Interacting with the FDA on Complex 
Innovative Trial Designs for Drugs 
and Biological Products 
 
 
Guidance for Industry 
 
 
 
 
 
 
 
 
 
Additional copies of this guidance are available from the Office of Communication, Outreach 
and Development (OCOD), 10903 New Hampshire Ave., Bldg. 71, Rm. 3128, Silver Spring, 
MD 20993-0002, or by calling 1-800-835-4709 or 240-402-8010, or email ocod@fda.hhs.gov, or 
from the Internet at https://www.fda.gov/vaccines-blood-biologics/guidance-compliance-
regulatory-information-biologics/biologics-guidances. 
 
For questions on the content of this guidance, contact OCOD at the phone numbers or email 
address listed above. 
 
For questions about this document concerning products regulated by Center for Drug Evaluation 
and Research (CDER), contact Scott N. Goldie at 301-796-2055, or email 
druginfo@fda.hhs.gov. 
 
 
 
 
 
 
U.S. Department of Health and Human Services 
Food and Drug Administration 
Center for Biologics Evaluation and Research 
Center for Drug Evaluation and Research 
December 2020 
   
Interacting with the FDA on Complex 
Innovative Trial Designs for Drugs and 
Biological Products 
______________________________________________________________________________ 
Guidance for Industry 
 
 
Additional copies of this guidance are available from: 
 
Office of Communication, Outreach and Development 
Center for Biologics Evaluation and Research 
Food and Drug Administration 
10903 New Hampshire Avenue, WO71, Room 3128  
Silver Spring, MD 20993 
Phone:  800-835-4709 or 240-402-8010 
E-mail:  ocod@fda.hhs.gov  
https://www.fda.gov/vaccines-blood-biologics/guidance-compliance-regulatory-information-
biologics/biologics-guidances 
 
or 
 
Office of Communications  
Division of Drug Information 
Center for Drug Evaluation and Research 
Food and Drug Administration 
10001 New Hampshire Ave., Hillandale Bldg., 4th Floor 
Silver Spring, MD 20993 
Phone:  301-796-3400 or 855-543-3784; Fax:  301-431-6353  
E-mail:  druginfo@fda.hhs.gov 
https://www.fda.gov/drugs/guidance-compliance-regulatory-information/guidances-drugs 
 
 
  Contains Nonbinding Recommendations 
 
Table of Contents 
 
I.  INTRODUCTION............................................................................................................. 1 
II.  SCOPE ............................................................................................................................... 2 
III.  INTERACTING WITH FDA ON CID PROPOSALS .................................................. 2 
A.  Meeting Availability.............................................................................................. 2 
B.  Recommended Common Elements of CID Proposals ....................................... 3 
C.  Recommended Elements of Bayesian CID Proposals ........................................ 4 
1.  Prior Distributions ............................................................................................. 5 
2.  Decision Criteria ............................................................................................... 6 
D.  Simulations ............................................................................................................ 6 
IV.  EXAMPLES OF CID AND INFORMATION RECOMMENDED TO 
FACILITATE INTERACTIONS ................................................................................................ 7 
A.  Master Protocols ................................................................................................... 7 
B.  Leveraging Data From Phase 2 to Phase 3 ......................................................... 7 
C.  Sequential Multiple Assignment Randomized Trials (SMARTs) .................... 8 
V.  REFERENCES .................................................................................................................. 9 
 
i 
  Contains Nonbinding Recommendations 
 
Interacting with the FDA on Complex Innovative Clinical Trial 
Designs For Drugs and Biological Products 
 
 
Guidance for Industry 
 
 
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 
 
This document provides guidance to sponsors and applicants on interacting with the FDA on 
complex innovative trial design (CID) proposals for drugs or biological products.1  FDA is 
issuing this guidance to satisfy, in part, a mandate under section 3021 of the 21st Century Cures 
Act (Cures Act).  In accordance with the Cures Act mandate, this guidance discusses the use of 
novel trial designs in the development and regulatory review of drugs and biological products, 
how sponsors may obtain feedback on technical issues related to modeling and simulation, and 
the types of quantitative and qualitative information that should be submitted for review.  
Additional recommendations related to the mandate set forth in section 3021 of the Cures Act are 
addressed in FDAs guidance on Adaptive Designs for Clinical Trials of Drugs and Biologics 
(Ref. 1).2  This guidance finalizes the draft guidance of the same title dated September 2019. 
 
FDAs guidance documents, including this guidance, do not establish legally enforceable 
responsibilities.  Instead, guidances describe the FDAs 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 FDAs guidances means that something is suggested or 
recommended, but not required. 
   
                                                 
1 The term drug as used in this guidance refers to human drugs, including drugs that are biological products, unless 
otherwise specified. 
2 See also Guidance for Industry:  Enrichment Strategies for Clinical Trials to Support Determination of 
Effectiveness of Human Drugs and Biological Products, March 2019, 
https://www.fda.gov/media/121320/download/.  
1 
  Contains Nonbinding Recommendations 
 
II.  SCOPE 
 
Although CID has been considered to refer to complex adaptive, Bayesian, and other novel 
clinical trial designs, there is no fixed definition of CID because what is considered innovative or 
novel can change over time.  For the purposes of this guidance, CID includes trial designs that 
have rarely or never been used to date to provide substantial evidence of effectiveness in new 
drug applications or biologics license applications.  CID can also include the novel application of 
complex trial design features to a given indication even when those design features have been 
used in other indications.  A common feature of many CIDs is the need for simulations rather 
than mathematical formulae to estimate trial operating characteristics (section III of this 
guidance).  Some examples of trial designs that might be considered novel or CID are discussed 
in section IV of this guidance, including those that formally borrow external or historical 
information or borrow control arm data from previous studies to expand upon concurrent 
controls, Sequential Multiple Assignment Randomized Trials, or master protocols.  
 
Although complex innovative designs can be applied at all phases of clinical development, the 
primary focus of this guidance is on FDA and sponsor interactions for CID proposals for trials 
intended to provide substantial evidence of effectiveness.  In most cases, interactions related to 
CID proposals will occur in the context of investigational new drug applications (INDs).  Novel 
clinical trial designs call for clear communication between sponsors and FDA on aspects of the 
design, including the purpose, execution, and operating characteristics (such as the chance of 
producing erroneous conclusions) of the design, and how the trial data will be analyzed and 
presented.  This guidance provides recommendations for such interaction.  The guidance also 
provides examples of clinical trial designs that FDA might consider to be CID and describes the 
type of information FDA recommends submitting with the proposals to facilitate a productive 
discussion between sponsors and FDA.  Additionally, the guidance addresses the role of 
simulations in clinical trial design and planning. 
 
This guidance does not indicate whether specific novel designs are or are not appropriate for 
regulatory use, as such determinations are made on a case-by-case basis depending on the 
reasons the design is being proposed, its validity in the specific setting, and possibly on factors 
unique to a given development program.  A CID proposal that may be appropriate for one 
product class in one indication may not be appropriate for another product class or in another 
indication.  The emphasis in this guidance is on the recommended elements for effective 
interactions between sponsors and FDA regarding CID proposals. 
 
 
III.  INTERACTING WITH FDA ON CID PROPOSALS 
 
A.  Meeting Availability 
 
Because CID proposals may involve novel scientific review considerations, FDA 
encourages sponsors of CID proposals to seek early interaction with FDA regarding 
details of their CID plans.  In general, sponsors should use existing pathways for 
interacting with FDA during the course of the clinical development program, including 
2  Contains Nonbinding Recommendations 
routine feedback on IND amendment submissions as well as formal meetings such as 
Type B End of Phase 2 meetings, Type C meetings, and possibly pre-IND meetings for 
early-phase studies with novel design elements (Ref. 2).  FDAs review of CID proposals 
often involves challenging evaluations of design operating characteristics, including 
extensive computer simulations, as well as detailed discussions across disciplines and 
FDA offices.  To facilitate FDA review of such designs, consideration should be given to 
the amount of time available for reviewing meeting packages.  For example, an End of 
Phase 2 meeting could include discussion of an overall strategy for the use of a CID in 
Phase 3, whereas a Type C meeting would be more appropriate for discussion of 
statistical details such as simulation plans. 
 
When the CID proposal involves extensive computer simulations to determine important 
aspects of the design, FDA may grant additional Type C meetings to provide more 
detailed feedback.  Sponsors are encouraged to indicate within meeting packages how the 
CID proposal fits into the overall clinical development program and in what ways it may 
improve the efficiency of the program or study, or the generalizability of its results 
compared to a simpler or conventional clinical trial design. 
 
Sponsors may also consider FDAs pilot program for complex innovative trial designs 
(CID Pilot Program) to obtain additional meetings with FDA review disciplines on their 
proposed CID (Ref. 3).  The CID Pilot Program provides sponsors participating in the 
program with additional opportunities to meet with FDA about their CID proposal and to 
obtain detailed feedback from review teams and senior regulatory decision-makers.  The 
program will run from August 30, 2018, through September 30, 2022, and is intended to 
advance the use of CIDs.  More details about applying to participate in the CID Pilot 
Program and the overall process for the pilot program meetings are available on FDAs 
webpage (Ref. 4). 
 
B.  Recommended Common Elements of CID Proposals  
 
Detailed documentation is important for FDA to review novel design proposals 
thoroughly and provide feedback.  Documentation should include the novel features that 
are planned to be incorporated, the timing and details of the planned implementation, and 
how the design addresses the underlying scientific objectives.  The specific 
documentation depends on the type of proposal submitted, but there are certain common 
elements that should be included in most proposals.  In this section, we give examples of 
common elements that should be included in a CID proposal, whenever applicable.  In 
section IV of this guidance, we give examples of additional information that may be 
important for specific types of CID proposals.  
 
  A discussion of the choice of trial design and how it fits into the overall drug 
development plan.  It is often helpful to explain how the novel design provides 
advantages over conventional trial designs for the particular product and 
indication.  This explanation may include a detailed comparison of operating 
characteristics and/or a descriptive explanation of how the proposed design meets 
the goals of the development program better than relevant conventional designs. 
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  A detailed description of important aspects of the design, including plans for any 
possible adaptations, implementation details for interim analyses, and decision 
criteria.  
  A discussion of the statistical considerations related to the complex innovative 
design features. 
  If prior information is being formally borrowed, details about the source and 
choice of the prior information, its relevance to the proposed trial design, and an 
explanation of steps taken to ensure that all relevant prior information is 
accounted for, so that the prior information formally borrowed does not lead to 
misleading results. 
  When external information is explicitly borrowed into a design, such as in a 
Bayesian framework, a rationale for the borrowing and an explanation of how the 
prior distributions were constructed from the prior information. 
  A detailed evaluation of the operating characteristics of the design, including its 
chance of producing erroneous conclusions and the reliability of treatment effect 
estimates.  Type I error probability control and power should be addressed where 
applicable.  When Type I error probability is not applicable (e.g., some Bayesian 
designs that borrow external information), appropriate alternative trial 
characteristics should be considered. 
  For Bayesian inference, it is informative to assess the sensitivity of trial operating 
characteristics to the choice of a prior distribution.  
  A simulation report if simulations are used to evaluate study operating 
characteristics.  
  Decisions allocated to the Data Monitoring Committee or other body charged 
with implementing critical aspects of the CID, and the instructions attendant to 
those decisions, as applicable and relevant to understand the design. 
  A comprehensive plan for appropriately restricting data access and describing 
how trial integrity will be maintained.  This is discussed in detail in FDAs 
Guidance for Industry on Adaptive Designs for Clinical Trials of Drugs and 
Biologics (Ref. 1). 
  For any trial that uses patient input to inform the study design or analysis (e.g., 
choice of endpoints, acceptable benefit-risk trade-offs, minimum acceptable 
benefit, maximum acceptable risk, or the level of certainty that will be targeted 
for estimates of treatment effects on safety and effectiveness), a study protocol for 
any study used to assess patient input should be submitted for discussion (Ref. 6).  
 
C.  Recommended Elements of Bayesian CID Proposals  
 
Bayesian approaches may be well-suited for some CIDs intended to provide substantial 
evidence of effectiveness because they can provide flexibility in the design and analysis 
of a trial, particularly when complex adaptations and predictive models are used.  In 
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addition, Bayesian inference may be appropriate in settings where it is advantageous to 
systematically combine multiple sources of evidence, such as extrapolation of adult data 
to pediatric populations, or to borrow control data from Phase 2 trials to augment a Phase 
3 trial (see section IV of this guidance).  
 
If a sponsor chooses to submit a Bayesian CID proposal, FDAs evaluation of the 
proposal relies on clear communication between the sponsor and FDA regarding two 
areas: the prior distribution and the study decision criteria for primary and key secondary 
endpoints.  Bayesian approaches that formally borrow information external to a trial may 
not be appropriate for every development program.  When submitting a Bayesian CID 
proposal with borrowing of external information, sponsors should include a rationale for 
the borrowing with specific details regarding how bias was avoided in the selection of the 
borrowed information.  As the focus of this guidance is on interactions with FDA 
regarding CID, the following sections discuss the elements unique to a Bayesian CID that 
should be included in a Bayesian CID proposal to facilitate discussion between a sponsor 
and FDA. 
 
1.  Prior Distributions 
 
Bayesian inference relies on well-informed specification of the prior distribution 
to control the chance of erroneous conclusions.  Without careful construction of 
the prior distribution, the use of Bayesian methods can increase the chance of 
erroneous conclusions.  As such, discussions regarding the prior distribution are 
particularly important to FDAs evaluation of Bayesian proposals.  Prior 
distributions can be formed from a variety of sources, including previous trial data 
or other clinical data, and they can also incorporate features such as 
downweighting of earlier data relative to contemporaneous data or initial 
skepticism regarding the likelihood of large treatment effects.  In some settings, 
non-informative or reference prior distributions may be used to reflect a stance of 
general uncertainty regarding the parameters of interest.  
 
In general, Bayesian CID proposals should include a detailed discussion of the 
prior distribution.  Any data or other external information used to form the prior 
distribution should be presented in detail for FDA to understand the source and 
completeness of the external information, its relevance, and the quality and 
reliability of the data.  One aspect of the relevance of external data relates to the 
issue of exchangeability, which should be addressed in Bayesian CID proposals.  
The concept of exchangeability is discussed in detail in FDAs Guidance for the 
Use of Bayesian Statistics in Medical Device Clinical Trials (Ref. 5).  In order to 
obtain accurate inference, informative prior distributions should generally be 
based on a thorough evaluation of relevant evidence, including evidence that may 
suggest initial skepticism regarding the existence or magnitude of a treatment 
effect.  For this reason, a Bayesian proposal should also include a discussion 
explaining the steps the sponsor took to ensure information was not selectively 
obtained or used.  In cases where direct downweighting of the historical data or 
5  Contains Nonbinding Recommendations 
other non-data-driven features are incorporated in a prior distribution, the 
proposal should include a rationale for the use and magnitude of these features.  
 
2.  Decision Criteria 
 
For many clinical studies conducted using a frequentist statistical approach, 
hypothesis tests at a one-sided alpha level of 0.025 are used to establish support 
for the efficacy of a product.  When Bayesian approaches are used, it is important 
to specify alternate decision criteria.  The choice of decision criteria has a large 
impact on both the design and the quality of inference from a study.  
There are many possible ways to specify decision criteria.  For example, a study 
protocol may state that a conclusion of effectiveness will be supported if the 
probability that the response rate in Group A is greater than the response rate in 
Group B exceeds 99% (in mathematical notation, Pr(  >  ) > 0.99).  Another 
A B
study protocol may state that a demonstration of effectiveness requires that the 
probability that the response rate in Group A is at least 10 percentage points 
higher than the response rate in Group B exceeds 95% (Pr(  -   > 0.10) > 
A B
0.95). 
Sponsors should propose decision criteria in study protocols for all primary and 
secondary endpoints intended to be included in product labeling if the product is 
approved.  These proposals should include a rationale for the choice of criteria.  
FDA evaluates these proposals during IND review, and final determination is 
based upon agreement with the review division. 
For some Bayesian designs, it is possible to use simulations to estimate the 
frequentist operating characteristics of power and Type I error probability.  In 
these cases, decision criteria can be chosen to provide Type I error control at a 
specified level. 
 
D.  Simulations 
 
A common feature of CID proposals is the use of simulations to estimate trial operating 
characteristics or to optimize design parameters such as number and timing of interim 
analyses.  FDAs Guidance for Industry:  Adaptive Designs for Clinical Trials of Drugs 
and Biologics (Ref. 1) discusses such simulations in some detail in the context of 
adaptive designs specifically, and provides recommendations regarding the content and 
format of simulation reports included with applications.  The same considerations apply 
generally across a wide variety of CID proposals.  
 
For studies intended to provide substantial evidence of effectiveness, it can be 
advantageous to discuss plans for trial simulations at meetings with FDA.  Specifically, 
End of Phase 2 meetings often occur at a stage in the development process where 
preliminary plans for Phase 3 trials can be discussed, and these preliminary plans may 
suggest the need for trial simulations.  Structuring part of the End of Phase 2 discussion 
around these simulations can help a sponsor and FDA consider the scenarios to be 
6  Contains Nonbinding Recommendations 
explored in simulations and the underlying trial assumptions, such as accrual rate and 
likely control group outcomes. 
IV.  EXAMPLES OF CID AND INFORMATION RECOMMENDED TO 
FACILITATE INTERACTIONS 
 
This section includes a discussion of several specific CID elements.  The intent is to give 
examples of some of the recommended information to facilitate a sponsor and FDA having a 
productive interaction regarding these designs in the context of a meeting or application review. 
The examples are not comprehensive; many other CID elements may be proposed, some of 
which may yet be invented.  Furthermore, what is considered CID today could become routine in 
the future. 
 
A.  Master Protocols 
 
Master protocol designs are used in situations where one overarching protocol is designed 
to answer multiple questions (Ref. 7, 8).  One of the trial proposals accepted for review 
under the CID Pilot Program (section III.A.) is a trial designed to evaluate multiple 
interventions across multiple chronic pain conditions.  The primary analysis uses a 
Bayesian mixed-model repeated measures approach to investigate differences in a 
numeric pain scale between active therapy and placebo.  This analysis allows for 
borrowing patient information from the placebo groups within a pain condition and 
borrowing information on the treatment difference across different pain conditions for the 
same active intervention.  In addition, data from external sources could be used to 
develop informative priors on some model parameters.    
Initial points of consideration for this design included potential for drift in the placebo 
response, the assumption of exchangeability among patients with different pain 
conditions, and the impact of missing data, which is frequently encountered in chronic 
pain trials. 
 
B.  Leveraging Data From Phase 2 to Phase 3 
 
A feature common to multiple proposals accepted for review under the CID Pilot 
Program (section III.A.) is leveraging of information from earlier studies into the design 
and analysis of a new trial.  This can include attempts to improve the efficiency of Phase 
3 trials by leveraging control data from Phase 2 trials.  In this scenario, control group 
outcome data from a Phase 2 trial is incorporated in the estimation of treatment 
differences from a subsequent Phase 3 trial.  Use of Phase 2 control data to bolster the 
control group in a Phase 3 trial may lead to reduction in the total Phase 3 sample size. 
 
CID proposals to incorporate Phase 2 data into Phase 3 raise a number of complex issues 
and call for thorough documentation and discussion to ensure productive interactions 
between sponsors and FDA.  Such proposals should include a discussion of the clinical 
comparability of the Phase 2 and expected Phase 3 populations, including similarity of 
7  Contains Nonbinding Recommendations 
study procedures, treatments, and endpoints, and whether there have been any changes in 
standards of care of patients over time that could affect outcome.  The Phase 2 data 
should be presented in enough detail for FDA to evaluate its quality.  It is important that 
the proposal include a landscape assessment to determine whether other data from other 
sources (for example, in the medical literature) are consistent with the Phase 2 data.  A 
risk of borrowing is that if the prior control group has a systematically lower response 
rate than the concurrent control group due to any kind of heterogeneity between Phase 2 
and Phase 3 design or conduct, borrowing the prior data could increase the chance of a 
false conclusion of effectiveness.  For this reason, a strategy for evaluating and 
addressing heterogeneity between the prior data and the concurrent Phase 3 data, such as 
the use of hierarchical models or other approaches that automatically downweight 
borrowing in the presence of heterogeneity, should be included.  As discussed above, if 
Bayesian approaches are used, the proposal should include detailed discussions of 
decision criteria and prior distributions, including, where applicable, the effective sample 
size of the Phase 2 data to be borrowed and how it will be borrowed.  
 
Similar approaches can be applied in borrowing information from sources other than 
Phase 2 studies. Examples from the CID Pilot Program include a randomized, double-
blind, placebo-controlled study of a low and high dose investigational treatment in 
ambulatory patients with Duchenne Muscular Dystrophy, and a randomized, double-
blind, group sequential non-inferiority study comparing an investigational drug to an 
active control in a pediatric multiple sclerosis population.  
 
C.  Sequential Multiple Assignment Randomized Trials (SMARTs)  
 
Sequential Multiple Assignment Randomized Trials (SMARTs) are designed to inform 
the development of adaptive interventions.  An adaptive intervention is a sequence of 
decision rules that specifies when and how the type and/or intensity of a treatment should 
be modified depending on the patients characteristics and/or ongoing performance (e.g., 
response, adherence) to optimize clinically important outcomes.  A SMART is comprised 
of multiple intervention stages, and each stage corresponds to one of the critical decisions 
involved in the adaptive intervention.  In a SMART, patients move along multiple stages 
and are randomly assigned to one of several treatment options at each stage.  
 
Elements that should be discussed and communicated with FDA in SMART proposals or 
similar designs include the statistical questions/hypotheses, number of stages, 
interventions embedded in the design, intermediate response categories, a clear 
illustration of the flow diagram, and methods to adjust for multiplicity if applicable. 
 
 
 
 
   
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V.  REFERENCES  
   
1.  Guidance for Industry:  Adaptive Designs for Clinical Trials of Drugs and Biologics, 
November 2019, https://www.fda.gov/media/78495/download.  
 
2.  Draft Guidance for Industry:  Formal Meetings Between the FDA and Sponsors or 
Applicants of PDUFA Products, December 2017*, 
https://www.fda.gov/media/109951/download. 
 
3.  Food and Drug Administration, Federal Register Notice, Complex Innovative Designs Pilot 
Meeting Program, 83 FR 44274, August 30, 2018, 
https://www.federalregister.gov/documents/2018/08/30/2018-18801/complex-innovative-
designs-pilot-meeting-program. 
 
4.  Complex Innovative Trial Designs Pilot Program, 
https://www.fda.gov/Drugs/DevelopmentApprovalProcess/DevelopmentResources/ucm6172
12.htm. 
 
5.  Guidance for Industry and FDA Staff:  Guidance for the Use of Bayesian Statistics in 
Medical Device Clinical Trials, February 2010, https://www.fda.gov/media/71512/download.  
 
6.  Guidance for Industry, Food and Drug Administration Staff, and Other Stakeholders:  
Patient-Focused Drug Development:  Collecting Comprehensive and Representative Input, 
June 2020,  https://www.fda.gov/media/139088/download. 
 
7.  Woodcock, J and LaVange, L. M. (2017). Master Protocols to Study Multiple Therapies, 
Multiple Diseases, or Both. New England Journal of Medicine, 377: 62  70.  
 
8.  Draft Guidance for Industry:  Master Protocols:  Efficient Clinical Trial Design Strategies To 
Expedite Development of Oncology Drugs and Biologics, October 2018*, 
https://www.fda.gov/media/120721/download. 
 
* When finalized, this guidance will represent FDAs current thinking on this topic. 
 
 
 
 
 
 
 
 
 
 
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