Adaptive Designs for 
Clinical Trials of Drugs 
and Biologics  
Guidance for Industry 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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) 
 
 
November 2019 
Biostatistics 
   
Adaptive Designs for 
Clinical Trials of Drugs 
and Biologics 
Guidance for Industry 
 
 
Additional copies are available from: 
 
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Center for Drug Evaluation and Research 
Food and Drug Administration 
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and/or 
 
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Email: ocod@fda.hhs.gov 
https://www.fda.gov/vaccines-blood-biologics/guidance-compliance-regulatory-information-biologics/biologics-guidances 
 
 
 
 
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) 
 
 
November 2019 
Biostatistics
  Contains Nonbinding Recommendations 
 
TABLE OF CONTENTS 
 
 
I.  INTRODUCTION AND SCOPE .................................................................................... 1 
II.  DESCRIPTION OF AND MOTIVATION FOR ADAPTIVE DESIGNS .................. 2 
A.  Definition ........................................................................................................................................ 2 
B.  Important Concepts ....................................................................................................................... 2 
C.  Motivation and Examples.............................................................................................................. 3 
D.  Limitations ...................................................................................................................................... 6 
E.  Choosing to Adapt ......................................................................................................................... 6 
III.  PRINCIPLES FOR ADAPTIVE DESIGNS .................................................................. 7 
A.  Controlling the Chance of Erroneous Conclusions ..................................................................... 7 
B.  Estimating Treatment Effects ....................................................................................................... 8 
C.  Trial Planning ................................................................................................................................ 8 
D.   Maintaining Trial Conduct and Integrity .................................................................................... 9 
IV.  ADAPTIVE DESIGNS BASED ON NON-COMPARATIVE DATA........................ 10 
V.  ADAPTIVE DESIGNS BASED ON COMPARATIVE DATA .................................. 10 
A.  Group Sequential Designs ........................................................................................................... 11 
B.  Adaptations to the Sample Size .................................................................................................. 13 
C.  Adaptations to the Patient Population (e.g., Adaptive Enrichment) ....................................... 14 
D.  Adaptations to Treatment Arm Selection .................................................................................. 15 
E.  Adaptations to Patient Allocation ............................................................................................... 16 
F.  Adaptations to Endpoint Selection ............................................................................................. 17 
G.  Adaptations to Multiple Design Features .................................................................................. 17 
VI.  SPECIAL CONSIDERATIONS AND TOPICS .......................................................... 18 
A.  Simulations in Adaptive Design Planning .................................................................................. 18 
B.  Bayesian Adaptive Designs.......................................................................................................... 20 
C.  Adaptations in Time-to-Event Settings ...................................................................................... 21 
D.  Adaptations Based on an Intermediate Endpoint ..................................................................... 21 
E.  Secondary Endpoints ................................................................................................................... 22 
F.  Safety Considerations .................................................................................................................. 22 
G.  Adaptive Design in Early-Phase Exploratory Trials ................................................................ 23 
H.  Unplanned Design Changes Based on Comparative Interim Results ..................................... 23 
I.  Design Changes Based on Information From a Source External to the Trial ........................ 24 
  Contains Nonbinding Recommendations 
 
VII.  MAINTAINING TRIAL INTEGRITY ........................................................................ 24 
VIII.  REGULATORY CONSIDERATIONS ........................................................................ 26 
A.  Interactions With FDA ................................................................................................................ 26 
B.  Documentation Prior to Conducting an Adaptive Trial ........................................................... 27 
C.  Evaluating and Reporting a Completed Trial ........................................................................... 29 
IX.  REFERENCES ................................................................................................................ 30 
  Contains Nonbinding Recommendations 
 
Adaptive Designs for Clinical Trials of Drugs and Biologics 
Guidance for Industry1 
 
 
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 staff responsible for this guidance as listed on the title 
page.  
 
 
 
 
I.  INTRODUCTION AND SCOPE 
 
This document provides guidance to sponsors and applicants submitting investigational new drug 
applications (INDs), new drug applications (NDAs), biologics licensing applications (BLAs), or 
supplemental applications on the appropriate use of adaptive designs for clinical trials to provide 
evidence of the effectiveness and safety of a drug or biologic.2 The guidance describes important 
principles for designing, conducting, and reporting the results from an adaptive clinical trial. The 
guidance also advises sponsors on the types of information to submit to facilitate FDA evaluation 
of clinical trials with adaptive designs, including Bayesian adaptive and complex trials that rely 
on computer simulations for their design. 
 
The primary focus of this guidance is on adaptive designs for clinical trials intended to support 
the effectiveness and safety of drugs. The concepts contained in this guidance are also useful for 
early-phase or exploratory clinical trials as well as trials conducted to satisfy post-marketing 
commitments or requirements.    
 
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. 
 
 
 
 
 
                                                 
1 This guidance has been prepared by the Office of Biostatistics in the Center for Drug Evaluation and Research and 
the Division of Biostatistics in the Center for Biologics Evaluation and Research at the Food and Drug 
Administration. 
2 The term drug as used in this guidance refers to both human drugs and biological products unless otherwise 
specified. 
  1  Contains Nonbinding Recommendations 
 
II.  DESCRIPTION OF AND MOTIVATION FOR ADAPTIVE DESIGNS 
 
A.  Definition 
 
For the purposes of this guidance, an adaptive design is defined as a clinical trial design that 
allows for prospectively planned modifications to one or more aspects of the design based on 
accumulating data from subjects in the trial.  
 
B.  Important Concepts 
 
The following are descriptions of important concepts used in this guidance: 
 
  An interim analysis3 is any examination of data obtained from subjects in a trial while that 
trial is ongoing and is not restricted to cases in which there are formal between-group 
comparisons. The observed data used in the interim analysis can include one or more types, 
such as baseline data, safety outcome data, pharmacokinetic, pharmacodynamic or other 
biomarker data, or efficacy outcome data.  
 
  A non-comparative analysis is an examination of accumulating trial data in which the 
treatment group assignments of subjects are not used in any manner in the analysis. A 
comparative analysis is an examination of accumulating trial data in which treatment groups 
are identified, either with the actual assigned treatments or with codes (e.g., labeled as A and 
B, without divulging which treatment is investigational).4 The terms unblinded analysis and 
blinded analysis are also sometimes used to make the distinction between analyses in which 
treatment assignments are and are not identified, respectively. We avoid the terms unblinded 
analysis and blinded analysis in this guidance because these terms can misleadingly conflate 
knowledge of treatment assignment with the use of treatment assignment in adaptation 
algorithms. An interim analysis can be comparative or non-comparative regardless of 
whether trial subjects, investigators, and other personnel such as the sponsor and data 
monitoring committee (DMC) have knowledge of individual treatment assignments or access 
to comparative results by treatment arm. For example, it is possible to include adaptations 
based on a non-comparative analysis even in open-label trials, but ensuring that the 
adaptations are completely unaffected by knowledge of comparative data presents additional 
challenges. The importance of limiting access to comparative interim results is discussed in 
detail in section VII. of this guidance. 
 
                                                 
3 The FDA guidance for industry E9 Statistical Principles for Clinical Trials (September 1998) defines an interim 
analysis as any analysis intended to compare treatment arms with respect to efficacy or safety... The current 
guidance uses a broader meaning for interim analysis to accommodate the wide range of analyses of accumulating 
data that can be used to determine trial adaptations. We update guidances periodically. For the most recent version 
of a guidance, check the FDA guidance web page at 
https://www.fda.gov/RegulatoryInformation/Guidances/default.htm.  
4 These definitions of the terms non-comparative analysis and comparative analysis refer to the setting of a multi-
arm clinical trial. In a single-arm clinical trial, any analysis of accumulating trial data involves identification of 
treatment assignment information and, therefore, is considered comparable to a comparative analysis for the 
purposes of this guidance.  
 
  2  Contains Nonbinding Recommendations 
 
  The term prospective, for the purposes of this guidance, means that the adaptation is planned 
and details specified before any comparative analyses of accumulating trial data are 
conducted. In nearly all situations, potential adaptive design modifications should be planned 
and described in the clinical trial protocol (and in a separate statistical analysis plan) prior to 
initiation of the trial.  
 
  This guidance distinguishes between those trials that are intended to provide substantial 
evidence of effectiveness and other trials, termed exploratory trials.5 This distinction 
depends on multiple features of a clinical trial, such as the clinical relevance of the primary 
endpoint, quality of trial conduct, rigor of control of the chance of erroneous conclusions, 
and reliability of estimation.  
 
  A fixed sample trial is a clinical trial with a targeted total sample size, or a targeted total 
number of events,6 that is specified at the design stage and not subject to prospectively 
planned adaptation. 
 
  A non-adaptive trial is a clinical trial without any prospectively planned opportunities for 
modifications to the design. 
 
  Bias is a systematic tendency for the estimate of treatment effect to deviate from its true 
value.  
 
  Reliability is the extent to which statistical inference from the clinical trial accurately and 
precisely evaluates the treatment effect. 
 
  A critical component of the demonstration of the effectiveness and, in some cases, safety of a 
drug is the test of a null hypothesis in a clinical trial. If the null hypothesis is rejected at a 
specified level of significance (typically a one-sided level equal to .025), with demonstration 
of a clinically meaningful effect of the drug, the evidence generally supports a conclusion of 
effectiveness. Sometimes, however, the null hypothesis is rejected even though the drug is 
ineffective. This is called a Type I error. Typically, there are multiple scenarios for which the 
null hypothesis is true. We will use the term Type I error probability to refer to the maximum 
probability of rejecting the null hypothesis across these scenarios. 
 
C.  Potential Advantages and Examples 
 
Adaptive designs can provide a variety of advantages over non-adaptive designs. These 
advantages arise from the fundamental property of clinical trials with an adaptive design: they 
allow the trial to adjust to information that was not available when the trial began. The specific 
                                                 
5 A variety of terms have been used to describe different kinds of clinical trials, such as phase 1, phase 2, and phase 
3 (21 CFR 312.21); pivotal; registration; and confirmatory (FDA guidance for industry E9 Statistical Principles for 
Clinical Trials (September 1998)). These terms will not be used in this guidance. 
6 In settings where the primary outcome of interest is the time to event (such as death), the statistical power of the 
trial is determined by the total number of observed events rather than the sample size. 
 
  3  Contains Nonbinding Recommendations 
 
nature of the advantages depends on the scientific context and type or types of adaptation 
considered, with potential advantages falling into the following major categories: 
 
  Statistical efficiency: In some cases, an adaptive design can provide a greater chance to 
detect a true drug effect (i.e., greater statistical power) than a comparable non-adaptive 
design.7 This is often true, for example, of group sequential designs (section V.A.) and 
designs with adaptive modifications to the sample size (section V.B.). Alternatively, an 
adaptive design may provide the same statistical power with a smaller expected sample size8 
or shorter expected duration than a comparable non-adaptive design.  
 
  Ethical considerations: There are many ways in which an adaptive design can provide ethical 
advantages over a non-adaptive design. For example, the ability to stop a trial early if it 
becomes clear that the trial is unlikely to demonstrate effectiveness can reduce the number of 
patients exposed to the unnecessary risk of an ineffective investigational treatment and allow 
subjects the opportunity to explore more promising therapeutic alternatives. 
 
  Improved understanding of drug effects: An adaptive design can make it possible to answer 
broader questions than would normally be feasible with a non-adaptive design. For example, 
an adaptive enrichment design (section V.C.) may make it possible to demonstrate 
effectiveness in either a given population of patients or a targeted subgroup of that 
population, where a non-adaptive alternative might require infeasibly large sample sizes. An 
adaptive design can also yield improved understanding of the effect of the experimental 
treatment. For example, a design with adaptive dose selection (section V.D.) may yield better 
estimates of the dose-response relationship, which may also lead to more efficient subsequent 
trials. 
 
  Acceptability to stakeholders: An adaptive design may be considered more acceptable to 
stakeholders than a comparable non-adaptive design because of the added flexibility. For 
example, sponsors might be more willing to commit to a trial that allows planned design 
modifications based on accumulating information. Patients may be more willing to enroll in 
trials that use response-adaptive randomization (section V.E.) because these trials can 
increase the probability that subjects will be assigned to the more effective treatment. 
 
The following examples of clinical trials with adaptive designs illustrate some of the potential 
advantages: 
 
  A clinical trial was conducted to evaluate Eliprodil for treatment of patients suffering from 
severe head injury (Bolland et al. 1998). The primary efficacy endpoint was a three-category 
outcome defining the functional status of the patient after six months of treatment. There was 
considerable uncertainty at the design stage about the proportions of patients in the placebo 
control group who would be expected to experience each of the three different functional 
outcomes. An interim analysis was prespecified to update estimates of these proportions 
based on pooled, non-comparative data in order to potentially increase the sample size. This 
                                                 
7 An example of a comparable non-adaptive design is a fixed sample design with sample size equal to the expected 
sample size of the adaptive design.  
8 The expected sample size is the average sample size if the trial were repeated many times.  
  4  Contains Nonbinding Recommendations 
 
approach was chosen to avoid a trial with inadequate statistical power and therefore helped 
ensure that the trial would efficiently and reliably achieve its objective. The interim analysis 
ultimately led to a sample size increase from 400 to 450 patients. 
 
  PARADIGM-HF was a clinical trial in patients with chronic heart failure with reduced-
ejection fraction designed to compare LCZ696, a combination of the neprilysin inhibitor 
sacubitril and the renin-angiotensin system (RAS) inhibitor valsartan, with the RAS inhibitor 
enalapril with respect to risk of the composite endpoint of cardiovascular death or 
hospitalization for heart failure (McMurray et al. 2014). The trial design included three 
planned interim analyses after accrual of one-third, one-half, and two-thirds of the total 
planned number of events, with the potential to stop the trial for superior efficacy of LCZ696 
over enalapril based on comparative results. The addition of interim analyses with stopping 
rules for efficacy reduced the expected sample size and expected duration of the trial while 
maintaining a similar probability of trial success, relative to a trial with a single analysis after 
observation of a fixed total number of events. PARADIGM-HF was stopped after the third 
interim analysis because the prespecified stopping boundary for compelling superiority of 
LCZ696 over enalapril had been crossed. The group sequential design therefore facilitated a 
more rapid determination of benefit than would have been possible with a fixed sample 
design.    
 
  To evaluate the safety and effectiveness of a nine-valent human papillomavirus (HPV) 
vaccine, a clinical trial with adaptive dose selection was carried out (Chen et al. 2015). The 
trial randomized subjects to one of three dose formulations of the nine-valent HPV vaccine or 
an active control, the four-valent HPV vaccine. An interim analysis was carried out to select 
one of the three dose formulations to carry forward into the second stage of the trial. The goal 
of the trial was to select an appropriate dose and confirm the safety and effectiveness of that 
dose in a timely manner.   
 
  STAMPEDE was a clinical trial designed to inform the practice of medicine and 
simultaneously evaluate multiple treatments in prostate cancer by comparing standard 
androgen deprivation therapy (ADT) with several different treatment regimens that combined 
ADT with one or more approved therapies (Sydes et al. 2012). The trial design included 
multiple interim analyses to potentially drop treatment arms that were not performing well 
based on comparative results. The use of a common control group, along with sequential 
analyses to potentially terminate treatment arms, allowed the simultaneous evaluation of 
several treatments more efficiently than could have been achieved in multiple individual 
trials. 
 
  PREVAIL II was a clinical trial conducted to evaluate ZMapp plus the current standard of 
care as compared to the current standard of care alone for treatment of patients with Ebola 
virus disease (PREVAIL II Writing Group et al. 2016; Dodd et al. 2016). The trial utilized a 
novel Bayesian adaptive design in which decision rules for concluding effectiveness at 
interim and final analyses were based on the Bayesian posterior probability that the addition 
of ZMapp to standard of care reduces 28-day mortality. Interim analyses were planned after 
every 2 patients completed, with no potential action taken until a minimum number of 
patients (12 per group) were enrolled. The design also allowed the potential to add 
  5  Contains Nonbinding Recommendations 
 
experimental agents as new treatment arms and the potential to supplement or replace the 
current standard of care arm with any agents determined to be efficacious during the conduct 
of the trial. 
 
D.  Limitations 
 
The following are some of the possible limitations associated with a clinical trial employing an 
adaptive design: 
 
  Adaptive designs require specific analytical methods to avoid increasing the chance of 
erroneous conclusions and introducing bias in estimates. For complex adaptive designs, such 
methods may not be readily available, and simulations are often critical (section VI.A.).  
 
  Gains in efficiency in some respects may be offset by losses in other respects. For example, 
an adaptive design may have a reduced minimum and expected sample size but have an 
increased maximum sample size9 relative to a comparable non-adaptive design. In addition, 
preplanning adaptive design modifications can require more effort at the design stage, 
leading to longer lead times between planning and starting the trial.  
 
  The use of an adaptive design adds logistical challenges to ensuring appropriate trial conduct 
and trial integrity. In particular, approaches to appropriately limit access to comparative 
interim results may be complex and add costs to the trial. In addition, it is challenging to 
ensure high-quality interim data are available in a timely manner so that adaptive decision-
making is based on up-to-date and reliable results. 
 
  The opportunity for efficiency gains through adaptation may be limited by important 
scientific constraints or in certain clinical settings. For example, a minimum sample size may 
be expected for a reliable evaluation of safety. There also may be limited utility in certain 
types of adaptations if the primary outcome of interest is ascertained over a longer period of 
time than the time it takes to enroll most or all patients in the trial.   
 
  An adaptive change to a trial design may lead to results after the adaptation that are different 
from those before the adaptation. This may lead to challenges in interpretability of results. 
 
E.  Choosing to Adapt 
 
In general, the decision to use or not use adaptive elements in a clinical trial design will depend 
on a large number of factors, including the potential advantages and disadvantages described in 
the preceding sections. There may also be a variety of non-scientific considerations. In short, 
designing a clinical trial is a complex process, and it is not the intent of this guidance to require 
or restrict the use of adaptive designs in general or in specific settings. However, FDA 
encourages sponsors to explore a variety of design options in planning and to discuss their 
                                                 
9 The minimum and maximum sample sizes are the smallest and largest sample sizes, respectively, that could be 
selected under the adaptive design if the trial were repeated many times. 
  6  Contains Nonbinding Recommendations 
 
considerations with the appropriate FDA review division at regulatory meetings such as End-of-
Phase-2 (EOP2) or Type C meetings. 
 
 
III.  PRINCIPLES FOR ADAPTIVE DESIGNS   
 
In general, the design, conduct, and analysis of an adaptive clinical trial intended to provide 
substantial evidence of effectiveness should satisfy four key principles: the chance of erroneous 
conclusions should be adequately controlled, estimation of treatment effects should be 
sufficiently reliable, details of the design should be completely prespecified, and trial integrity 
should be appropriately maintained. While all clinical trials intended to provide substantial 
evidence of effectiveness should satisfy these four principles, the following sections outline 
considerations specific to adaptive designs. 
 
A.  Controlling the Chance of Erroneous Conclusions 
 
Because clinical trials play a central role in premarket decision-making, it is critical to assess the 
probability that any trial design under consideration will lead to incorrect conclusions of safety 
or effectiveness, incorrect conclusions of lack of safety or effectiveness, or misleading estimates 
that contribute to an overall assessment of benefit-risk. For example, there are a number of ways 
in which adaptive features can inflate the Type I error probability of a trial. The most obvious 
examples of this are cases in which multiple statistical hypothesis tests are performed. Consider a 
group sequential design, in which a preliminary test to potentially stop the trial for efficacy is 
performed after 50 percent of planned subjects have completed the trial. If the trial is not stopped 
early, a final test is performed once 100 percent of the planned subjects have completed the trial. 
If each of these two tests were performed at the conventional .025 one-sided significance level 
and the drug were not effective, the overall chance of the trial yielding a Type I error would 
exceed 2.5 percent. This is a well-known problem, and a variety of methods exist to determine 
appropriate significance levels for interim and final analyses that together ensure the overall 
Type I error probability of the trial is controlled at 2.5 percent (Jennison and Turnbull 1999). 
 
Explicit multiple hypothesis tests are not the only way adaptive design features can lead to 
erroneous conclusions. Consider a naive approach to adaptive patient population selection, in 
which data in the overall trial population and in a subpopulation are examined halfway through a 
trial, and the population with the larger treatment effect at that point is chosen for continued 
study. If the final analysis is performed in the selected population at a .025 significance level and 
includes the same data that were used to choose the patient population, the Type I error 
probability would exceed 2.5 percent. Other adaptive design features may introduce still more 
subtle Type I error probability inflation. 
 
Adaptive design proposals for trials incorporating null hypothesis testing should therefore 
address the possibility of Type I error probability inflation. In some cases, such as simple group 
sequential designs (section V.A.), statistical theory can be used to derive significance levels that 
ensure Type I error probability is controlled at the desired level. In other cases, such as sample 
size re-estimation based on non-comparative interim results (section IV.), it can be shown that 
performing analyses at the conventional .025 significance level has no effect or a limited effect 
  7  Contains Nonbinding Recommendations 
 
on the Type I error probability. In still other cases, such as many Bayesian adaptive designs 
(section VI.B.), it may be critical to use simulations (section VI.A.) to evaluate the chance of an 
erroneous conclusion.  
 
B.  Estimating Treatment Effects 
 
It is important that clinical trials produce sufficiently reliable treatment effect estimates to 
facilitate an evaluation of benefit-risk and to appropriately label new drugs, enabling the practice 
of evidence-based medicine. Some adaptive design features can lead to statistical bias in the 
estimation of treatment effects and related quantities. For example, each of the two cases of Type 
I error probability inflation mentioned in section III.A. above has a potential for biased estimates. 
Specifically, a conventional end-of-trial treatment effect estimate such as a sample mean that 
does not take the adaptations into account would tend to overestimate the true population 
treatment effect. This is true not only for the primary endpoint which formed the basis of the 
adaptations, but also for secondary endpoints correlated with the primary endpoint. Furthermore, 
confidence intervals for the primary and secondary endpoints may not have correct coverage 
probabilities for the true treatment effects. 
 
For some designs there are known methods for adjusting estimates to reduce or remove bias 
associated with adaptations and to improve performance on measures such as the mean squared 
error10 (e.g., Jennison and Turnbull 1999; Wassmer and Brannath 2016). Such methods should 
be prospectively planned and used for reporting results when they are available. Biased 
estimation in adaptive design is currently a less well-studied phenomenon than Type I error 
probability inflation, however, and methods may not be available for other designs. For these 
other designs, the extent of bias in estimates should be evaluated, and treatment effect estimates 
and associated confidence intervals should be presented with appropriate cautions regarding their 
interpretation. 
 
C.  Trial Planning 
 
In general, as with any clinical trial,11 it is expected that the details of the adaptive design are 
completely specified prior to initiation of the trial and documented accordingly (section VIII.B.). 
Prospective planning should include prespecification of the anticipated number and timing of 
interim analyses, the type of adaptation, the statistical inferential methods to be used, and the 
specific algorithm governing the adaptation decision. Complete prespecification is important for 
a variety of reasons. First, for many types of adaptations, if aspects of the adaptive decision-
making are not planned, appropriate statistical methods to control the chance of erroneous 
conclusions and to produce reliable estimates may not be feasible once data have been collected. 
Second, complete prespecification helps increase confidence that adaptation decisions were not 
based on accumulating knowledge in an unplanned way. For example, consider a trial with 
planned sample size re-estimation based on pooled, non-comparative interim estimates of the 
variance (section IV.) in which personnel involved in the adaptive decision-making (e.g., a 
monitoring committee) have access to comparative interim results. Prespecification that includes 
                                                 
10 The mean squared error is a measure of the performance of an estimate that incorporates both bias and variability. 
11 FDA guidance for industry E9 Statistical Principles for Clinical Trials (September 1998) recommends 
prespecification of the design and analysis plan for all clinical trials. 
  8  Contains Nonbinding Recommendations 
 
the exact rule for modifying the sample size reduces concern that the adaptation could have been 
influenced by knowledge of comparative results and precludes the use of a statistical adjustment 
to account for modifications based on comparative interim results (section V.B.). Finally, 
complete prespecification can motivate careful planning at the design stage, eliminate 
unnecessary sponsor access to comparative interim data, and help ensure that the DMC, if 
involved in implementing the adaptive design, effectively focuses on its primary responsibilities 
of maintaining patient safety and trial integrity (section VII.).  
 
Although we recommend prespecification of the rules governing adaptations, monitoring 
committee recommendations might occasionally deviate from the anticipated algorithm based on 
the totality of the data. If this type of flexibility is desired, the prespecified plan should 
acknowledge the possibility of deviations from the anticipated algorithm, outline factors that 
may lead to such deviations, and propose testing and estimation methods that do not rely on strict 
adherence to the algorithm. When completely unforeseen circumstances arise, we recommend 
discussing any potential design changes with FDA as soon as possible. 
 
D.   Maintaining Trial Conduct and Integrity 
 
Adaptive designs can create additional trial operational complications. Knowledge of 
accumulating data can affect the course and conduct of a trial, and the behavior of its sponsor, 
investigators, and participants, in ways that are difficult to predict and impossible to adjust for. 
Therefore, for all clinical trials (adaptive and non-adaptive) it is strongly recommended that 
access to comparative interim results be limited to individuals with relevant expertise who are 
independent of the personnel involved in conducting or managing the trial.12 Maintaining 
confidentiality of comparative interim results is especially challenging when the trial design 
includes adaptive features. Two examples of issues that could arise in adaptive trials are: 
 
  If investigators are improperly provided access to comparative results from an interim 
analysis, knowledge of a small or unfavorable estimated treatment effect based on unreliable 
data could be misinterpreted as reliable evidence of no effect, leading to decreased adherence 
and decreased efforts to retain patients, increasing the amount of missing data in the 
remainder of the trial. 
 
  After an interim analysis in a design with sample size re-estimation based on comparative 
results (section V.B.), knowledge that the targeted sample size has been increased could be 
interpreted by investigators and potential trial subjects as indicative of a less-than-expected 
interim treatment effect, potentially depressing future enrollment and endangering the 
success of the trial. 
 
As these and other similar issues are generally impossible to adjust for once data have been 
collected, planning for an adaptive design trial should include a consideration of possible sources 
and consequences of trial conduct issues and plans to avoid these issues. Plans should describe 
the processes intended to control access to information and to document access throughout the 
trial. This is discussed in more detail in section VII. 
                                                 
12 This recommendation is also conveyed in FDA guidance for industry E9 Statistical Principles for Clinical Trials 
(September 1998). 
  9  Contains Nonbinding Recommendations 
 
IV.  ADAPTIVE DESIGNS BASED ON NON-COMPARATIVE DATA 
 
This section addresses adaptive clinical trial designs in which adaptations are based entirely on 
analyses of non-comparative data, that is, without incorporating information about treatment 
assignment. Such analyses are sometimes called blinded or masked analyses. In general, 
adequately prespecified adaptations based on non-comparative data have no effect or a limited 
effect on the Type I error probability. This makes them an attractive choice in many settings, 
particularly when uncertainty about event probabilities or endpoint variability is high.    
 
Accumulating outcome data can provide a useful basis for trial adaptations. The analysis of 
outcome data without using treatment assignment is sometimes called pooled analysis. The most 
widely used category of adaptive design based on pooled outcome data involves sample size 
adaptations (sometimes called blinded sample size re-estimation). Sample size calculations in 
clinical trials depend on several factors: the desired significance level, the desired power, the 
assumed or targeted difference in outcome due to treatment assignment, and additional nuisance 
parametersvalues that are not of primary interest but may affect the statistical comparisons. In 
trials with binary outcomes such as a response or an undesirable event, the probability of 
response or event in the control group is commonly considered a nuisance parameter. In trials 
with continuous outcomes such as symptom scores, the variance of the scores is a nuisance 
parameter. By using accumulating information about nuisance parameters, sample sizes can be 
adjusted according to prespecified algorithms to ensure the desired power is maintained. In some 
cases, these techniques involve statistical modeling to estimate the value of the nuisance 
parameter, because the parameter itself depends on knowledge of treatment assignment (Gould 
and Shih 1992). These adaptations generally do not inflate the Type I error probability. However, 
there is the potential for limited Type I error probability inflation in trials incorporating 
hypothesis tests of non-inferiority or equivalence (Friede and Kieser 2003). Sponsors should 
evaluate the extent of inflation in these scenarios. 
 
Another example of adapting based on pooled outcome data is the planned interim reevaluation 
of the prognostic strength of a biomarker or other baseline characteristic in a prognostic 
enrichment strategy.13 For example, a trial may be targeting greater enrollment among patients 
with a certain biomarker to increase the number of endpoint events, but interim pooled outcome 
data may suggest the biomarker does not have the anticipated effect on the pooled event rate, 
perhaps leading to a change in recruitment strategies. 
 
 
V.  ADAPTIVE DESIGNS BASED ON COMPARATIVE DATA 
 
This section discusses different types of clinical trial designs in which there are prespecified 
rules for stopping the trial or modifying the design based on interim analyses of comparative 
data. Such analyses are sometimes called unblinded or unmasked analyses. There are a few 
important concepts that are generally applicable to the sections that follow. First, in contrast to 
adaptations based on non-comparative data, adaptations based on comparative data often directly 
increase the Type I error probability and induce bias in treatment effect estimates. Therefore, 
                                                 
13 See additional discussion in the FDA guidance for industry Enrichment Strategies for Clinical Trials to Support 
Approval of Human Drugs and Biological Products (March 2019). 
  10  Contains Nonbinding Recommendations 
 
statistical methods should take into account the adaptive trial design. Second, when adaptations 
are based on comparative interim analyses, additional steps are critical to ensure appropriate trial 
conduct. This is discussed in more detail in section VII. Finally, stopping or adaptation rules can 
be specified on a variety of different scales, such as the estimate of treatment effect, fixed sample 
p-value, conditional probability of trial success, Bayesian posterior probability that the drug is 
effective, or Bayesian predictive probability of trial success. The choice of scale is relatively 
unimportant as long as the operating characteristics14 of the designs are adequately evaluated.  
 
A.  Group Sequential Designs 
 
Group sequential trials allow for one or more prospectively planned interim analyses of 
comparative data with prespecified criteria for stopping the trial. The inclusion of sequential 
analyses can provide ethical and efficiency advantages by reducing the expected sample size and 
duration of clinical trials and by accelerating the approval of safe and effective new treatments. 
For example, a group sequential design with a single interim analysis and a commonly used 
stopping boundary for efficacy can reduce the expected sample size of the trial by roughly 15 
percent relative to a comparable fixed sample trial.15  
 
Group sequential designs may include rules for stopping the trial when there is sufficient 
evidence of efficacy to support regulatory decision-making or when there is evidence that the 
trial is unlikely to demonstrate efficacy, which is often called stopping for futility. Performing 
each of the multiple statistical hypothesis tests for efficacy in a group sequential trial at the 
conventional .025 one-sided significance level would inflate the Type I error probability and, 
therefore, increase the chance of erroneous conclusions. A variety of methods exist to determine 
appropriate stopping boundaries for the interim and final analyses such that the Type I error 
probability is appropriately controlled. For example, the OBrien-Fleming approach tends to 
require very persuasive early results to stop the trial for efficacy (OBrien and Fleming 1979). 
Alternative approaches such as that proposed by Pocock require less persuasive early results and 
have higher probabilities of early stopping (Pocock 1977). These and other approaches rely on 
prospective planning of both the number of interim analyses and the specific sample size or 
number of event targets at which those analyses will occur.  
 
The Lan-DeMets alpha-spending16 approach accommodates varying levels of evidence for early 
stopping by specifying a function for how the Type I error probability is spent throughout the 
trial, while also allowing for flexibility in determining the number and timing of interim analyses 
(Lan and DeMets 1983). The flexibility in timing helps accommodate scheduling of monitoring 
meetings at specific calendar times rather than at specific interim sample sizes or number of 
                                                 
14 Trial operating characteristics are the properties of the trial with a given design. For example, properties of interest 
might include Type I error probability; power; expected, minimum, and maximum sample size; bias of treatment 
effect estimates; and coverage of confidence intervals (i.e., the probability the confidence interval would include the 
true treatment effect if the clinical trial were repeated many times). 
15 A group sequential design with an interim analysis that occurs when outcome information is available on half of 
the maximum number of patients and that utilizes an OBrien-Fleming stopping boundary for efficacy, reduces the 
expected sample size of the trial by roughly 15 percent if the alternative hypothesis (at which there is 90 percent 
power) is true, as compared to a design with a single analysis planned when all patients have been enrolled and had 
their outcomes ascertained.  
16 The Type I error probability of a clinical trial is often denoted by the Greek letter  (alpha). 
  11  Contains Nonbinding Recommendations 
 
event targets. The flexibility in the number of analyses can help accommodate faster- or slower-
than-expected enrollment rates. If, however, interim analysis times are chosen based on 
accumulating comparative results, the Type I error probability can be inflated. For example, 
adjusting the next interim analysis to occur sooner than originally planned because the current 
interim analysis result is close to the stopping boundary would not be appropriate. Because of 
this potential issue with the Lan-DeMets alpha-spending approach, sponsors should put in place 
additional safeguards such as a targeted number of interim analyses and an approximate schedule 
for their occurrence, as well as a decision framework for changing the number or timing of 
analyses after the trial has begun. The decision framework should be based on information that is 
statistically independent of the estimated treatment effect (e.g., enrollment rate or scheduling 
logistics). For example, the decision framework could specify semi-annual interim analyses, with 
additional analyses planned if enrollment is considerably slower than a prespecified target.   
 
There are a number of additional considerations for ensuring the appropriate design, conduct, 
and analysis of a group sequential trial. First, for group sequential methods to be valid, it is 
important to adhere to the prospective analytic plan and terminate the trial for efficacy only if the 
stopping criteria are met. Second, guidelines for stopping the trial early for futility should be 
implemented appropriately. Trial designs often employ nonbinding futility rules, in that the 
futility stopping criteria are guidelines that may or may not be followed, depending on the 
totality of the available interim results. The addition of such nonbinding futility guidelines to a 
fixed sample trial, or to a trial with appropriate group sequential stopping rules for efficacy, does 
not increase the Type I error probability and is often appropriate. Alternatively, a group 
sequential design may include binding futility rules, in that the trial should always stop if the 
futility criteria are met. Binding futility rules can provide some advantages in efficacy analyses 
(e.g., a relaxed threshold for a determination of efficacy), but the Type I error probability is 
controlled only if the stopping rules are followed. Therefore, if a trial continues despite meeting 
prespecified binding futility rules, the Agency will likely consider that trial to have failed to 
provide evidence of efficacy, regardless of the outcome at the final analysis. Note also that some 
DMCs might prefer the flexibility of nonbinding futility guidelines. 
 
Third, a trial terminated early for efficacy will have a smaller sample size for the evaluation of 
safety and potentially important secondary efficacy endpoints. Therefore, early stopping for 
efficacy is typically reserved for circumstances where there are compelling ethical reasons (e.g., 
the primary endpoint is survival or irreversible morbidity) or where the stopping rules require 
highly persuasive results in terms of both the magnitude of the estimated treatment effect and the 
strength of evidence of an effect. In some cases, there may be a limit on how early group 
sequential interim analyses should occur or whether they should occur at all because of a 
minimum sample size expected for a reliable evaluation of safety. This is often true, for example, 
in preventive vaccine trials. 
 
Finally, conventional fixed sample estimates of the treatment effect such as the sample mean 
tend to be biased toward greater effects than the true value when a group sequential design is 
used. Similarly, confidence intervals do not have the desired nominal coverage probabilities. 
Therefore, a variety of methods exist to compute estimates and confidence intervals that 
appropriately adjust for the group sequential stopping rules (Jennison and Turnbull 1999). To 
ensure the scientific and statistical credibility of trial results and facilitate important benefit-risk 
  12  Contains Nonbinding Recommendations 
 
considerations, an approach for calculating estimates and confidence intervals that appropriately 
accounts for the group sequential design should be prospectively planned and used for reporting 
results. 
 
B.  Adaptations to the Sample Size 
 
One adaptive approach is to prospectively plan modifications to the sample size based on interim 
estimates of nuisance parameters from analyses that utilize treatment assignment information. 
For example, there are techniques that estimate the variance of a continuous outcome 
incorporating estimates of the variances on the individual treatment arms, or that estimate the 
probability of a binary outcome on the control arm based on only data from that arm. These 
approaches generally have no effect, or a limited effect, on the Type I error probability. 
However, unlike adaptations based on non-comparative pooled interim estimates of nuisance 
parameters (section IV.), these adaptations involve treatment assignment information and, 
therefore, require additional steps to maintain trial integrity (section VII.). 
 
Another adaptive approach is to prospectively plan modifications to the sample size based on 
comparative interim results (i.e., interim estimates of the treatment effect). This is often called 
unblinded sample size adaptation or unblinded sample size re-estimation. Sample size 
determination depends on many factors, such as the event rate in the control arm or the 
variability of the primary outcome, the Type I error probability, the hypothesized treatment 
effect size, and the desired power to detect this effect size. In section IV., we described potential 
adaptations based on non-comparative interim results to address uncertainty at the design stage 
in the variability of the outcome or the event rate on the control arm. In contrast, designs with 
sample size adaptations based on comparative interim results might be used when there is 
considerable uncertainty about the true treatment effect size. Similar to a group sequential trial, a 
design with sample size adaptations based on comparative interim results can provide adequate 
power under a range of plausible effect sizes, and therefore, can help ensure that a trial maintains 
adequate power if the true magnitude of treatment effect is less than what was hypothesized, but 
still clinically meaningful. Furthermore, the addition of prespecified rules for modifying the 
sample size can provide efficiency advantages with respect to certain operating characteristics in 
some settings.  
 
Indiscriminately modifying the sample size of a trial without proper adjustment can inflate the 
Type I error probability. Consider a design with one interim analysis at which the interim 
estimate of treatment effect is used to modify the final sample size. If one carries out a 
hypothesis test at the end of the trial at the conventional .025 significance level, the Type I error 
probability can be more than doubled (Proschan and Hunsberger 1995).17 Therefore, one of a 
variety of available methods should be used to appropriately control the Type I error probability 
with this type of adaptive design. For example, hypothesis testing approaches have been 
developed based on combining test statistics or p-values from the different stages of the trial in a 
preplanned manner or through preservation of the conditional Type I error probability (e.g., 
Bauer and Kohne 1994; Fisher 1998; Cui et al. 1999; Denne 2001; Muller and Schafer 2001; 
Chow and Chang 2011). These approaches also accommodate adaptations to aspects of the 
                                                 
17 This means that even use of the Bonferroni method to adjust for the two analyses conducted would not be 
adequate. 
  13  Contains Nonbinding Recommendations 
 
sampling plan other than the maximum sample size, such as the number and spacing of future 
interim analyses.  
 
The additional considerations regarding adherence to the adaptation plan, the evaluation of 
safety, and the estimation of treatment effects that were discussed in section V.A. on group 
sequential designs also apply to designs with sample size adaptations based on comparative data. 
Of note, prospective planning should include prespecification of not only the statistical 
hypothesis testing method that will be used, but also the rule governing the sample size 
modification. Finally, there are additional challenges in maintaining trial integrity in the presence 
of sample size adaptations. For example, sample size modification rules are often based on 
maintaining the conditional probability of a statistically significant treatment effect at the end of 
the trial (often called the conditional power) at or near some desired level. In this scenario, 
knowledge of the adaptation rule and the adaptively chosen sample size allows a relatively 
straightforward back-calculation of the interim estimate of treatment effect. Therefore, additional 
steps should be taken to limit personnel with this detailed knowledge so that trial integrity can be 
maintained. See section VII. for additional discussion.  
 
The principles discussed in this section also apply to trials with time-to-event endpoints where 
the adaptive design allows prospectively planned modifications to the total number of events 
based on comparative interim results. However, there are some special additional considerations 
in such settings that are discussed further in section VI.C. 
 
C.  Adaptations to the Patient Population (e.g., Adaptive Enrichment) 
 
In many settings, it may be expected that the treatment effect will be greater in a certain subset of 
the trial population. This subpopulation could be defined, for example, by a demographic 
characteristic or by a genetic or pathophysiologic marker that is thought to be related to the 
drugs mechanism of action. In such a setting, consideration could be given to a design that 
allows adaptive modifications to the patient population based on comparative interim results. For 
example, a trial might enroll subjects from the overall trial population up through an interim 
analysis, at which time a decision will be made based on prespecified criteria whether to 
continue enrollment in the overall population or to restrict future enrollment to the targeted 
subpopulation. Data accumulated both before and after the interim analysis may be combined to 
draw inference on the treatment effect in the targeted group. This type of design, often called an 
adaptive enrichment18 design, can provide advantages over non-adaptive designs. In particular, 
such an adaptive design can provide greater power19 at the same sample size as a non-adaptive 
fixed sample design in the overall population. Furthermore, unlike a trial restricting enrollment 
to the targeted subpopulation, the adaptive design allows an evaluation of the experimental 
treatment in the non-targeted (complementary) subpopulation.  
 
A design that allows adaptive modifications to the patient population often involves both (1) 
modification of design features, such as the enrolled population and the population evaluated in 
                                                 
18 The term adaptive enrichment is used, for example, in the FDA guidance for industry Enrichment Strategies for 
Clinical Trials to Support Approval of Human Drugs and Biological Products (March 2019).   
19 Power in this context could be defined, for example, as the probability of successfully identifying a true treatment 
effect in either the targeted subpopulation or the overall population. 
  14  Contains Nonbinding Recommendations 
 
the primary analysis, based on comparative interim results; and (2) hypothesis tests in multiple 
populations, such as a targeted subpopulation and the overall population. Therefore, statistical 
hypothesis testing methods should account for both sources of multiplicity. For example, one 
approach is to combine test statistics or p-values from the different stages of the trial in a 
preplanned manner, while also using an appropriate multiple testing procedure (Wassmer and 
Brannath 2016). Such an approach could potentially also accommodate adaptations to the sample 
size or to the proportion of patients enrolled from a particular subpopulation (e.g., increasing the 
proportion in a subset rather than completely restricting enrollment to that subset). 
 
There are a number of important considerations beyond those previously discussed for group 
sequential designs and designs with adaptive modifications to the sample size. First, in the case 
of an adaptive enrichment design, the proposed adaptive modifications to the patient population 
should be motivated by results from previous (e.g., early-phase) trials and/or strong biologic 
plausibility that the benefit-risk profile will be most favorable in a particular subpopulation. 
Second, if the baseline characteristic that is thought to modify the treatment effect is not binary 
in nature, any threshold or thresholds used to define subpopulations should be appropriately 
justified. Third, the identification of the targeted subpopulation may depend on the use of an in 
vitro diagnostic device. In this scenario, the diagnostic device should have adequate performance 
characteristics.20 Finally, the extent to which the trial should be designed to characterize the 
treatment effect in the complementary subpopulation may depend on a number of factors, such 
as the pathophysiologic or empirical rationale for enrichment, the toxicities of the drug, the 
distribution of the baseline marker defining the subpopulations, the justification for a threshold 
defining subpopulations, and the potential for off-label use in the complementary subpopulation 
if approval is limited to the targeted subpopulation.  
 
D.  Adaptations to Treatment Arm Selection  
 
Another adaptive approach is to prospectively plan modifications to the treatment arms included 
in the clinical trial based on comparative interim results. Modifications could include adding or 
terminating arms. This kind of design has often been used in early-phase exploratory dose-
ranging trials. An adaptive dose-ranging trial might begin with several doses and incorporate 
interim analyses based on comparative data to select doses for continued evaluation, with the 
goal of providing improved characterization of the dose-response relationship relative to a non-
adaptive design and allowing selection of an optimal dose or doses for evaluation in future 
confirmatory trials. For example, the continual reassessment method (CRM) is an approach to 
adaptively escalate the doses evaluated in early-phase trials based on observed toxicities in order 
to reliably and efficiently estimate the maximum tolerated dose for a new drug (Le Tourneau et 
al. 2009). Adaptive treatment arm selection is also possible in trials intended to provide 
substantial evidence of effectiveness. For example, in a setting where it is plausible that either or 
both of two doses might have a favorable benefit-risk profile, an adaptive design with sequential 
analyses allowing early termination of one of the dose arms can meet its scientific objective in a 
                                                 
20 See the FDA guidance for industry and FDA staff In Vitro Companion Diagnostic Devices (August 2014) and the 
FDA draft guidance for industry and FDA staff Principles for Codevelopment of an In Vitro Companion Diagnostic 
Device with a Therapeutic Product (July 2016). When final, this guidance will represent the FDAs current thinking 
on this topic. For the most recent version of a guidance, check the FDA guidance web page at 
https://www.fda.gov/RegulatoryInformation/Guidances/default.htm. 
  15  Contains Nonbinding Recommendations 
 
more efficient manner than alternative non-adaptive designs. Such an adaptive design could in 
principle allow interim modifications to additional aspects of the design, such as the number of 
additional patients that will be enrolled (the sample size) and the randomization ratio for 
treatment arms carried forward. 
 
For trials intended to provide substantial evidence of effectiveness, statistical hypothesis testing 
methods should account for the adaptive selection of a best dose or doses from among the 
multiple doses evaluated in the trial, as well as any additional adaptive modifications, such as the 
potential to stop the trial early or to modify future sample sizes. In the simple case of a design 
with more than one dose that includes interim analyses to potentially stop enrollment for a 
particular dose for efficacy or futility, typical group sequential testing methods can be used, 
along with some multiple testing approach to control the Type I error probability across the 
multiple doses evaluated. If the design allows for additional adaptations such as modifications to 
the sample size, methods such as those described for sample size and population adaptations 
should be used. As with other adaptive designs, prospective planning is important and should 
include prespecification of not only the testing method, but also the adaptation rule for selecting 
treatment arms and for any other potential interim modifications. In general, seamless designs 
that incorporate both dose selection and confirmation of efficacy of a selected dose (based on 
data from the entire trial) can be considered if the principles outlined in section III. are followed. 
 
A special case of adaptive treatment arm selection occurs in the context of an adaptive platform 
trial designed to compare more than one experimental treatment against an appropriate control 
for a disease (e.g., Woodcock and LaVange 2017). Two features of these trials often 
incorporated for efficiency gains are use of a common control arm and use of prospectively 
planned adaptations to select promising treatments at interim analyses for continued study. 
Because these trials may involve investigational agents from more than one sponsor, may be 
conducted for an unstated length of time, and often involve complex adaptations, they should 
generally involve extensive discussion with FDA. 
 
E.  Adaptations to Patient Allocation 
 
This section considers two types of adaptations to patient allocation: adaptations based on 
comparative baseline characteristic data and adaptations based on comparative outcome data. 
The first type is covariate-adaptive treatment assignment, a technique in which a patients 
treatment assignment depends in part or entirely on his or her baseline characteristics and the 
baseline characteristics and treatment assignments of previously enrolled patients. Such an 
approach is used to promote balance between treatment groups on baseline covariates. One well-
known example of covariate-adaptive randomization is minimization (Pocock and Simon 1975), 
which involves assigning each consecutive patient to treatment in such a way that differences 
between treatment groups on potentially prognostic covariates are minimized. Covariate-adaptive 
treatment assignment techniques do not directly increase the Type I error probability when 
analyzed with the appropriate methodologies (generally randomization or permutation tests). 
These techniques can increase the predictability of treatment assignment relative to simple 
randomization, but this predictability can be mitigated with an additional random component to 
prevent perfectly deterministic treatment assignment.  
 
  16  Contains Nonbinding Recommendations 
 
The second type is response-adaptive randomization, an adaptive feature in which the chance of 
a newly-enrolled subject being assigned to a treatment arm varies over the course of the trial 
based on accumulating outcome data for subjects previously enrolled. There are a variety of 
response-adaptive randomization techniques, some of which go by names such as play the 
winner designs. Statistical, ethical, and pragmatic rationales are all sometimes given for using 
response-adaptive randomization. In statistical terms, response-adaptive techniques can in some 
circumstances minimize the variance of the test statistics, leading to shorter trials, smaller sample 
sizes, and/or greater statistical power. The ethical argument for response-adaptive randomization 
is that this design feature can lead to more trial subjects being assigned to the more promising of 
the treatment arms. Finally, a pragmatic argument is that clinical trials with this design feature 
can be appealing to potential participants, thereby increasing speed and ease of accrual. Note that 
the arguments for response-adaptive randomization are controversial, and some researchers feel 
that inconclusive interim results should not be used to alter randomization in an ongoing trial 
and/or that statistical efficiency is not substantially improved in two-arm trials to justify 
adjusting randomization ratios (Hey and Kimmelman 2015, and accompanying commentaries).  
 
Response-adaptive randomization alone does not generally increase the Type I error probability 
of a trial when used with appropriate statistical analysis techniques. It is important to ensure that 
the analysis methods appropriately take the design of the trial into account. Finally, as with many 
other adaptive techniques based on outcome data, response-adaptive randomization works best in 
trials with relatively short-term ascertainment of outcomes. 
 
F.  Adaptations to Endpoint Selection 
 
This is a design that allows adaptive modification to the choice of primary endpoint based on 
comparative interim results. Such a design might be motivated by uncertainty about the treatment 
effect sizes on multiple patient outcomes that would be considered acceptable primary endpoints 
by FDA. As with other adaptive designs, the adaptation rule should be prespecified, and 
statistical hypothesis testing methods should account for the adaptive endpoint selection. 
Because endpoint selection involves important clinical considerations, early discussion with the 
FDA review division is recommended when such designs are being considered. 
 
G.  Adaptations to Multiple Design Features 
 
It is possible for a clinical trial to be more complex by combining two or more of the adaptive 
design features discussed in this guidance. The same general principles apply to these complex 
designs as to simpler adaptive designs. It may be particularly difficult to estimate Type I error 
probability and other operating characteristics for designs that incorporate multiple adaptive 
features. Clinical trial simulations (section VI.A.) will often be critical to evaluate the trial 
design. 
 
 
 
  17  Contains Nonbinding Recommendations 
 
VI.  SPECIAL CONSIDERATIONS AND TOPICS 
 
A.  Simulations in Adaptive Design Planning  
 
Clinical trial simulations often play a critical role in planning and designing clinical trials in 
general and are particularly important for adaptive trials. Simulations can be used, for example, 
to select the number and timing of interim analyses, or to determine the appropriate critical value 
of a test statistic for declaring efficacy or futility. Simulations can also be useful for comparing 
the performance of alternative designs. A major use of simulations in adaptive trial design is to 
estimate trial operating characteristics and to demonstrate that these operating characteristics 
meet desired levels. 
 
Traditional non-adaptive clinical trials have generally relied on statistical theory to ensure that 
Type I error probability is controlled at a desired level and to obtain estimates of the power of the 
trial. In the simplest case, when testing a single endpoint in a fixed-sample size clinical trial 
design, it can typically be shown that the final test statistic has a certain asymptotic probability 
distribution,21 and inference and operating characteristics can then be based on the properties of 
this distribution. For many adaptive designs, such as traditional group sequential designs, it is 
similarly possible to derive asymptotic probability distributions mathematically and base 
inference and planning on those distributions. 
 
For some adaptive designs, however, it either is not possible to derive relevant distributions of 
test statistics, or the distributions themselves are not computationally tractable. This tends to be 
the case for more complex adaptive designs, such as designs that adapt several elements or 
designs that use predictive probability models to determine analysis time points. In these cases, 
trial operating characteristics can often be estimated by means of clinical trial simulations. For 
example, for Type I error probability and power, the basic logic of this approach is to simulate 
many instances of the trial based on various assumptions and evaluate the proportion of 
simulations that would have met the predetermined bar for supporting a conclusion of 
effectiveness under each set of assumptions.  
 
For simulations intended to estimate Type I error probability, hypothetical clinical trials would 
be simulated under a series of assumptions compatible with the null hypothesis. For each set of 
such assumptions, the proportion of simulated trials that led to a false positive conclusion would 
be taken as an estimate of Type I error probability under those assumptions. In almost all cases, 
there are an infinite number of scenarios potentially compatible with the null hypothesis. 
Identifying which scenarios should be considered when estimating Type I error probability can 
be challenging and may rely on a combination of medical and mathematical considerations.  
 
These scenarios may include varying assumptions about nuisance parameters. These nuisance 
parameters can include statistical parameters, such as the variance of a symptom scale or the 
probability of response in the control group, and also operational parameters, such as the speed 
of subject accrual to a trial. For example, consider a trial comparing 2-year mortality rates 
between an experimental therapy and placebo in an oncology indication with very low (for 
                                                 
21 The asymptotic distribution of a test statistic is the approximate probability distribution of that statistic when the 
sample size gets large. 
  18  Contains Nonbinding Recommendations 
 
example, median 6-month) survival. The null hypothesis is equal mortality rates in the two arms. 
Possible scenarios consistent with this null hypothesis would include equal mortality rates of 5 
percent, of 50 percent, of 99 percent, of 99.01 percent, and so on. While it is impossible to 
simulate every scenario compatible with the null hypothesis, it may be possible to determine a 
limited set of scenarios that adequately represent the plausible range of potential false positives. 
In this example, medical experts might feel comfortable ruling out any scenario with a 2-year 
placebo mortality rate below 75 percent, for instance, based on literature and clinical experience 
with the disease. Mathematical considerations can also play a role in determining which 
scenarios need to be simulated to estimate Type I error probability. It could be possible to argue 
that certain scenarios necessarily have lower Type I error probability than other scenarios based 
on monotonicity.  
 
In many cases, it will not be possible to estimate Type I error probability for every set of null 
assumptions even after taking clinical and mathematical considerations into account. It is 
common to perform simulations on a grid of plausible values and argue based on the totality of 
the evidence from the simulations that maximal Type I error probability likely does not exceed a 
desired level across the range covered by the grid. In the example above, simulations might be 
performed at placebo and experimental treatment mortality rates equal to 75, 80, 85, 90, 95, and 
99 percent. If, in each of these scenarios, estimated Type I error probability was below .025, that 
could be considered sufficient evidence that Type I error probability was adequately controlled 
for all scenarios with placebo mortality between 75 and 99 percent. However, with any approach, 
the evaluation at the end of the trial should consider whether the statistical inference is 
appropriate and the conclusions are justified in light of the accumulated information about the 
nuisance parameters. In the example, if the observed placebo mortality rate was unexpectedly 50 
percent, additional simulations would be required. 
 
Another complicating factor is the presence of multiple endpoints. If a sponsor would like to test 
multiple clinical endpoints and control the familywise Type I error probability across all of these 
endpoints, then simulations of all endpoints for each subject under null hypothesis scenarios 
should be performed, which could in turn require knowledge of the correlational structure of the 
multiple endpoints. This can be too complex an issue to address in clinical trial simulation. In 
some cases, however, it can be argued that assuming independence among multiple endpoints 
will provide an upper bound on the Type I error probability. This is true, for instance, when 
using the Bonferroni or Holm approach to control for multiple testing.22  
 
It is important to consider the precision of simulated operating characteristics, which depends on 
the number of simulated trials (iterations). The number of iterations should be sufficient to 
facilitate an understanding and review of the proposed clinical trial design. Using 100,000 
iterations per scenario, for instance, ensures a 95% confidence interval for estimated Type I error 
probability with a width of approximately  0.1%, which would be sufficient in most cases. This 
will allow very small differences in estimated Type I error probability to be identified, which 
may be important in some cases. In general, it is also preferable to use different random seeds for 
different simulation scenarios; this helps avoid consistently atypical results across scenarios. In 
                                                 
22 Additional discussion on the Bonferroni, Holm, and other multiple testing approaches can be found in the FDA 
draft guidance for industry Multiple Endpoints in Clinical Trials (January 2017). When final, this guidance will 
represent the FDAs current thinking on this topic.  
  19  Contains Nonbinding Recommendations 
 
some cases, fewer iterations might suffice to evaluate Type I error probability. For example, it 
might be sufficient to use 10,000 iterations if a particularly fine grid of scenarios is explored and 
every scenario has an estimated Type I error probability below the desired level. Also, a smaller 
number of simulations can generally be used if the upper bound of the 95% confidence interval 
for the Type I error probability estimate is below the desired level.  
 
Clinical trial simulations can also be used to estimate power and other relevant operating 
characteristics, such as expected sample size, expected duration, and bias in treatment effect 
estimates, for complex adaptive designs. Similar considerations apply to these estimates as to 
Type I error probability estimates. The level of precision expected for Type I error probability 
estimates, however, is generally not needed for other operating characteristics, so it is usually 
appropriate to investigate a sparser set of scenarios using smaller numbers of iterations for power 
and other operating characteristics. 
 
B.  Bayesian Adaptive Designs 
 
The term Bayesian adaptive design has been used to refer to a wide variety of clinical trial 
designs that use Bayesian statistical reasoning and/or calculations in various ways (Berry, et al. 
2010). Some examples of Bayesian adaptive design features are: 
 
  Use of predictive statistical modeling, possibly incorporating information external to a trial, 
to govern the timing and decision rules for interim analyses 
 
  Use of assumed dose-response relationships to govern dose escalation and selection 
 
  Explicit borrowing of information from external sources, e.g., previous trials, natural history 
studies, and registries, via informative prior distributions to improve the efficiency of a trial 
 
  Use of posterior probability distributions to form trial success criteria 
 
In general, the same principles apply to Bayesian adaptive designs as to adaptive designs without 
Bayesian features. Trial designs that use Bayesian adaptive features may rely on frequentist or 
Bayesian inferential procedures to support conclusions of drug effectiveness. Frequentist 
inference is characterized by hypothesis tests performed with known power and Type I error 
probabilities and is often used along with Bayesian computational techniques that rely on non-
informative prior distributions. Bayesian inference is characterized by drawing conclusions 
based directly on posterior probabilities that a drug is effective and has important differences 
from frequentist inference (Berger and Wolpert 1988). For trials that use Bayesian inference with 
informative prior distributions, such as trials that explicitly borrow external information, 
Bayesian statistical properties are more informative than Type I error probability. FDAs draft 
guidance for industry Interacting with the FDA on Complex Innovative Clinical Trial Designs 
for Drugs and Biological Products (September 2019) provides recommendations on what 
information should be submitted to FDA to facilitate the review of trial design proposals that use 
Bayesian inference.  
 
  20  Contains Nonbinding Recommendations 
 
One common feature of many Bayesian adaptive designs is the use of simulations (section VI.A.) 
to estimate trial operating characteristics. Because many Bayesian methods themselves rely on 
extensive computations (Markov chain Monte Carlo (MCMC) methods and other techniques), 
trial simulations can be particularly resource-intensive for Bayesian adaptive designs.  
 
C.  Adaptations in Time-to-Event Settings 
 
There are certain additional considerations specific to adaptive trials in which the primary 
endpoint is the time to occurrence of a certain event, such as time to death or time to tumor 
response. In these trials, power is dependent on the number of events rather than the number of 
subjects. It is therefore common to target a fixed number of events rather than a fixed number of 
subjects. Sample size adjustment in these trials has the purpose of modifying the number of 
events and, therefore, may take the form of modifying the number of subjects, the length of the 
follow-up period for each subject, or both. In addition, interim analyses in time-to-event settings 
may utilize information on surrogate or intermediate outcomes, and use of such approaches 
should be appropriately accounted for in the analysis (see next section for further discussion). 
 
D.  Adaptations Based on a Potential Surrogate or Intermediate Endpoint 
 
Most adaptive designs rely on ongoing monitoring of the primary endpoint or endpoints. 
However, in cases where a potential surrogate or intermediate endpoint23 exists that is correlated 
with the primary endpoint, and the primary endpoint itself is difficult or slow to ascertain, an 
adaptive design can be based on the potential surrogate or intermediate endpoint. For example, 
consider a trial of a treatment for a cancer where the primary endpoint is overall survival, median 
survival time is well over 2 years, and tumor response (e.g., complete or partial response) may be 
anticipated to predict clinical benefit. In this case, it may be sensible to base sample size 
reassessment or other adaptive features on tumor response rather than mortality. The final 
evaluation of efficacy would still be based on the primary endpoint (overall survival in this 
example). Similarly, an adaptive design could be based on a 2-month measurement of patient 
symptoms when the primary endpoint is the assessment of the same symptom outcome at 6 
months. Some approaches involve assumptions about the relationship between the potential 
surrogate or intermediate endpoint and the primary endpoint, and any evaluation of Type I error 
probability or other trial operating characteristics should consider the possible effects of 
misspecification of this relationship. Other approaches do not rely on assumptions about the 
relationship between the potential surrogate or intermediate endpoint and the primary endpoint 
(Jenkins et al. 2011; Irle et al. 2012; Magirr et al. 2016). 
 
In adaptive design trials with time-to-event or longitudinal outcomes, using surrogate or 
intermediate outcome information at the interim analysis can increase the chance of an erroneous 
conclusion of effectiveness unless appropriate statistical analysis techniques are used. For 
example, it has been noted (Bauer and Posch 2004) that in trials with time-to-event endpoints, 
using surrogate information at the time of an interim analysis from subjects for whom events 
                                                 
23 For the purposes of this guidance, a potential surrogate endpoint refers to an endpoint that may be a candidate 
surrogate endpoint, a reasonably likely surrogate endpoint, or a validated surrogate endpoint, and an intermediate 
endpoint refers to an intermediate clinical endpoint. See The Biomarkers, EndpointS, and other Tools (BEST) 
Resource glossary for definitions of these additional terms. 
  21  Contains Nonbinding Recommendations 
 
have not been observed to help predict future event times can lead to Type I error probability 
inflation. Additional safeguards such as limitation of access to comparative interim results and 
prespecification of an adaptation rule that relies on only the primary endpoint can help increase 
confidence that such unplanned approaches were not carried out. See section VII. for additional 
discussion.  
 
E.  Secondary Endpoints 
 
Most clinical trials have one or more secondary endpoints specified in addition to the primary 
endpoint,24 and adaptive designs can have consequences for the analysis of these secondary 
endpoints. Consider group sequential designs: It is widely understood that multiple analyses of 
the primary endpoint can inflate the Type I error probability and lead to biased estimation of 
treatment effects on that endpoint. Less well appreciated, however, is that Type I error 
probability inflation and biased estimation can also apply to any endpoint correlated with the 
primary endpoint (Hung et al. 2007). Most secondary endpoints in clinical trials are correlated 
with the primary endpoint, often very highly correlated. For some designs such as group 
sequential approaches, methods exist to adjust secondary endpoint analyses for the adaptation 
(Glimm et al. 2010). Without such adjustment, appropriate caution should be applied in 
interpreting secondary endpoint results. 
 
F.  Safety Considerations 
 
Although adaptive design clinical trial planning often focuses on outcomes intended to 
demonstrate effectiveness, safety objectives also play a critical role. First, there are cases where 
adaptations are planned on safety rather than efficacy endpoints. One example is early-phase 
dose-ranging trials in oncology that attempt to identify a maximum tolerated dose using the 
CRM or other adaptive techniques. Another example is the Rotavirus Efficacy and Safety Trial 
(REST) that formed a primary basis for the 2006 approval of a rotavirus vaccine, RotaTeq 
(Heyse et al. 2008). REST was a group sequential trial designed to evaluate the risk of 
intussusception, a serious gastrointestinal condition, in up to 100,000 infants, of whom a subset 
was used for an efficacy evaluation. 
 
Second, the acquisition of sufficient safety information to support product approval is usually a 
major concern in trials that adapt on efficacy endpoints. Trials with early stopping for strong 
evidence of effectiveness still need to collect sufficient safety data to allow for a reliable benefit-
risk evaluation of the investigational drug and to inform labeling. For this reason, the size of a 
safety database should be taken into account when planning the number, timing, and stopping 
boundaries of interim analyses. In particular, the timing of interim analyses may be restricted by 
the expectation for a minimum number of patients studied and a minimum length of exposure to 
ensure a reliable safety evaluation.  
 
Finally, it is important to consider whether certain adaptations can potentially put trial subjects at 
unnecessary risk. This can be a concern in particular in early-phase dose-escalation trials. 
                                                 
24 See the FDA draft guidance for industry Multiple Endpoints in Clinical Trials (January 2017) for a discussion of 
general considerations in the evaluation of multiple endpoints in clinical trials. When final, this guidance will 
represent the FDAs current thinking on this topic. 
  22  Contains Nonbinding Recommendations 
 
Adaptation rules that allow for successive cohorts of subjects to receive quickly escalating doses 
could lead to subjects receiving unsafe high doses that would have been avoided by a design with 
more gradual dose-escalation. This is particularly true when there is a possibility for serious 
adverse events with a delayed onset of action of the investigational drug. For this reason, the 
speed of escalation should be considered in choosing a specific adaptation rule in an adaptive 
dose-escalation trial.  
 
G.  Adaptive Design in Early-Phase Exploratory Trials 
 
Exploratory trials in drug development are intended to obtain information on a wide range of 
aspects of drug use that guide later decisions on how best to study a drug (e.g., choices of dose, 
regimen, population, concomitant treatments, or endpoints). There can be a series of separate 
early trials in which different aspects of the drugs effect are sequentially examined or a more 
complex trial attempting to evaluate multiple different aspects simultaneously. The flexibilities 
offered by adaptive designs may be particularly useful in this exploratory period of development 
by allowing initial evaluation of a broad range of choices. Using adaptive designs in early 
development trials to learn about various aspects of dosing, exposure, pharmacodynamics, 
variability in patient response, or response modifiers offers sponsors opportunities that can 
improve the designs and possibly the chances of success of later-phase trials.  
 
Although exploratory trials do not generally have the same regulatory expectations as trials 
intended to provide substantial evidence of effectiveness in terms of statistical rigor and 
operating characteristics, it is still important to be aware of the potential for erroneous 
conclusions in exploratory trials. For example, flaws in an exploratory multiple-dose comparison 
trial could lead to suboptimal dose selection for a subsequent confirmatory trial, with a resultant 
failure to show effectiveness or a finding of unnecessarily excessive toxicity. Thus, following 
good principles of adaptive trial design for exploratory trials can decrease the risk of adversely 
affecting the development program. 
 
H.  Unplanned Design Changes Based on Comparative Interim Results 
 
When trial data are examined in a comparative interim analysis, data analyses that were not 
prospectively planned as the basis for adaptations may unexpectedly appear to indicate that some 
specific design change (e.g., restricting analyses to some population subset, dropping a treatment 
arm, adjusting sample size, modifying the primary endpoint, or changing analysis methods) is 
ethically important or might increase the potential for a statistically significant final trial result. 
For example, unexpected lack of treatment adherence in one arm of a multiple-arm trial might 
motivate dropping that treatment arm. Such revisions based on non-prospectively planned 
analyses can create difficulty in controlling the Type I error probability and in interpreting the 
trial results. Sponsors are strongly discouraged from implementing such changes without first 
meeting with FDA to discuss the changes being considered, provided patient safety is not 
compromised. 
 
 
 
  23  Contains Nonbinding Recommendations 
 
I.  Design Changes Based on Information From a Source External to the Trial 
 
Unpredictable events that occur outside of an ongoing trial during the course of drug 
development programs may provide important new information relevant to the ongoing trial and 
may motivate revisions to the trial design. For example, there may be unexpected safety 
information arising from a different study (perhaps in a different patient population), new 
information regarding the disease pathophysiology or patient characterization that identifies 
disease subtypes, new information on pharmacokinetics or pharmacodynamic responses to the 
drug, or other information that might have led to a different trial design had the information been 
known when the trial was designed. When this occurs, there may be reason to revise the trial 
design in some manner rather than, for example, terminating the existing trial and starting a new 
trial with a modified design. In cases of serious safety concerns, and particularly in large trials, 
revising the trial design may be critical to allowing the trial to continue. Well-motivated design 
changes based on only information external to the trial do not affect the validity of statistical 
inference and will often be considered acceptable to the Agency. Practically, it is very 
challenging to ensure that a decision to modify a trial was based entirely on external information 
except in cases where the sponsor is completely blinded to comparative interim results. This is 
one reason why limitation of access to comparative interim results is so important. See section 
VII. for additional discussion.  
 
 
VII.  MAINTAINING TRIAL INTEGRITY 
 
In general, it is strongly recommended that access to comparative interim results be limited to 
individuals with relevant expertise who are independent of the personnel involved in conducting 
or managing the trial and have a need to know. Ensuring that patients, investigators and their 
staff, and sponsor personnel do not have access to comparative interim results serves two 
important purposes. First, it provides the greatest confidence that potential unplanned design 
modifications are not motivated in any way by accumulating data. For example, knowledge of 
comparative interim results by trial management personnel may make it difficult for regulators to 
determine whether a protocol amendment seemingly well-motivated by information external to 
the trial was influenced, in any way, by access to accumulating comparative data. If it is thought 
that design changes may have been influenced by comparative interim results, appropriate 
statistical methods to control the chance of erroneous conclusions and to produce reliable 
estimates may not be known, may be challenging to implement, or may greatly reduce the 
efficiency of the trial.  
 
Second, limitation of access to comparative interim results provides the greatest assurance of 
quality trial conduct. Knowledge of accumulating data by trial investigators can adversely affect 
patient accrual, adherence, retention, or endpoint assessment, compromising the ability of the 
trial to reliably achieve its objective in a timely manner (Fleming et al. 2008). Issues with trial 
conduct are difficult to predict and generally impossible to adjust for in statistical analyses. 
Therefore, a clinical trial with an adaptive design should include rigorous planning, careful 
implementation, and comprehensive documentation of approaches taken to maintain 
confidentiality of comparative interim results and to preserve trial integrity.  
 
  24  Contains Nonbinding Recommendations 
 
There are multiple potential models for implementing a plan for the sponsor to limit access to 
comparative data in an adaptive design trial. A dedicated independent adaptation body could be 
established, exclusive of a DMC, if one exists. Alternatively, the adaptive decision-making role 
could be assigned to the DMC, although its primary responsibility should remain to ensure 
patient safety and trial integrity.25 This latter model might best be reserved for group sequential 
designs and other straightforward adaptive designs with simple adaptation algorithms. There are 
arguments favoring both approaches. For example, use of separate bodies might facilitate the 
inclusion of more relevant expertise on each committee and allow the DMC to most effectively 
focus on its primary responsibilities. On the other hand, use of a single body such as a DMC for 
both purposes avoids the logistical challenges of determining information sharing with and 
interactions between multiple monitoring groups.  
 
Regardless of the approach chosen, the committee tasked with making adaptation 
recommendations should have members with the proper expertise, including a statistician or 
statisticians who are knowledgeable about the adaptation methodology, the data monitoring plan, 
and the decision rules. Furthermore, the responsibility of this committee should be to make 
adaptation recommendations or decisions based on appropriately implementing a carefully 
designed and prespecified adaptation plan, not to identify potential design aspects to adapt after 
reviewing comparative interim results. Therefore, it is important for the DMC and/or adaptation 
committee to be involved at the design stage in extensive discussions with the sponsor about 
hypothetical scenarios and whether actions dictated by the adaptation plan would be considered 
reasonable by all involved parties.  
 
Safeguards should be in place to ensure that the persons responsible for preparing and reporting 
interim analysis results to the DMC or the adaptation committee are physically and logistically 
separated from the personnel tasked with managing and conducting the trial, whether those 
personnel reside within the sponsor organization, another organization such as a contract 
research organization (CRO), or both. This practice will help ensure that persons involved in the 
day-to-day management and conduct of the trial do not have access to treatment assignments or 
comparative results, even inadvertently. Similarly, recommendations from the DMC or 
adaptation committee back to the sponsor should generally exclude any details of the interim 
analysis results for the reasons cited above.  
 
Although it is generally recommended that no sponsor representatives have access to 
comparative interim results, there are situations where limited access for specific sponsor 
personnel can be justified. For example, some adaptive trials may involve decisions, such as dose 
selection, that are typically the responsibility of the sponsor in non-adaptive settings and have 
important long-term implications for the drug development program. Limited access by sponsor 
personnel might be justifiable in such circumstances; for example, if a small number of sponsor 
representatives are involved, the individuals allowed access are not otherwise involved in trial 
conduct or management, and appropriate procedures are put in place to ensure that comparative 
interim results remain unknown to other key parties, such as patients, investigators, and the trial 
steering committee. However, risks to trial integrity are most easily minimized by completely 
                                                 
25 See the FDA guidance for clinical trial sponsors Establishment and Operation of Clinical Trial Data Monitoring 
Committees (March 2006) for a detailed discussion of the roles, responsibilities, and operating procedures of DMCs 
in clinical trials. 
  25  Contains Nonbinding Recommendations 
 
restricting sponsor access to comparative interim results, and this is likely to be achievable in 
most circumstances through extensive planning and discussion between the sponsor and the 
DMC or adaptation committee at the design stage. 
 
Appropriate limitation of access entails carefully planned procedures to maintain and verify 
confidentiality, as well as documentation of monitoring and adherence to the operating 
procedures. Approaches typically include the use of confidentiality agreements for persons with 
access to interim data; the use of logistical or physical firewalls that prevent access by trial 
personnel to any data that include information that might allow one to infer treatment 
assignment; and development and use of a data access plan that identifies who has access to 
confidential data, when that access occurs, and what types of data and results are involved. 
Important documentation is discussed in more detail in section VIII.  
 
There is also potential in adaptive trials for knowledge of the adaptation decision to convey 
information about the interim results. Knowledge of a sample size modification algorithm and 
the adaptively chosen sample size, for example, can allow back-calculation of the interim 
estimate of the treatment effect. Therefore, steps should be taken where possible to minimize the 
information that can be inferred by observers. Prespecification of the adaptation rule remains 
critical, although the protocol could perhaps outline only the general approach, with details on 
the specific algorithm reserved for documents such as the DMC charter or adaptive design 
charter that are made available to fewer individuals. Careful consideration and planning with 
respect to the extent of information that is disseminated following an interim analysis is also 
important. In general, investigators and trial participants should be shielded as much as possible 
from knowledge of adaptive changes. For example, if the sample size is increased after an 
interim analysis, trial sites could be informed that the targeted enrollment number has not been 
reached rather than being notified of the specific targeted final sample size. The use of a 
discretized rather than a continuous adaptation decision threshold is another possible approach to 
limit the knowledge that can be inferred to help minimize risks to trial integrity.  
 
 
VIII.  REGULATORY CONSIDERATIONS 
 
A.  Interactions With FDA 
 
The purpose and nature of interactions between a trial sponsor and FDA vary depending on the 
stage of development. The increased complexity of some adaptive trials and uncertainties 
regarding their operating characteristics may warrant earlier and more extensive interactions than 
usual. Early in the development of a drug, FDAs review of a trial protocol typically focuses on 
the safety of trial participants rather than the validity of inference about pharmacologic activity 
or efficacy. However, as resources allow, FDA might review exploratory protocols to consider 
the relevance of the information being gathered to guide the design of later trials. Sponsors who 
have questions about adaptive design elements in an early-phase exploratory trial should seek 
FDA feedback by requesting a meeting (or written responses only) addressing those questions. 
For example, discussion of the plans for an adaptive trial can be the basis for requesting a Type 
C meeting. FDAs ability to address such requests early in development may be limited and will 
depend on competing workload priorities and on the specifics of the development program.  
  26  Contains Nonbinding Recommendations 
 
 
At later phases of development, FDA will have a more extensive role in evaluating the design 
and analysis plan to ensure that the trial will provide sufficiently reliable results to inform a 
regulatory decision. Regulatory mechanisms for obtaining formal, substantive feedback from 
FDA on later stage clinical trials are well-established and include, for example, EOP2 meetings. 
Depending on the preexisting knowledge regarding the drug and its intended use, and the nature 
of the adaptive features, an EOP2 meeting may be the appropriate setting for a sponsor to obtain 
feedback, or earlier interactions with FDA may be advisable (e.g., at a Type C or EOP2A 
meeting). Earlier interactions can help allow time for iterative discussions without slowing 
product development.  
 
FDAs review of complex adaptive designs often involves challenging evaluations of design 
operating characteristics, usually requiring extensive computer simulations, as well as increased 
discussion across disciplines and FDA offices about the evaluations. This may make it difficult 
for FDA to adequately review such designs under short timelines. Given the timelines (45-day 
responses) and commitments involved with special protocol assessments (SPAs), we recommend 
the submission of SPAs for trials with complex adaptive designs only if there has been extensive 
previous discussion between FDA and the sponsor regarding the proposed trial and design.  
 
FDAs review of proposed late-phase adaptive clinical trials will include considerations about 
whether the design and analysis plan satisfy the key principles outlined in this guidance. In 
particular, the sponsor should prespecify the details of the adaptive design and explain how the 
chance of erroneous conclusions will be adequately controlled, estimation of treatment effects 
will be sufficiently reliable, and trial integrity will be appropriately maintained. Furthermore, it is 
good practice for a sponsor to have explored a variety of adaptive and non-adaptive design 
options in planning and to discuss its considerations in choosing the proposed adaptive design 
with the Agency.  
 
Although FDA should be advised during the course of a trial of any proposed unplanned changes 
to the trial design (usually through protocol amendments), the Agency will generally not be 
involved in the prospectively planned adaptive decision-making. This is the responsibility of the 
sponsor, typically through the use of a committee (such as a DMC) designated to implement the 
adaptive design. Meeting minutes from open sessions of a monitoring committee may be 
requested by the Agency during an ongoing trial, but meeting minutes of closed sessions or any 
other communication or information about comparative interim results should be kept 
confidential until the conclusion of the trial, except in unusual circumstances where patients 
safety is at risk. 
 
B.  Documentation Prior to Conducting an Adaptive Trial 
 
To allow for a thorough FDA evaluation, the documented plan for a clinical trial with an 
adaptive design will necessarily be more complex than for a trial with a non-adaptive design. In 
addition to the typical components of a non-adaptive clinical trial protocol and statistical analysis 
plan, such as those discussed in the ICH guidance E9 Statistical Principles for Clinical Trials, 
documentation submitted to the Agency prior to initiation of an adaptive design trial should 
include the following:  
  27  Contains Nonbinding Recommendations 
 
 
  A rationale for the selected design. As discussed in other sections, it is good practice to 
evaluate the important operating characteristics of the proposed design as compared to 
alternative adaptive and non-adaptive designs, and it can be useful to submit such 
information to FDA. However, the ultimate choice of design is the sponsors responsibility. 
 
  A detailed description of the adaptation plan, including the anticipated number and timing of 
interim analyses, the specific aspects of the design that may be modified, and the rule that 
will be used to make adaptation decisions. 
 
  Information on the roles of the bodies responsible for implementing the adaptive design, such 
as the DMC and/or the dedicated adaptation committee, if applicable. 
 
  Prespecification of the statistical methods that will be used to produce interim results, guide 
adaptation decisions, carry out hypothesis tests, estimate treatment effects, and estimate 
uncertainty in the treatment effect estimates at the end of the trial. Software to carry out 
interim and final analyses should be prespecified. If novel or custom software will be used, 
sufficient information should be submitted to FDA before the trial to ensure there is no 
ambiguity in the statistical procedures that will be performed. This information might include 
computer code when applicable. 
 
  Evaluation and discussion of the design operating characteristics, which should typically 
include Type I error probability; power; expected, minimum, and maximum sample size; bias 
of treatment effect estimates; and coverage of confidence intervals. Such evaluations might 
be achieved through analytical calculations and/or computer simulations. If operating 
characteristics are evaluated analytically, appropriate details (e.g., literature references or 
proofs) for the methodology should be submitted.  
 
  In cases where simulations are the primary or sole technique for evaluating trial operating 
characteristics as defined above, a detailed simulation report should be submitted, including: 
 
  An overall description of the trial design. 
o
 
  Example trials, in which a small number of hypothetical trials are described with 
o
different conclusions, such as a positive trial with the original sample size, a trial 
stopped for futility after the first interim look, a positive trial after increasing the 
sample size, etc. 
 
  A description of the set of parameter configurations used for the simulation scenarios, 
o
including a justification of the adequacy of the choices. 
 
  The number of simulated trials (iterations) evaluated for each scenario and a rationale 
o
for the adequacy of this number. 
 
  Simulation results detailing the estimated operating characteristics under the various 
o
scenarios. 
  28  Contains Nonbinding Recommendations 
 
 
  Simulation code. Because FDA reviewers will need to verify simulation studies used 
o
to evaluate trial operating characteristics, it is important to document the software 
package used for simulations and, if custom software was used, to provide the code 
used for the simulations. When code is provided, it should be readable and adequately 
commented. The code should include the random seeds used to generate the 
simulation results. It is also helpful to provide code written in widely-used statistical 
programming languages. Even in cases where another language has been used to 
generate simulation results (typically for reasons of computational efficiency), it can 
be helpful to provide a runnable version of the code in a widely-used statistical 
programming language to facilitate the simulation review. In some cases, it will be 
important to include additional detailed information, such as formulas and 
instructions for use of simulation code. 
 
  A summary providing overall conclusions.  
o
 
  A comprehensive written data access plan defining how trial integrity will be maintained in 
the presence of the planned adaptations. This documentation should include information 
regarding: (1) the personnel who will perform the interim analyses; (2) the personnel who 
will have access to interim results; (3) how that access will be controlled; (4) how adaptive 
decisions will be made; and (5) what type of information will be disseminated following 
adaptive decisions, and to whom it will be disseminated. The data access plan should 
describe what information, under what circumstances, is permitted to be passed on to the 
sponsor or investigators. In addition, it is recommended that sponsors establish procedures to 
evaluate compliance with the data access plan and to document all interim meetings of the 
committee tasked with making adaptation decisions (i.e., the DMC or adaptation committee). 
For example, interim meetings should be documented with written meeting minutes 
describing what was reviewed, discussed, and decided.   
   
This written documentation could be included in the clinical trial protocol and/or in separate 
documents such as a statistical analysis plan, a DMC charter, or an adaptation committee charter. 
Although different types of information might be included in different documents, all important 
information described above should be submitted to FDA during the design stage so that the 
review division has sufficient time to provide feedback prior to initiation of the trial. 
 
C.  Evaluating and Reporting a Completed Trial 
 
A marketing application to FDA that relies on a trial with an adaptive design should include 
sufficient information and documentation to allow FDA to thoroughly review the results. In 
particular, in addition to the typical content of an NDA or a BLA,26 the application should 
include the following: 
 
                                                 
26 See, for example, the FDA draft guidance for industry Providing Regulatory Submissions in Electronic Format  
Certain Human Pharmaceutical Product Applications and Related Submissions Using the eCTD Specifications (July 
2019). When final, this guidance will represent the FDAs current thinking on this topic. 
  29  Contains Nonbinding Recommendations 
 
  All prospective plans, any relevant committee charters (e.g., the DMC or adaptation 
committee charter), and any supporting documentation, as described above (e.g., literature 
references, programming code, and a simulation report). 
 
  Information on compliance with the planned adaptation rule and compliance with the 
procedures outlined in the data access plan to maintain trial integrity.  
 
  Records of deliberations and participants for any interim discussions by any committees 
involved in the adaptive process (e.g., meeting minutes from closed and open DMC or 
adaptation committee meetings, meeting minutes from steering or executive committee 
meetings). 
 
  Results of the interim analysis or analyses used for the adaptation decisions. 
 
  Appropriate reporting of the adaptive design and trial results in section 14 of the proposed 
package insert. For example, the trial summary should describe the adaptive design utilized. 
In addition, treatment effect estimates should adequately take the design into account, or if 
naive estimates such as unadjusted sample means are used, the extent of bias should be 
evaluated, and estimates should be presented with appropriate cautions regarding their 
interpretation. 
 
More limited information (e.g., reports without the database copies and less detailed information 
on other aspects) may be sufficient for trial summaries provided to FDA during the course of 
development to support ongoing discussions within an IND.  
 
 
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  30  Contains Nonbinding Recommendations 
 
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  31  Contains Nonbinding Recommendations 
 
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  33