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 

It 

and regulations. 

This guidance represents the Food and Drug Administration's (FDA's) current thinking on this topic. 

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 

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 drug’s 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 drug’s 

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. 

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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 drug’s 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 populationand 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 

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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 clinician’s 

understanding of the drug’s 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 clinician’s 

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). 

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Contains Nonbinding Recommendations 

understanding of the drug’s 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 noninferiority 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, 

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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). 

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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. 

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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 

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Contains Nonbinding Recommendations 

B. Graphs Most Commonly Used in the Description of Clinical Trial 

Effectiveness Data 

Frequency 

Histogram 

Distribution of Responses 

by Percent Change from Baseline and Treatment Group 

Group A

40

30

20

10

0

Group B

0 

10 

20 

30 

40

Frequency

 -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. 

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Bar Graph 

0 

10 

20 

30 

40 

50 

60 

Percentage of Subjects 

Drug A 

Drug B 

Distribution of Responses by Response Category 

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. 

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Contains Nonbinding Recommendations 

Line Graph 

Percent Change from Baseline by Week 

and Treatment Group for Study 1 

-70 

-60 

-50 

-40 

-30 

-20 

-10 

0 

10 

% Change from Baseline 

Treatment: 

Placebo (n=75) 

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. 

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Survival Curve 

Kaplan-Meier Survival Curve of Time to Event for Study 1 

L.R.: p = 0.0012 0 

0.1 

0.2 

0.3 

0.4 

0.5 

0.6 

0.7 

0.8 

0.9 

1 

Proportion SurvivingTreatment: 

Drug 

Placebo 

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. 

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Contains Nonbinding Recommendations

17

Scatter Plot 

200 250 300 350

Baseline

150

200

250

300

350

Endpoint Response

Endpoint Response Versus Baseline Response for Study 1

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)). 
Contains Nonbinding Recommendations 

Boxplot 

Boxplots of Response at Endpoint by Dose for Study 1 Change from Baseline 

0 

-20 

-40 

-60 

-80 

-100 

-120 

-140 

-160 

-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). 

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Contains Nonbinding Recommendations 

Cumulative Distribution Plot 

 Cumulative Distribution Plot of Change From Baseline for Study 1 

0 

10 

20 

30 

40 

50 

60 

70 

80 

90 

100 

DRUG B 

DRUG A

Cumulative 

Improvement No Improvement

-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 xaxis. 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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Contains Nonbinding Recommendations 

Meta-Analytic Graph 

 Odds Ratios and 95% Confidence Intervals 

For Each Study and for All Studies Combined 

Favors Treatment A Favors Placebo 

B 

B 

B 

B 

B 

B 

B 

B 

B 

B 

Overall 

2 

5 

4 

1 

6 

8 

3 

7 

9 

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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Contains Nonbinding Recommendations 

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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