the effect is small 
 
3.  Clinical studies that prospectively evaluate an important safety endpoint 
                                                 
4 See 21 CFR 314.126. 
 
  2 Contains Nonbinding Recommendations 
 
B.  Studies Not To Include in the Clinical Studies Section 
 
The following are the types of studies that should usually not be included in the 
CLINICAL STUDIES section, unless they also meet one of the factors in II.A (above).  
If an exception is made, the limitations of the study and the reasons for inclusion should 
be stated.   
 
1.  Clinical studies with results that imply effectiveness for an unapproved 
indication, use, or dosing regimen  
 
2.  Active control clinical studies that imply comparative effectiveness or safety 
claims not supported by substantial evidence  
 
3.  Studies that are not adequate and well-controlled within the meaning of  
21 CFR 314.126.   
 
 
III.  DESCRIBING STUDIES IN THE CLINICAL STUDIES SECTION 
 
A.  General Principles 
 
1.  Focus on Effectiveness Data  
 
The primary objective of the CLINICAL STUDIES section is to summarize (1) the 
evidence supporting effectiveness in the subjects who were studied, (2) the critical design 
aspects of the studies, including the populations studied and endpoints measured, and (3) 
the important limitations of the available evidence.  Ordinarily, safety data are described 
in the ADVERSE REACTIONS section.  However, in some cases it may be appropriate 
to present important information about safety in the CLINICAL STUDIES section (e.g., 
if the safety data are best understood when presented with a detailed study description or 
in the context of effectiveness results).  The section should also include safety data from 
controlled studies specifically designed to evaluate a safety endpoint.  If safety data are 
presented in the CLINICAL STUDIES section, they must be cross-referenced in the 
ADVERSE REACTIONS section and other sections, as appropriate (21 CFR 
201.57(c)(15)(ii)).   
 
2.  Amount of Detail 
 
In general, the amount of detail needed to provide a useful description of a study and its 
results will depend on the indication, the trial design, the understanding of the drug or 
drug class, and the extent to which the information adds to an understanding of the 
clinical effects of the drug and how the drug should be used.  The amount of detail 
appropriate for a given study or dataset is inevitably a matter of judgment, but some 
general principles can be described. 
 
  3 Contains Nonbinding Recommendations 
Ordinarily, applicants should include more detail when: 
 
  The study responses measured are of critical health importance.  In most cases, such 
responses would be direct measures of a meaningful clinical outcome (e.g., mortality, 
stroke, acute myocardial infarction rates, fracture rates, symptom alleviation, or 
functional improvement), but could also include effects on important surrogate 
endpoints (e.g., cholesterol or hemoglobin A1c). 
 
  The study results demonstrate that a new agent offers a clear advantage over existing 
therapy (see section III.A.4 for a discussion of comparative claims). 
 
  The study results represent a significant advance in the treatment of a disease or 
condition, or provide important information about a drugs activities relative to its 
therapeutic class.   
 
  The study enrolled a very specific population or used a very specific concomitant 
regimen, and the results may not be applicable to other populations.   
 
  The study results are not what would be expected for that drug class and indication 
for example, when the study results demonstrate a particularly marginal response or a 
response for which the clinical meaning or implications are unclear.  
 
  The study uses an unfamiliar endpoint (e.g., a novel surrogate endpoint), or there are 
important limitations and uncertainties associated with an endpoint. 
 
Applicants should include less detail when:   
 
  The new drug appears to have effects that are typical of its class. 
 
  The magnitude of the effect on clinical endpoints measured in the study is not readily 
translatable into effects in clinical practice.  For example, exercise testing in a study 
of heart failure can demonstrate effectiveness, but does not translate to a quantifiable 
clinical outcome.  Similarly, changes in HAM-D scores can be used to demonstrate 
effectiveness of an antidepressant, but the results for a given study are population- 
and probably site-specific, and thus, do not necessarily translate to a numerically 
similar outcome in clinical practice. 
 
In these cases, it could be useful to describe the study in general terms (e.g., population, 
duration, endpoints measured, and qualitative outcome) without providing detailed 
results. 
 
3.  Endpoints  
 
The CLINICAL STUDIES section should present those endpoints that establish the 
effectiveness of the drug or show the limitations of effectiveness.  This includes 
  4 Contains Nonbinding Recommendations 
endpoints the Agency has accepted as evidence of effectiveness, or closely related 
endpoints that may be more easily understood.  When it would be informative, the 
CLINICAL STUDIES section can also discuss other endpoints shown to be affected by 
the drug and endpoints that might have been expected to be influenced by the drug, but 
were not. 
 
  Composite Endpoints:5  In general, the results for all components of a composite 
endpoint should be presented.  Presentation of all components reveals which 
components are driving the result and which components may be unaffected, or even 
adversely affected, by treatment with the drug.  When there is a range of effects on 
the components of a composite endpoint, selectively presenting only a single 
component of the composite endpoint, or presenting only the change in the composite 
endpoint, can be misleading.  In most cases, discussion of a component of a 
composite endpoint should be only descriptive (i.e., not be presented with statistical 
analyses) unless the component has been assessed as a separate endpoint with a 
prospectively defined hypothesis and analysis plan.   
 
  Primary and Secondary Endpoints:  The terms primary endpoint and secondary 
endpoint are used so variably that they are rarely helpful.  The appropriate inquiry is 
whether there is a well-documented, statistically and clinically meaningful effect on a 
prospectively defined endpoint, not whether the endpoint was identified as primary or 
secondary. 
 
  Closely Related Endpoints:  If two or more endpoints are closely related and convey 
essentially the same information, only one should generally be presented. 
 
4.  Comparative Data 
 
If the effectiveness of a drug can be determined by comparison to placebo, data 
comparing the effects of the drug to an active comparator should generally not be 
included in labeling unless the data are adequate to support an explicit comparative claim 
(either a superiority or similar effectiveness claim).  For example, when describing a 
clinical trial with three treatment arms (study drug, active control, and placebo) in which 
the comparison of study drug to placebo yields important effectiveness information and 
the active control was present only to confirm assay sensitivity, the identity of the active 
control and the results from that arm should be omitted if those data are not adequate to 
support a comparative claim and are not otherwise important to a clinicians 
understanding of the drugs effect.   
 
If effectiveness can be determined only by comparison to an active control (superiority or 
non-inferiority trial) and the identity of the active comparator is important to a clinicians 
                                                 
5 Note that composite endpoint refers to combined morbidity and mortality endpoints that could potentially be 
evaluated separately, not to the separate components of standard evaluative scales (e.g., HAM-D for depression, 
BPRS for schizophrenia, nasal symptoms score). 
 
  5 Contains Nonbinding Recommendations 
understanding of the drugs effects, the active control data and identity of the comparator 
should be included in labeling.  In such cases, the labeling should make clear that no 
comparative claim has been established (if it has not been) and should disclose any 
limitations of the comparative data (e.g., if the comparator was administered in a 
suboptimal or unapproved regimen).  
 
An explicit claim of superior or similar effectiveness must be supported by substantial 
evidence (21 CFR 201.56(a)(3)).  For superiority claims, such evidence would include 
adequate and well-controlled trials designed to establish superiority of one treatment over 
another.  For claims of similar effectiveness, such evidence would include adequate and 
well-controlled trials designed to demonstrate that one treatment is not inferior to another 
and that the difference between the two is not clinically significant.  Thus, the non-
inferiority margin used would have to be smaller than the margin needed to merely 
establish effectiveness.  For example, a non-inferiority trial designed to show that a new 
drug has at least 50 percent of the effect of the active control can provide adequate 
evidence of effectiveness, but a 50 percent non-inferiority margin would ordinarily be too 
large to support a claim of similar effectiveness.  For each type of trial, the active control 
should be used at an appropriate dose and regimen, generally the highest recommended 
dose, and in an appropriate patient population. 6 
 
B.  Describing the Study Design 
 
The following elements are recommended when describing the study design. 
 
1.  Major Design Characteristics 
 
The major design characteristics should be identified, including level of blinding (e.g., 
double-blinded, partially blinded, open-label), type of controls (e.g., placebo, active, 
historical), duration of the study, method of allocation to treatment groups (e.g., 
randomization), and use of a run-in period to identify potential responders or eliminate 
placebo responders from subsequent phases of the study.  Often, many of these factors 
can be summarized in a phrase such as randomized, double-blind, placebo-controlled 
study. 
 
2.  Treatment Arms 
 
The dose, regimens, and any titration procedure should be identified for each trial arm. 
  
3.  Concomitant Therapy  
 
Information about concomitant therapies should be included to the extent it helps to 
                                                 
6 International Conference on Harmonization (ICH) guidance E10 (Choice of Control Group and Related Issues in 
Clinical Trials) considers fairness of comparisons intended to show superiority or equivalence of one treatment to 
another.  The guidance discusses how to design a trial that does not inappropriately favor one treatment over another 
(see section on Fairness of Comparisons in ICH E10). 
 
  6 Contains Nonbinding Recommendations 
understand the use of the study drug or its effects.   
 
4.  Study Population 
 
The description of the study population should identify those characteristics that are 
important for understanding how to interpret and apply the study results.  The description 
thus should identify important inclusion and exclusion criteria, the demographic 
characteristics of the studied population, baseline values of any clinically relevant 
variables important for understanding the treatment effect, and other characteristics of the 
population that have important implications for the extent to which results can be 
generalized.  For example, the description should discuss enrollment factors that exclude 
subjects prone to adverse effects, the age distribution of the study population, a baseline 
value that results in a study population that is more or less sick than usual, or a study 
population enriched by a study design that eliminates nonresponders.  
 
5.  Critical Endpoints 
 
Endpoints critical to establishing effectiveness should be identified, and those that are not 
commonly understood should be defined. 
 
C.  Summarizing Study Findings 
 
When including a detailed summary of study findings (see section III.A.2 for a discussion 
of when more detail is important), the following elements should be addressed to the 
extent they contribute to practitioners understanding of drug effectiveness. 
 
1.  Disposition of Subjects 
 
It is generally recommended that the discussion of disposition of subjects include the 
following: 
 
  The number of subjects enrolled 
 
  The number of subjects completing the study 
 
  The number of subjects discontinuing the study and the reasons for discontinuation 
 
  For a study with a run-in period or other distinct phases, the number of subjects 
entering each phase and the number of subjects not progressing to the next phase  
 
2.  Treatment Effect7 
 
                                                 
7 Treatment effect means the effect that can be attributed to the drug.  It is typically derived from a comparison of 
two prospectively identified treatment arms.  Examples of such comparisons include differences in proportions of 
patients achieving some treatment goal, differences in mean change from baseline, or hazard ratios. 
  7 Contains Nonbinding Recommendations 
It is recommended that the summary of findings describe the clinical outcome of the 
treatment relative to the comparator (e.g., placebo or active).   
 
  Absolute vs. Relative Difference:  When presenting differences between study 
group and comparator, it is important to present the absolute difference between 
treatment groups for the endpoint measured, as well as the relative difference (e.g., 
relative risk reduction or hazard ratio).  For example, if mortality is 6 percent in one 
study arm and 8 percent in the other, the absolute difference (2 percent) should be 
presented along with the 25 percent relative risk reduction.  
 
  Group Results and Individual Subject Data:  In most cases, the treatment effect is 
presented as a mean or median result accompanied by a measure of uncertainty or 
distribution of results for the treated groups.  However, providing individual subject 
data for all treatment groups can be a useful alternative for describing the clinical 
effect of a drug.  This can be done by including a graphical presentation of the 
distribution or cumulative distribution of responses among individual subjects (see 
Appendix for examples of graphical methods for presenting individual subject data). 
Individual data can also be presented as categorical outcomes (e.g., the proportion of 
patients reaching a prospectively defined goal, such as systolic blood pressure of 120 
mmHg). 
 
  Combined Data:  In certain situations, analyses of data combined from multiple 
effectiveness studies can be useful for estimating the treatment effect.  These analyses 
should be included only when they are scientifically appropriate and better 
characterize the treatment effect.  Meta-analytic graphs (see Appendix) can be useful 
for displaying confidence intervals from several studies. 
 
  Uncertainty of Treatment Effect:  A confidence interval and a p-value provide 
complementary information, and both should usually be provided when describing 
uncertainty of the treatment effect.  A confidence interval provides a better numerical 
description of the uncertainty of the treatment effect and provides some information 
about its size.  A p-value better conveys the strength of the finding (i.e., how likely it 
is that the observed treatment effect is a chance finding).  However, it is generally 
better not to use a p-value alone.  
 
3.  Describing Results Within Treatment Groups  
 
In controlled trials, the change from baseline in a treatment group is usually not by itself 
informative.  The comparison of the change from baseline between treatment groups is 
critical for understanding the treatment effect.  Therefore, results for both the study drug 
and comparator should almost always be presented because the magnitude of the 
treatment effect is conveyed by the comparison.  Presentation of results for both study 
drug and comparator is especially important for studies with large effects in the placebo 
group, where presentation of results uncorrected for the placebo group response can be 
highly misleading.  When results from active control arms are discussed, a comparative 
claim should not be implied where one is not supported.  The relevant statistical 
  8 Contains Nonbinding Recommendations 
comparisons are those comparing the groups, not the comparison of the treated and 
baseline value within a group. 
 
For continuous data, the presentation of results within a treatment group should include, 
where appropriate, information about the variability of individual subject responses 
within the treatment group.  This variability can be described with standard deviations 
and illustrated with box plots (see Appendix for examples of graphical methods for 
presenting results within treatment groups). 
 
4.  Demographic and Other Subgroups 
 
The CLINICAL STUDIES section should include a summary statement about the results 
of required explorations of treatment effects in age, gender, and racial subgroups (21 
CFR 314.50(d)(5)(v)).  The summary statement should report the findings of analyses 
that had a reasonable ability to detect subgroup differences and should note when 
analyses were not useful because of inadequate sample size.  The following are examples 
of appropriate summary statements. 
 
  The database was not large enough to assess whether there were differences in effects 
in age, gender, or race subgroups. 
 
  Examination of age and gender subgroups did not identify differences in response to 
(study drug) among these subgroups.  There were too few African-American subjects 
to adequately assess differences in effects in that population.   
 
  Examination of age and gender subgroups suggested a larger treatment effect in 
women (possibly resulting from the larger mg/kg dose in women), but no age-related 
differences.  There were too few African-American subjects to adequately assess 
differences in effects in that population. 
 
Compelling results from analyses of other subgroups of established interest should also 
be presented, with a caution statement, where appropriate, about the inherent risks of 
unplanned subgroup analyses. 
 
D.  Presenting Data for Different Types of Outcomes 
 
Data on outcomes of treatment should be presented only if the outcome is of clinical 
significance.  
 
1.  Categorical Outcomes (e.g., success or failure) 
 
For categorical outcomes, the number (or percentage) of outcomes for randomized 
subjects should be shown.  For example, the total sample size for the treatment group, the 
number of successes, the number of failures, and the number of unknown status should be 
provided.  Where informative, those subjects whose outcome status is unknown can be 
further differentiated by including the number who dropped out due to adverse events, 
  9 Contains Nonbinding Recommendations 
the number who were lost to follow-up, or any other pertinent distinction.  If only 
percentages are reported, the denominator should be included. 
 
2.  Continuous Variables 
 
For continuous variables, measures of central tendency (e.g., mean, median), 
accompanied by a measure of distribution, are the usual methods for presenting data.  
When means or medians are used, the number of subjects remaining in the study at each 
time point should be provided.  Because means or medians, even when accompanied by 
descriptions of variability, may not adequately convey the variability of responses, it 
might be useful to display individual responses (e.g., by graphical representation of the 
cumulative distribution of responses  see Appendix).  It is important to include the 
baseline value when reporting any change (either numerical or percent change) from that 
baseline. 
 
3.  Time-to-Event Endpoints  
 
When time-to-event endpoints (e.g., mortality) are used, median or mean survival alone 
is not usually an adequate descriptor.  Survival curves (or event-free survival curves) and 
hazard ratios are often effective ways to display such data.  Data can also be summarized 
at specific times (e.g., prevalence at 3, 6, 9, 12 months) or at specific event frequency 
(e.g., time to 25 percent, 50 percent, and 75 percent prevalence of events).  The number 
of subjects evaluated at a given interval or frequency should be specified.  Note again the 
need to convey both absolute and relative difference (see III.C.2). 
 
4.  Graphs or Tables 
 
Graphs and/or tables are often more effective than text alone in communicating study 
results, and one or the other should be used when presenting study results in the 
CLINICAL STUDIES section.  See the Appendix for guidance on the use of graphs and 
tables in the CLINICAL STUDIES section of labeling. 
 
E.  Implied Claims and Advertising and Promotional Considerations 
 
The CLINICAL STUDIES section must not suggest or imply indications, uses or dosing 
regimens not stated in the INDICATIONS AND USAGE or DOSAGE AND 
ADMINISTRATION section (21 CFR 201.57(c)(15)(i)).  Words or phrases that lack a 
commonly understood meaning (e.g., imprecise quantitative terms), are not easily 
defined, are vague, misleading, or promotional in tone should be avoided.  Examples 
include large or small (instead, use actual size or amount), well-designed (instead, 
provide specifics about the study design), extensively studied (instead, provide specifics 
about the database), rapid (instead, specify change/unit time), trend (instead, provide 
specifics about the outcome), potent (instead, give the size of the effect), pivotal study 
(instead, describe as major effectiveness study), and highly significant (instead, provide 
the confidence interval).   
 
  10 Contains Nonbinding Recommendations 
Advertising and promotion make frequent use of statements or data appearing in the 
CLINICAL STUDIES section.  Sponsors are reminded that any claim of effectiveness 
made in prescription drug promotion, including comparative effectiveness, must be 
supported by substantial evidence (21 CFR 201.56(a)(3)) or substantial clinical 
experience (see e.g., 21 CFR 202.1(e)(6)(i)). 
 
F.  Updating the Clinical Studies Section 
 
The CLINICAL STUDIES section must be updated when new information becomes 
available (21 CFR 201.56(a)(2)) that causes the labeling to become inaccurate, false or 
misleading.  Such outdated information must be promptly revised or deleted.   
  11 Contains Nonbinding Recommendations 
 
APPENDIX 
 
Presenting Study Results in Tables and Graphs 
 
INTRODUCTION 
   
When clinical data are to be presented in some detail in the CLINICAL STUDIES section, tables 
and graphs are often better than text alone because they can convey the desired information more 
effectively.  The following general principles should be applied to the use of tables and graphs: 
 
  Depict study results clearly, fairly, and accurately. 
 
  Do not repeat in accompanying text, information that would be clear from 
viewing the table or graph, although the text can point out the most important data 
presented.  Often the statement, Results are found in Table X, is sufficient. 
 
  Small tables can be embedded in the text.  Do not place larger tables and graphs 
next to any explanatory text. 
 
  Use clear titles and clearly labeled axes to limit the need for any explanatory text. 
 
GRAPHS 
 
A.  Common Uses of Graphs 
 
  To present a large amount of data, such as individual 
subject data points (e.g., distribution of responses) 
 
  To show effects of treatment on major events or survival 
over time (Kaplan-Meier curves) 
 
  To illustrate changes over time 
 
  To illustrate differences in magnitude of response, 
particularly where more than two treatment groups are 
being compared 
 
  To convey dose-response information 
  12 Contains Nonbinding Recommendations 
 
B.  Graphs Most Commonly Used in the Description of Clinical Trial 
Effectiveness Data 
 
Histogram 
Distribution of Responses
by Percent Change from Baseline and Treatment Group
GGrroouupp  AA
y
40
c
n
e 30
u
q 20
e
r 10
F
0
GGrroouupp  BB
y
40
c
n
e 30
u
q 20
e
r 10
F
0
 -60  -50  -40  -30  -20  -10   0.   10   20   30
% Change from Baseline
 
   
 
A histogram illustrates results of a trial by presenting the number or percentage of 
subjects (y-axis) exhibiting a given response (x-axis) over the whole response range.  
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
  13 Contains Nonbinding Recommendations 
 
Bar Graph 
 
Distribution of Responses by Response Category
60
Drug A
s50
t
c
e Drug B
j
b
40
u
S
 
f
o
 30
e
g
a
nt20
e
c
r
e
P10
0
0 1 2 3 4
Response Category
 
 
   
In a bar graph, the length of the bar (the y-axis) represents the group response for the 
outcome variable or the percentage or frequency of subjects exhibiting a categorical 
response.  Similar graphs can be useful for comparing effects among subgroups.  In this 
case, the response would appear on the y-axis, with various subgroups on the x-axis.  In 
most cases, it is helpful to include error bars.  A bar graph should not be used to illustrate 
just a few numbers that could be summarized better in a table.  Graphs in 3-D should be 
avoided because the values for the bars are difficult to read.  Stippling or other small 
patterns in bars should also be avoided because the bars can be difficult to differentiate 
after reduction or reproduction. 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
  14 Contains Nonbinding Recommendations 
 
 
 
Line Graph 
 
Percent Change from Baseline by Week
and Treatment Group for Study 1
10
0
e
n -10
i
l
e
s
a -20
B
 
m
-30
o
r
f
 
e -40
g
n
a
h -50
C
 
%
-60
Treatment:
Placebo (n=75)
-70 Low Dose  (n=73)
High Dose  (n=76)
0 1 2 4 6
Week
 
 
   
A line graph most often illustrates responses (y-axis) over time (x-axis) where each line 
represents the data for a defined group of subjects (e.g., a treatment group, a subgroup).  
It is helpful in some cases to include error bars and number of subjects remaining on 
study treatment at each time point.  Similar graphs can be used to show dose response 
with response on the y-axis and dose on the x-axis.  
 
 
 
 
 
 
 
 
 
 
 
  15 Contains Nonbinding Recommendations 
 
 
Survival Curve 
 
Kaplan-Meier Survival Curve of Time to Event for Study 1
1
0.9
0.8
g
n 0.7
i
v
i
v
r 0.6
u
S
n  0.5
o
i
t
or 0.4
p
o
r 0.3
P Treatment:
Drug
0.2 Placebo
0.1
L.R.: p = 0.0012
0
12 24 36 48 60 72
Time to Event (Months)
 
 
 
A survival curve depicts time-to-event data for events like death or recurrence of 
disease.  Usually, Kaplan-Meier estimates of the proportion of patients surviving at any 
time as a fraction of people still in the study at that point are plotted, but some survival 
plots show the raw cumulative incidence rates as a fraction of patients randomized over 
time.  
 
 
 
 
 
 
 
 
 
 
 
 
 
 
  16 Contains Nonbinding Recommendations 
 
 
 
 
Scatter Plot 
 
Endpoint Response Versus Baseline Response for Study 1
350
300
e
s
n
o
p
s
e
R
  250
t
n
i
o
p
d
n
E
200
150
200 250 300 350
Baseline
 
   
A scatter plot shows the relationship between two (usually continuous) variables for 
individual patients (e.g., response (y-axis) related to a baseline value (x-axis)). 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
  17 Contains Nonbinding Recommendations 
 
 
 
Boxplot 
 
Boxplots of Response at Endpoint by Dose for Study 1
0
-20
-40
e
n -60
eli
s -80
a
B
-100
m 
o -120
r
f
ge  -140
n
a -160
h
C
-180
-200
N=  38  N=  45  N=  38  N=  44  N=  42 
5 10 20 40 80
Dose (mg)
 
 
A boxplot illustrates the distribution of data for a single group.  Several plots in a single 
graph are useful for comparing distributions.  Boxes in this example represent the range 
of values from the 25th percentile to the 75th percentile.  The median may be 
represented by a line or symbol.  The definition for the length of the whiskers (lines 
extending out from each end of the box) varies with software packages and should be 
defined with the plot (e.g., the ends represent the 10th and 90th percentile). 
 
  18 Contains Nonbinding Recommendations 
 
 
 
Cumulative Distribution Plot 
 
 Cumulative Distribution Plot of Change From Baseline for Study 1
Improvement     No Improvement
100
DRUG B
90
80
70 DRUG A
60
e
v
ti 50
a
l
u 40
m
u
30
C
20
10
0
-8 -6 -4 -2 0 +2 +4
Change from Baseline
 This graph shows the percentage of subjects (y-axis) attaining a
change from baseline less than or equal to the value on the x-
axis.  A curve that shifts to the left indicates a better response.
 
 
 
 
A cumulative distribution plot shows the percentage of subjects with a change value 
equal to or less than the value on the x-axis.  These distributions can be graphed using 
connected points, bars, or steps.  A cumulative distribution plot may need a footnote and 
additional text in the body of the label describing how to read the graph.  For example, 
the following text could accompany the graph shown above:  Approximately 50% of 
the patients in each group had a decrease of at least 2 mg/dL at endpoint. 
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Meta-Analytic Graph 
 
 Odds Ratios and 95% Confidence Intervals
For Each Study and for All Studies Combined
Favors Treatment A Favors Placebo
9 B
7 B
3 B
8 B
6 B
1 B
4 B
5 B
2 B
Overall B
0.05 0.1 0.2 0.5 1 2
Odds Ratio Plotted on Log Scale
 
A meta-analytic graph depicts summary results (usually a treatment difference or ratio) for 
several studies (or centers) on one graph.  The x-axis displays the difference or ratio with a 
reference line at zero or one.  The results are usually given with their variance or confidence 
interval.  This graph is useful for illustrating consistency or lack of consistency across studies. 
Ordering the responses by magnitude or sample size enhances the visualization of the effects.  
Similar displays can show results in demographic or other subsets (e.g., disease severity, 
background therapy, country, or region) of a population. 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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C.  Features of a Good Graph 
 
  Title:  The title should include the name of the study, the type of data, the timepoint, and 
important features of the patient population (e.g., intent-to-treat, evaluable, age range if 
relevant).  For ease of reading, the first letter of each word should be capitalized, not every 
letter in the title. 
 
  Axis Label/Title:  Label each axis and include units of measurement. 
 
  Ticks and Grids:  Label selected ticks for each axis.  A graph will appear less cluttered if 
ticks face away from the graph and if grids are eliminated. 
 
  Axis Scale:  Ensure that the scale of measurement (generally the y-axis) does not exaggerate 
the treatment effect or any other variable being measured (e.g., by interruptions).  In general, 
make the scales for like graphs consistent within the labeling. 
 
  Symbols:  Distinguish symbols by size, shape, or fill (e.g., open symbols for placebo and 
closed symbols for treatment). 
 
  Footnotes:  Use a footnote if further information would be helpful to explain the content of 
the graph (e.g., the meaning of a term used, the meaning of a symbol).  Statements directly 
interpreting the graph, however, should not be in a footnote, but in the text accompanying the 
graph. 
 
  Error Bars:  Measures of variability or uncertainty are represented in graphs by error bars or 
sometimes by shading.  Make it clear from the graph which measure of variability is used to 
define the error bars (e.g., standard deviation, standard error, percentiles).  Accompany 
treatment differences or ratios with confidence intervals. 
 
  Legend:  Ensure that the legend does not overpower the graph.  Labels directly on the graph 
are preferable to a legend. 
 
  Sample Size:  Including sample sizes for each group often helps the reader interpret the 
graph.  Sample sizes can be identified in text within the graph or in a small table just below 
the graph. 
 
  P-values:  The text or a table accompanying the graph usually will include p-values when 
describing the significance of the results, so it is generally not necessary or desirable to 
include p-values in a graph.  One exception may be the plot of Kaplan-Meier survival curves 
where a p-value may be preferred to confidence bands and may enhance interpretation of the 
graph. 
 
  Additional Graph Descriptors:  To aid in interpretation of the graph, show reference lines 
for no change or no difference.  Descriptors that denote change, such as an arrow labeled 
Improvement may be useful to the reader. 
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III.  TABLES 
 
A.  Use of Tables 
 
  To present simple, descriptive statistics such as medians, means, standard 
deviations, and sample sizes by treatment group 
 
  To present comparative statistics such as treatment differences, confidence 
intervals, and p-values 
 
  To summarize data from more than one effectiveness variable 
 
  To present exact values, if that information is desirable 
 
B.  Features of a Good Table 
 
  Title:  Include the name of the study, the type of data, the timepoint, and 
the patient population (intent-to-treat, evaluable).  For ease of reading, 
capitalize the first letter of each word, not every letter in the title. 
 
  Units:  Include the units of measurement for the data presented, either in 
the title or column headings.  When presenting percentages, it is helpful to 
include the percent sign, particularly when several numbers are included 
on one line (such as mean percentages and sample sizes).  Only include 
the number of digits after the decimal that are significant or meaningful.  
 
  Sample Size:  Include the sample size for each treatment group in the 
table.   
 
  Baseline Data:  Include baseline data whenever applicable. 
 
 
 
 
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