Adaptive Designs for Medical 
Device Clinical Studies 
Guidance for Industry and Food 
and Drug Administration Staff
 
Document issued on July 27, 2016. 
The draft of this document was issued on May 18, 2015. 
For questions regarding this document that relate to devices regulated by CDRH, contact Dr. 
Gerry Gray (CDRH) at 301-796-5750 or by e-mail at Gerry.Gray@fda.hhs.gov.  
For questions regarding this document that relate to devices regulated by CBER, contact the 
Office of Communication, Outreach and Development (CBER) at 1-800-835-4709 or 240-
402-8010. 
 
 
U.S. Department of Health and Human Services 
Food and Drug Administration 
 
Center for Devices and Radiological Health 
 
Center for Biologics Evaluation and Research   Contains Nonbinding Recommendations 
 
 
 
Preface 
 
Public Comment 
You may submit electronic comments and suggestions at any time for Agency consideration to 
http://www.regulations.gov.  Submit written comments to the Division of Dockets 
Management, Food and Drug Administration, 5630 Fishers Lane, Room 1061, (HFA-305), 
Rockville, MD 20852.  Identify all comments with the docket number FDA-2015-D-1439. 
Comments may not be acted upon by the Agency until the document is next revised or 
updated. 
 
Additional Copies 
CDRH 
Additional copies are available from the Internet.  You may also send an e-mail request to 
CDRH-Guidance@fda.hhs.gov to receive an electronic copy of the guidance or send a fax 
request to 301-847-8149 to receive a hard copy.  Please use the document number 
GUD1500005 to identify the guidance you are requesting. 
CBER 
Additional copies are available from the Center for Biologics Evaluation and Research 
(CBER) by written request, Office of Communication, Outreach, and Development (OCOD), 
10903 New Hampshire Ave., WO71, Room 3128, Silver Spring, MD 20903, or by calling 1-
800-835-4709 or 240-402-8010, by email, ocod@fda.hhs.gov, or from the Internet at  
http://www.fda.gov/BiologicsBloodVaccines/GuidanceComplianceRegulatoryInform
ation/Guidances/default.htm.  
 
  Contains Nonbinding Recommendations 
 
 
 
Table of Contents 
1.  Introduction and Scope.....................................................................................................1
2.  What are Adaptive Designs? .............................................................................................2
A. Definition.......................................................................................................................2
B. Planning.........................................................................................................................3
C. Advantages of Adaptive Designs ...................................................................................5
D. Limitations of Adaptive Designs...................................................................................6
E. Adaptive Studies as a Learning Paradigm.....................................................................7
F. Study Design Changes That Are Not Adaptive.............................................................8
3.  When to Choose an Adaptive Design...............................................................................9
A. Is an Adaptive Design Feasible? ....................................................................................9
B. How to Decide an Adaptive Design is Advantageous ................................................. 10
C. Anticipated Regret ....................................................................................................... 11
4.  Principles for Adaptation in the Design of Clinical Studies ........................................... 12
A. Controlling the Chance of Erroneous Conclusions ...................................................... 12
B. Minimization of Operational Bias ............................................................................... 14
5.  Adaptively-Designed Studies without the Need to Break the Blind .............................. 15
6.  Adaptations Using Unblinded Data ................................................................................ 17
A. Group Sequential Designs ........................................................................................... 17
B. Sample Size Reassessment .......................................................................................... 18
C. Bayesian Sample Size Adaptation ............................................................................... 21
D. Group Sequential Designs with Sample Size Reassessment ....................................... 22
E. Dropping a Treatment Arm .......................................................................................... 23
F. Changing the Randomization Ratio ............................................................................. 23
G. Investigating both superiority and non-inferiority ....................................................... 23
H. Adaptive Enrichment ................................................................................................... 24
I.  Planning to Adapt Based on the Total Information ..................................................... 24
J.  Adaptation of the Device or Endpoint ......................................................................... 25
K. Seamless Studies .......................................................................................................... 26
7.  Special Considerations .................................................................................................... 26
A. Changes to Pivotal Clinical Studies that are Not Preplanned Using Blinded Data ..... 26
B. Changes to Pivotal Clinical Studies that are Not Preplanned with Unblinded Data ... 27
C. Simulations in Planning an Adaptive Design .............................................................. 28
D. Adaptive Designs for Safety Endpoints ....................................................................... 29
E. Adaptive Designs for Open-Label Randomized Studies ............................................. 29
F. Adaptive Designs for Observational Comparative Studies ......................................... 30
G. Adaptive Designs for One-Arm Studies without a Control ......................................... 31
8.  Additional Considerations for Diagnostic Devices ........................................................ 31
A. Adaptation to prevalence and the entire disease spectrum .......................................... 32
B. Blinded Sample Size Reassessment Based on Interim Estimates for the Comparator 33
9.  Principles in the Analysis of Data from Adaptive Designs ............................................ 33
A. Bias Control in the Estimates ....................................................................................... 34
B. Homogeneity of Results after a Modification .............................................................. 34
C. No adaptation made ..................................................................................................... 34 Contains Nonbinding Recommendations 
 
 
 
10.  Challenges of Adaptive Studies ...................................................................................... 35
A. Data Monitoring Committees ...................................................................................... 35
B. Techniques to Minimize Operational Bias .................................................................. 36
C. Institutional Review Boards ......................................................................................... 38
D. Logistical Challenges ................................................................................................... 38
11.  Regulatory Considerations .............................................................................................. 39
A. Interactions with FDA ................................................................................................. 39
B. Sponsor Monitoring ..................................................................................................... 40
C. Best Practices to Protect Study Blinding (Masking) ................................................... 41
D. Content of an Adaptive Design Submission to the FDA ............................................. 41
12.  Conclusion ...................................................................................................................... 42
13.  References ....................................................................................................................... 42
  Contains Nonbinding Recommendations 
 
 
 
Adaptive Designs for Medical 
Device Clinical Studies  
 
Guidance for Industry and  
Food and Drug Administration Staff
 
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 or Office responsible for this guidance as listed on the title page.  
1.  Introduction and Scope 
An adaptive design for a medical device clinical study is defined as a clinical study 
design that allows for prospectively planned modifications based on accumulating study data 
without undermining the studys integrity and validity.  Adaptive designs, when properly 
implemented, can reduce resource requirements, decrease time to study completion, and/or 
increase the chance of study success.  This guidance provides sponsors and Food and Drug 
Administration (FDA) staff with guidance on how to plan and implement adaptive designs 
for clinical studies when used in medical device development programs. 
This document addresses adaptive designs for medical device clinical studies and is 
applicable to premarket medical device submissions including Premarket Approval 
Applications (PMA), premarket notification (510(k)) submissions, de novo submissions 
(Evaluation of Automatic Class III Designation), Humanitarian Device Exemption (HDE) 
applications and Investigational Device Exemption (IDE) submissions.  This guidance can be 
applied throughout the clinical development program of a medical device, from feasibility 
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studies to pivotal clinical studies.  This guidance does not apply to clinical studies of 
combination products or co-development of a pharmaceutical product with an 
unapproved/uncleared diagnostic test..  However, the underlying principles may be 
applicable to such studies. 
FDA's guidance documents, including this guidance, do not establish legally enforceable 
responsibilities.  Instead, a guidance document describes the Agency's current thinking on a 
topic and should be viewed only as recommendations, unless specific regulatory or statutory 
requirements are cited.  The use of the word should in Agency guidance means that 
something is suggested or recommended, but not required. 
2.  What are Adaptive Designs? 
A.  Definition  
An adaptive design for a medical device clinical study is defined as a clinical study 
design that allows for prospectively planned modifications based on accumulating study data 
without undermining the studys integrity and validity.1  In nearly all situations, in order to 
preserve the integrity and validity of a study, modifications should be prospectively planned 
and described in the clinical study protocol prior to initiation of the study. However, in some 
specific circumstances, modifications to the design after the study begins can be 
scientifically valid if they are made without knowledge of the outcome results by treatment.2  
The different types of adaptive study design modifications (e.g., changes to the study design, 
study conduct, statistical hypotheses or analysis), as well as their advantages and limitations, 
are discussed in Section 6. 
                                                 
 
 
1 For the purposes of this definition, integrity refers to the credibility of the results and validity refers to being 
able to make scientifically sound inferences. 
2 Knowledge of outcome results by coded treatment groups (e.g., outcomes known for treatments A and B), 
even without divulging which treatment is investigational, can undermine scientific validity. 
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B.  Planning 
A sound clinical study requires extensive planning, with consideration given to all 
elements of the study, from design to a plan for data analysis. Adaptive study design 
planning  focuses on anticipated changes that may be desirable based on the data that will be 
accumulating during the course of the study.  With adequate preplanning, a sponsor can use 
the studys accumulating data to modify various aspects of the study in a scientifically-valid 
manner. 
  However, there is a real danger that an unplanned modification to the study may 
weaken its scientific validity and therefore the study might not be adequate to support a 
future marketing application.  Sponsors should anticipate and plan for modifications based 
on a variety of possible scenarios that could occur during the course of the study. 
The following examples of adaptive modifications highlight some of the advantages 
of prospectively-planned adaptive study designs.   
Example 1 - A sponsor conducted a randomized trial of a novel bone graft device designed to 
demonstrate non-inferiority of their device when compared to an autologous bone graft.  That 
is, the study was designed to show that the novel device was no more than delta worse than 
the autologous graft, where delta is a pre-specified clinically determined margin.  The 
study design assumed that fusion rates would be the same for the two treatments. An 
optional, prospectively planned, interim analysis to assess aggregate fusion outcomes 
(blinded (masked) by treatment group) was included in the study design to permit adjustment 
of the sample size, if necessary. 
Example 2 - A randomized trial was conducted to demonstrate non-inferiority of an artificial 
cervical disc to the standard of care of anterior cervical discectomy and fusion.  Although the 
study was sized for 500 patients, a planned interim look when subject number 340 reached 
the 24-month follow up demonstrated success.  The PMA was submitted to the FDA and 
approved based on this smaller data set.  This is referred to as group sequential design and, 
in many instances, has led to shorter and smaller trials.  See Section 6.A. for more details. 
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Example 3 - A sponsor conducted a randomized two-arm unblinded study comparing a 
wound-healing device to the standard of care with a primary endpoint of time to drain 
removal.  At study initiation, there was uncertainty about the variability in the estimated 
difference in mean time to drain removal (i.e., the standard error of the difference), so the 
sponsor chose to design the study to proceed until the estimated standard error for the 
difference in mean time to drain removal reached a certain agreed-upon threshold.  As a 
result, the study needed to be conducted only until the pre-determined amount of information 
was acquired..  A similar approach could be taken in a study with a performance goal where 
the standard deviation is not known at the outset.    
Example 4  A sponsor conducts a diagnostic clinical study, in which the sponsor compares 
their investigational imaging system for detecting solid tumors, using an already-approved 
device as a comparator plus a reference standard.  The goal is to establish non-inferiority to 
the already approved device. The primary comparisons will involve both sensitivity and 
specificity of the new device in comparison to the old device.  The ability to anticipate 
sample size is limited, because it will depend on whether both new and old devices 
misclassify the same cases or different ones, as well as prevalence of the target condition of 
interest (a tumor present or absent).  So an interim analysis can be used to determine if (a) a 
stop for futility is warranted or (b) additional subjects need to be enrolled.   
Example 5  A sponsor designs a group sequential trial for a surgical device so that at each 
interim analysis there are separate tests for effectiveness in diabetics and non-diabetics as 
well as overall.  At one interim analysis, according to pre-specified rules, the device does not 
appear to be effective in diabetic patients.  Further enrollment into the study is restricted to 
non-diabetic patients and the device gains approval for non-diabetic patients only. This is 
referred to as adaptive enrichment and is discussed further in Section 6.H. 
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Adaptive designs can use either frequentist or Bayesian statistical methodology.  However, 
underlying principles discussed in this guidance regarding issues such as bias control, 
blinding, and operating characteristics of the study design are the same regardless of the 
statistical methods used. 
C.  Advantages of Adaptive Designs 
An adaptive study design can have several distinct advantages when compared to a fixed 
(non-adaptive) design.   
  It can be more efficient, saving time, money, and resources.  This can occur in several 
ways.  A trial with interim analyses could stop early for effectiveness in a preplanned 
way.  A trial with two or more investigational arms could plan to drop one of them 
based on accumulating data.  A single or multi-arm trial with a preplanned interim 
analysis could be stopped early for futility.   
  Adaptive designs can improve the chance of trial success by employing sample size 
reassessment (SSR), whether for a superiority or a non-inferiority study.  Based on 
accumulating data in the trial, planned SSR could lead to an adjustment in sample 
size, converting an underpowered study likely to fail into a study more likely to 
succeed.  This approach allows for mid-course correction in trials where there is a 
treatment effect that is meaningful but smaller than originally anticipated.  This  
facilitates the timely assessment and marketing of medical devices demonstrating a 
reasonable assurance of safety and effectiveness.   
  Adaptive design may facilitate transition from premarket to postmarket follow-up.  
For example, a preplanned interim analysis that demonstrates favorable short-term 
study outcomes may result in a successful marketing application with continued 
follow-up relegated to the post-market stage.  For further information see the FDA 
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Guidance Balancing Premarket and Postmarket Data Collection for Devices Subject 
to Premarket Approval.3 
  In some cases, planned modifications can occur without inflating the false positive 
error rate, provided there is a sufficiently strong blind to outcomes by treatment 
groups.  
  Certain types of adaptive designs can enhance patient protection by increasing the 
probability that a patient is assigned to the treatment most likely to result in a better 
outcome for that patient.    
  Adaptive designs can include a plan to modify the patient population during the 
study, converting what would otherwise be a failed study to one with, for example, a 
more targeted indication for which there are data to support both safety and 
effectiveness.  This adaptation could help identify patients more likely to have a 
favorable benefit-risk profile from the use of a device.  Such an adaptation effectively 
enriches the study population, which should be considered when interpreting the 
study results. 
  Adaptive studies can improve decision-making at milestones during product 
development or increase the chance of a successful study with the potential to 
improve time-to-market.   
Overall, adaptive designs may enable more timely device development decision-making 
and therefore, more efficient investment in resources in a clinical study.  From an ethical 
standpoint, adaptive designs may optimize the treatment of subjects enrolled in the study and 
safeguard their welfare from ineffective or unsafe treatments and interventions at the earliest 
possible stage. 
D.  Limitations of Adaptive Designs 
                                                 
 
 
3http://www.fda.gov/downloads/MedicalDevices/DeviceRegulationandGuidance/GuidanceDocument
s/UCM393994.pdf 
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The following are some of the possible limitations associated with an adaptive design 
study: 
  Preplanned study design modifications can require more effort at the design stage, 
although this investment can pay great dividends during the study conduct.  Adaptive 
study designs that are overly complicated can be difficult to plan, cost more, be 
logistically difficult to carry out, and provide overall results that are difficult to 
interpret. 
  If not done correctly, adaptive designs can introduce operational or statistical bias, 
making it difficult to characterize the true effect of the investigational device.  See 
Section 8.A. for additional details.  
  A change to the study due to an adaptation may lead to results before the adaptation 
that are not sufficiently similar to those after the adaptation; this may confound the 
interpretation of the study results. (See Section 9.B.) 
  The maximum sample size and/or study duration may be greater than a non-adaptive 
design. 
For an in-depth discussion of the various types of planned modifications or 
adaptations, and their advantages and limitations, see Section 6. 
E.  Adaptive Studies as a Learning Paradigm  
An adaptive design can allow for learning from the results of the study during its 
course and for preplanned changes to the study based on the accumulating outcome data.  
Such adaptation is a natural process during early feasibility studies in device development 
but for pivotal studies and some late feasibility studies such adaptation needs to be well-
planned.  Adaptive studies can be especially useful in the pivotal stage if there are 
uncertainties about one or two aspects of the study.  In some cases, an adaptive design can 
obviate the need for a feasibility study (or a second feasibility study), and instead can allow 
the uncertainties to be scientifically addressed in an adaptive pivotal study.  Generally, an 
adaptive study allows the planners to learn, during the study conduct, about a small number 
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of uncertainties and make preplanned, scientifically-valid changes based on accumulating 
data while maintaining study integrity.  However, if there are numerous uncertainties, an 
adaptive design may be difficult to plan and implement.  In such cases, it may actually be 
more efficient and increase the overall likelihood of success to conduct one (or more) 
additional feasibility studies to resolve some of these uncertainties before embarking on a 
pivotal trial.   
Medical devices are often developed in a linear fashion, i.e., feasibility followed by 
pivotal explorations regarding clinical performance.  Early feasibility studies may have a 
number of modifications that occur during the study, which may be unplanned.  For these 
studies, it may not be necessary to apply formal statistical principles in order to draw useful 
conclusions for device development.  In contrast, for some traditional (later stage) feasibility 
studies and for most pivotal studies, robust  statistical validity is important, and unplanned 
modifications can undermine the studys purpose.  For more general information on pivotal 
clinical investigations, see the FDA Guidance Design Considerations for Pivotal Clinical 
Investigations for Medical Devices.4   
While most of the adaptations described in this guidance are more useful and 
appropriate for pivotal studies, adaptive designs can apply to some late feasibility studies.  
For example, an adaptive feasibility study could increase the statistical rigor and lead to a 
more accurate estimate of device performance and hence enhance decision-making and the 
likelihood of later success at the pivotal stage.  As outlined in Section 6.K., the planning of 
adaptations at the feasibility stage can also facilitate seamless feasibility-pivotal study 
transition.  Sponsors may be able to productively utilize information from feasibility studies 
to help guide the appropriate design of pivotal studies, whether adaptive or not. 
F.  Study Design Changes That Are Not Adaptive  
                                                 
 
 
4http://www.fda.gov/MedicalDevices/DeviceRegulationandGuidance/GuidanceDocuments/u
cm373750.htm 
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The following are examples of changes that are not adaptive:   
  Any change or revision to a study design is post hoc and not adaptive if it is based on 
unplanned findings from an interim (or final) analysis in a study where the blind 
(mask) of outcomes by treatment groups has been broken (even if only the coded 
treatment group outcomes).  Such modifications generally would endanger the 
scientific validity of the study since the false positive rate is not controlled and there 
is a strong possibility of operational bias5. The results from such a flawed study may 
not be valid. 
  Modifications based entirely on information from a source completely external to the 
study are not adaptive designs according to the definition provided in this guidance. 
These modifications will be discussed in detail in Section 7.B. 
3.  When to Choose an Adaptive Design 
Several factors contribute to the decision of whether or not to choose an adaptive 
design.  The most important considerations are, first, whether an adaptive design is feasible 
and second, whether an adaptive design is  more appropriate than non-adaptive 
(conventional) design. 
A. Is an Adaptive Design Feasible? 
An adaptive design is usually feasible if there are a small number of endpoints on which 
the adaptation will take place, and if the timing of the primary outcome is such that there is 
time to implement the adaptation if required. 
                                                 
 
 
5 For the purposes of this guidance, operational bias is the bias that arises if some or all participants 
(investigators, patients, care-givers) in the study have access to study results by treatment group and this 
information influences the ongoing operations of the study.   
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  When the time to ascertainment of the primary outcome is short relative to a longer 
enrollment period, a study is well situated to allow for adaptation. 
  When subjects enroll rapidly there may be inadequate time to implement changes in 
the study design. Alternatively, sponsors could consider slowing enrollment in order 
to implement the study modifications.  
  When enrollment and ascertainment time are both long but measurement of the 
primary outcome at an intermediate time point is predictive of the final time point 
then an adaptive design may be feasible, see section 6.C. for discussion of Bayesian 
adaptive designs. 
  When studies are designed with multiple primary and secondary endpoints then 
proper statistical control of the Type I (or false positive) error under an adaptive 
design may be difficult to ascertain.  
  When the study sample size is driven primarily by safety concerns (e.g. there are 
important rare adverse events associated with a medical device), sample size 
adaptation on effectiveness endpoints will not be feasible. 
B. How to Decide an Adaptive Design is Advantageous 
Given that an adaptive design is an option, there still remains the question of whether or 
not to choose an adaptive as opposed to non-adaptive design.  The choice of an adaptive 
design should be considered as the sponsor plans a pivotal study.  The recommendation is to 
select the optimal design for the particular situation, whether it is adaptive or a non-adaptive 
design.  In order to determine whether or not to pursue an adaptive study design, it can help 
to select a number of realistic scenarios, some perhaps optimistic and some less so.  For each 
scenario and a particular adaptive design, the challenge is to gauge how likely each scenario 
is and to calculate for that design the chance of success, the average size of the study, and the 
operating characteristics (probability of Type I error and the statistical power, discussed in 
Section 4.A.) and contrast it with the characteristics of a non-adaptive design.  For non-
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adaptive designs these calculations are usually straightforward.  The topic of how to calculate 
these quantities for adaptive designs, using either analytical techniques or computer 
simulation, will be discussed later.  Ultimately, the decision to proceed with a non-adaptive 
design over an adaptive one may rest on the sponsors confidence in the anticipated 
parameter values and willingness to risk a failed study. 
For a non-adaptive design, the sample size calculation is typically based on assumed 
values of several parameters.  When assumptions regarding the parameters used for 
designing the trial are somewhat uncertain, a study may benefit from an adaptive approach to 
re-estimate sample sizes based on interim data. A basic issue is the amount of confidence 
there is in the choice of these parameter values.  For example, suppose the study is planned 
for a somewhat optimistic treatment effect but the observed treatment effect is only 80% as 
large, but it is still clinically important.  In a non-adaptive design powered for the optimistic 
effect, the chance of succeeding on the effectiveness endpoint is smaller than planned and 
may be unacceptably low.  In this case the non-adaptive design based on a more optimistic 
effect size would be more likely to lead to a failed study for the sponsor.  In contrast, an 
adaptive design with an interim analysis to reassess the sample size can correct an under-
powered study, consistent with a smaller but still clinically meaningful effect size, increasing 
the chance of study success.  An adaptive design can guard against these uncertainties by 
learning from accumulating data during the study. 
If there is almost no knowledge of the parameters needed to design the trial then an 
adaptive design, although feasible, may be inefficient.  In this case, a non-adaptive design 
will also carry high risk, and additional feasibility studies may be necessary before 
embarking on a pivotal trial.  An alternative may be to combine feasibility and pivotal 
studies.  See section 6.K. for discussion on seamless designs. 
C. Anticipated Regret 
It is sometimes helpful to anticipate particular study outcomes that could lead to failure 
so as to ask what planning decisions one might later regret.  This concept is called 
anticipated regret.  For example, if a study just barely missed its objective but still had a 
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clinically important effect and in retrospect would have likely succeeded if the sample size 
had been 15% larger, that might suggest that one should have planned for an adaptive sample 
size design in which the sample size could be reassessed partway through the study.  The 
ability to anticipate what one might regret and then plan to adapt can significantly increase 
the likelihood of study success.  Adaptive designs that rely on anticipated regret can decrease 
the uncertainty in studies and make them much more predictable.  Such planning can be 
thought of as insurance against possible threats to the success of the study.  Using either 
analytical formulas or computer simulations one can calculate the costs associated with such 
insurance by comparing an adaptive design to a non-adaptive design.  (Simulations will be 
discussed in Section 7.C.).  
4.  Principles for Adaptation in the Design of Clinical 
Studies 
There are two underlying principles for the design of all clinical studies and of adaptive 
ones in particular: (1) control of the chance of erroneous conclusions (positive and negative) 
and (2) minimization of operational bias .  These principles are crucial to assure that a 
clinical study produces valid scientific evidence.  If the chance of erroneous positive 
conclusions is unacceptably large then the study will not provide sufficiently strong evidence 
to support a conclusion of device effectiveness.  Conversely, if the chance of erroneous 
negative conclusions is large, the study may fail even when the device is truly effective.  In 
short, studies that fail to follow these principles could generate evidence that is either invalid 
or inadequate.  In the two subsections below, these principles will be further explored.   
A.  Controlling the Chance of Erroneous Conclusions 
In order to assure scientific validity, a medical device clinical study should be 
designed to control the chance of erroneous conclusions.  For example, in a superiority study 
of a new device compared to a control, an erroneous positive conclusion would be to 
determine that the new device is superior to the control when it is not.  The inability to 
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minimize the chance of such erroneous conclusions threatens the scientific validity of the 
study and needs to be addressed.  An erroneous negative conclusion would be to fail to 
determine that the new device is superior to the control when it is.  Failure to control this 
type of error could lead to studies that fail to demonstrate the true effectiveness of the device. 
In adaptive designs, control of the rate of false positive conclusions can be a major 
statistical challenge, and inflation of this error rate can arise from various sources.  Most 
commonly, inflation of the false positive rate occurs due to multiplicity, which arises when 
the study data are examined and analyzed multiple times during the study without 
appropriate statistical preplanning and the study is stopped at any time point where nominal 
statistical significance appears to have been achieved.  Such multiple looks at the data may 
require a statistical adjustment to control the chance of erroneous positive conclusions.  For 
adaptive designs there are other sources of multiplicity:  multiple endpoints, multiple 
subgroups, multiple exposures (or dosages) or a combination of these features that could be 
dropped or added at an interim analysis.  Another type of multiplicity would be an increase 
in sample size at an interim analysis without any statistical adjustment; this could also lead to 
the inability to control erroneous conclusions.  With preplanning these types of error can be 
well controlled. 
It is advantageous for both the sponsor and the FDA to understand the 
operating characteristics of a study design.  The operating characteristics include the 
chances of false positive and false negative conclusions.  The former is called the 
probability of a Type I  error, where it is erroneously concluded that a device was 
effective when in fact it was not.  A Type II (or false negative) error would be failing 
to conclude that a device was effective when in fact it was.  The (statistical) power of 
a study is the probability of correctly concluding that the device is effective and is 1 
minus the probability of a Type II error.  Operating characteristics can also include 
other important properties of a design, including, for example, the expected sample 
size, or the probability of stopping for futility or success at an interim analysis. 
There are usually two approaches for evaluating the operating characteristics of 
adaptive study designs for regulatory submissions: analytical methods and simulation 
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studies. Analytical statistical methods are often used in some frequentist adaptive study 
designs and can provide approximate probabilities for Type I errors and for statistical power 
for non-adaptive and simple adaptive designs under different scenarios.  Simulations can be 
used to obtain operating characteristics for complex frequentist and Bayesian adaptive 
designs.  Analytical methods and simulation studies can be complementary to each other in 
evaluation of the Type I error rate and power of adaptive study designs.  In adherence to 
regulatory practice, the FDA recommends sponsors control the Type I error rate and maintain 
adequate power for all study designs. 
B.  Minimization of Operational Bias 
One type of bias frequently encountered in studies with adaptive designs is the 
operational bias (defined in footnote 5) that can arise in the conduct of the clinical study.  It 
is important that operational as well as statistical bias be reduced or eliminated because the 
presence of bias can distort the findings of a clinical study and undermine its scientific 
validity.  For example, in a two-arm study, if an interim analysis is conducted resulting in an 
increased sample size in a preplanned manner, investigators at the study sites, study subjects 
and/or third-party evaluators may behave differently, either consciously or subconsciously, if 
the existence or size of the increase, or the reason for the increase, becomes known to them.  
As a consequence, bias may be introduced into the clinical study.  Knowledge that the size of 
the study has been increased may help participants, site investigators, or blinded sponsor 
personnel to estimate the magnitude of the interim treatment effect which, in turn, can then 
affect the ongoing conduct of the study in various ways.  Similarly, if not blinded to the 
patients treatment assignment, the investigator may, unintentionally and unconsciously,  
change a decision about whether to enroll a subject in the study, or start treating the subjects 
in the investigational treatment group differently from subjects in the control group.  Any of 
these actions can then lead to operational bias.  Operational bias can be a significant threat to 
the scientific integrity of a clinical study and cannot be overcome by statistical adjustments 
to account for its presence.  If designated analysts of the study data have access to the overall 
unblinded results of an adaptive trial during its conduct, it is vital that standard operating 
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procedures be followed to insulate this information from the study sponsor and investigators.  
Furthermore, it is important to assure regulatory authorities and other stakeholders that there 
are safeguards in place to ensure that those with legitimate access to unblinded data do not 
share information about these data with others.  This concept of operational bias and 
firewalls6 will be discussed in Section 10.B. of this document.   
5.  Adaptively-Designed Studies without the Need to 
Break the Blind 
For a comparative study, when data blinding is unequivocally maintained, adaptations 
based only on the demographic characteristics of the subjects at baseline and/or on the 
aggregate outcome results do not pose any difficulty in terms of Type I error control or bias.  
On the other hand, changes based on outcomes by treatment group (whether coded or fully 
unblinded) may be problematic.  In this section, breaking the blind means having access to 
the outcomes by treatment groups.  It does not mean that one cannot know: 1) the 
demographic breakdown of the groups, 2) the overall combined outcomes if there are two or 
more groups, or 3) which subjects are assigned to which groups (as long as the outcomes by 
subject or by group remain masked or blinded).  That is, someone with knowledge of both 
outcome and treatment group is not blinded. 
An example of an adaptation based on demographic or baseline measurements of the 
subjects enrolled in the study would be to change the allocation rule on an individual basis to 
obtain better balance between the control and treatment groups.  Note that this allows for 
knowledge of which individual subjects have been assigned to different treatment groups but 
does not allow for knowledge of any effectiveness or safety outcomes.  This is called 
covariate adaptive randomization or covariate adjusted randomization; it uses accumulating 
baseline data in an attempt to provide better balance between the two groups.   
                                                 
 
 
6 For the purpose of this guidance we define a firewallas a process or procedure to ensure and document that 
sufficient restrictions on information flow to control statistical and operational bias is maintained. 
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A classic example of adaptation based on aggregate outcomes that is widely used is to 
power a time-to-event study or a survival study not by the number of patients in the study, 
but by the total number of clinical events.  The study continues until the desired number of 
events has been observed.  For such studies, the exact number of subjects cannot be planned 
in advance, although a maximum may be pre-specified.  One is using the accumulating 
evidence from the study in the form of the aggregate results, in this case the total number of 
events, although the number in each of the comparative groups would not be revealed in 
either an unblinded or coded fashion to the investigators.  The knowledge of the total number 
of events could lead to changing the total number of patients or to an extension of the 
duration of the study. 
As another example of using aggregate results with multiple treatment groups without 
breaking the blind, one could observe the pooled overall success rate and, assuming two 
groups that differ by a hypothesized amount, infer that the original assumptions about the 
control rate and the investigational rate cannot be valid and that a change in sample size is 
merited.  As yet another example, it is possible to calculate the overall variance for a 
continuous endpoint and make a sample size adjustment based on the hypothesized 
difference in the means.   
In the prior two examples, in order to make a prospective decision, the required amount 
of aggregate information to be used is determined in advance, and the study is continued 
until that amount of information is obtained. 
If the blind is maintained so that decisions regarding adaptation are made without 
knowledge of outcomes by coded or unblinded treatment group, in the case of a comparative 
study (or any outcomes in a one-arm study), then such adaptive designs pose no theoretical 
scientific difficulty.  Sponsors are encouraged to consider adaptations that use baseline data 
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and aggregate outcomes for studies that do not break the blind, and it is strongly advised that 
such a study be conducted under an approved IDE, when appropriate.7 
  While it is strongly preferred that such adaptations be preplanned at the start of the 
study, it may be possible to make changes during the studys conduct as well.  In such 
instances, the FDA will expect sponsors to be able to both justify the scientific rationale why 
such an approach is appropriate and preferable, and demonstrate that they have not had 
access to any unblinded data (either by coded treatment groups or completely unblinded) and 
that access to the data has been scrupulously safeguarded.  
6.  Adaptations Using Unblinded Data 
This section considers some adaptive designs that are based on accumulating unblinded 
results; these designs require thoughtful planning.  Sponsors are encouraged to consult with 
the FDA prior to embarking on an adaptive design, in general, and for the types of 
adaptations that follow, in particular.  Group sequential designs, sample size adaptation, and 
group sequential design with sample size reassessment are the most widely used.   
A.  Group Sequential Designs 
Group sequential designs allow for interim analysis of the outcomes by treatment group 
and possible early stopping for success or futility.  These designs have been relied upon for 
many years by the statistical and clinical trial community.  These designs usually prescribe 
one or more planned interim looks at unblinded data with the possibility of stopping the 
study to declare either success or futility.  They require prospective planning to determine the 
exact nature of the group sequential design, and introduce more flexibility compared to the 
non-adaptive sample size designs while controlling the overall Type I error rate of the study.  
                                                 
 
 
7 An IDE is required when a sponsor intends to use a significant risk device in an investigation, when the 
sponsor intends to conduct an investigation that involves an exception from informed consent under 21 CFR 
50.24, or when FDA notifies the sponsor that an application is required for an investigation.  21 CFR 
812.20(a)(1).   
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Group sequential studies can be frequentist or Bayesian.  If the device performs better than 
expected and there are sufficient safety data, this adaptive design can enable early stopping 
for success, saving time and resources.  Such designs should include pre-specified statistical 
plans that account for the interim analyses and appropriate adjustments to the significance 
level alpha.  For example, an OBrien-Fleming plan prescribes a pre-determined fixed 
number of interim looks at fixed times with a prescribed fraction of the significance level 
alpha spent at each look.  In contrast, a Lan-DeMets alpha-spending approach allows for 
more flexibility, since what is specified is the function for spending alpha at various time 
points in the trial.  Once the alpha-spending function is specified at the outset, the number of 
looks (up to than the maximum allowed) and their timing are flexible.  If there is a real 
possibility that the device may perform better than expected, the sponsor should consider 
using a group sequential design to allow for the possibility of stopping for success since in a 
non-adaptive design early stopping is not scientifically valid.  If a sponsor believes that it is 
possible that a study could have results that would be so impressive at an interim look that 
the ethical decision would be to stop the trial, then the preferred approach would be to design 
an adaptive trial to allow for a scientifically-valid interim look such as in a group sequential 
trial.  We recommend that sponsors utilize a Data Monitoring Committee (DMC) to examine 
the data in a secure and confidential manner and implement the group sequential design. 
(DMCs are discussed in Section 9.A.) 
A disadvantage of any group sequential study is that a sponsor needs to accept some 
uncertainty because the accumulating data and study interim analyses will determine whether 
the study needs to enroll the entire cohort or can be stopped early for success or futility.  
Another disadvantage is the possibility of operational bias after a decision to continue at an 
interim analysis since a site or sponsor personnel could conclude that the effect size is not 
sufficiently large to stop the study.   
B.  Sample Size Reassessment 
It is a common fallacy that simply adding more subjects or samples as an extension to 
a concluded study that has failed to meet its pre-specified endpoints is a scientifically-valid 
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way to continue a clinical investigation.  Because the chance of an erroneous positive 
conclusion is no longer well controlled, the approach of simply extending a study at the end 
in a manner that is not pre-specified is neither scientifically sound nor recommended.  In 
contrast, an adaptive design can permit sample size reassessment and appropriately control 
the Type I error in hypothesis testing or, correspondingly for interval estimation, the 
confidence coefficient.  This may be accomplished through pre-specified analysis after a 
specified portion of the study has been completed to assess whether the planned sample size 
is adequate and, if not, to increase it in pre-specified manner.  Such a strategy can control the 
chance of erroneous positive conclusions and produce scientifically-valid inferences.  
Adaptive designs using such sample size reassessment (SSR) can help avoid under-
powering studies, particularly in situations where substantial uncertainty exists concerning 
the variance or effect size.  In a study design with a preplanned sample size reassessment, 
one or more preplanned interim looks are conducted to potentially adjust the sample size 
according to the comparison of the unblinded treatment group results.  This is in contrast to 
blinded sample size reassessment that was considered in Section 5.  It is crucial that the 
discussion concerning the clinically important effect size occurs during the study planning 
stage and not after outcome data are available.  As a result, an adaptive SSR study design is 
not intended to fix or salvage an already failed study, but instead can help prevent a failed 
study from occurring in the first place.  Specifically, study planners should ask the 
anticipated regret question about the impact of a smaller effect size at the planning stage and 
incorporate a realistic, rather than overly optimistic, assessment of the investigational 
devices performance into their study planning. 
There are a number of statistical techniques for SSR.  Some methodologies use 
conditional power and others predictive probability (i.e., two different forms of stochastic 
curtailment).  SSR can be done in a simple study with a single interim analysis or it can be 
performed more than once at pre-specified times during the study.  It is recommended that 
the sponsor and the FDA reach agreement prior to study initiation on the minimal clinically 
important difference in treatment effect.  Any decision concerning whether a particular 
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difference is clinically important should be made at the outset and not influenced by the 
interim study effectiveness results.   
In planning a sample size reassessment, careful consideration should be given to the 
reassessment time point(s).  If reassessment is performed too late, it may be inefficient. Early 
looks may result in a suboptimal sample size determination due to inherent variability during 
earlier phases of study conduct. Analytical calculations or computer simulations performed 
under different scenarios can help guide the choice of optimal time point(s) for the 
reassessment.  (See Section 7.C. for more discussion on simulations.)  Actual control of Type 
I error rate will depend on the sample size adjustment methodology employed and the 
preplanned analysis that is used to combine the data from before and after the adaptation.   
In some circumstances, if the primary endpoint takes a long time to observe (such as a 
two-year endpoint), the sample size adaptation may be ineffective.  For such cases, sample 
size adaptation could instead be based on intermediate endpoints known to be associated 
with the primary endpoint.  For more information on the use of surrogate and intermediate 
endpoints see the FDA guidance Expedited Access for Premarket Approval and De Novo 
Medical Devices Intended for Unmet Medical Need for Life Threatening or Irreversibly 
Debilitating Diseases or Conditions.8 
  The use of a Bayesian model that learns from the accumulating data on the intermediate 
endpoint as well as the final endpoint is one statistical approach and is discussed in the next 
subsection.   
In some cases, sample size reassessment is preferable to a group sequential design.    
Sample size reassessment is usually relatively more efficient when the increase in sample 
size is small.  If, at the interim analysis, a large increase in sample size is required, then 
regardless of the statistical methodology chosen, SSR is extremely inefficient and a better 
strategy would have been to construct a group sequential design with some more realistic 
                                                 
 
 
8http://www.fda.gov/downloads/MedicalDevices/DeviceRegulationandGuidance/GuidanceD
ocuments/UCM393978.pdf 
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expectations about the size of the treatment effect.  While the effect size is unknown at the 
start, if the expected range is narrow, a sample size reassessment strategy might make more 
sense. 
C.  Bayesian Sample Size Adaptation 
Many Bayesian designs include sample size adaptation, since several factors that 
determine the trial sample size, such as effect size, variability of the sample estimate of the 
primary endpoint, and amount of prior information borrowed, are often not known at the 
design stage.  Sample size decreases as the effect size and the amount of prior information 
increases and sample size increases as sampling variability increases. 
When Bayesian hierarchical models are used to combine data from a current study with 
prior data, the amount of prior information borrowed is unknown before the start of the study 
and will depend on the similarity between the current study data and prior data, which is 
learned as data from the current trial accumulates.  Whether there are prior data or not, a 
Bayesian trial design can often include a mathematical model that predicts a final clinical 
endpoint from earlier measurements.  In that case, predictability will depend on the 
correlation between the earlier measurements and the final outcome, a correlation that is not 
known at the design stage.  All these factors are learned as data accumulate and the sample 
size is adjusted as information is gathered. 
In other cases, where a mathematical model relating results obtained in the course of the 
trial with the primary endpoint can be constructed and the models parameters are estimated 
using accumulating data, the results can be used to predict the primary endpoint.  The better 
the prediction, the smaller the required sample size, and a well-designed Bayesian study 
should be planned in such a way that the sample size is adjusted as information accumulates.  
As noted above, this idea is referenced in the FDA guidance document Expedited Access 
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for Premarket Approval and De Novo Medical Devices Intended for Unmet Medical Need for 
Life Threatening or Irreversibly Debilitating Diseases or Conditions.97 
Preplanned Bayesian adaptive designs could include interim analyses for sample size 
adaptation, for early trial success, and for futility.  At the interim analyses, predictive 
probabilities of trial success would be calculated based on data accumulated up to that point.  
If the probability is sufficiently high (i.e.,above a pre-specified value), the trial may stop for 
early success; if the probability is too low (i.e.,below a pre-specified value), the trial may 
stop for futility; and if the probability falls between these values, continuation of the trial 
may be warranted (with (or without) termination of recruiting, if above (or below) yet 
another pre-specified value).  Simulations are needed to determine reasonable thresholds for 
these actions. 
A Bayesian adaptive design should consider simulations for assessment of its operating 
characteristics; the performance of the design depends on preselected design parameters.  
Simulations are used to determine the threshold values of predictive probabilities to stop for 
early success, futility, or for stopping recruitment of new patients.  For more information on 
how to conduct such simulations, see Section 7.C. on simulation, and, for a more detailed 
discussion, refer to the FDAs Guidance for the Use of Bayesian Statistics in Medical 
Device Clinical Trials.10  
D.  Group Sequential Designs with Sample Size Reassessment  
A common adaptive design combines a group sequential design with interim looks, not 
only to stop early for success but also to re-assess the sample size and to increase it 
                                                 
 
 
9http://www.fda.gov/MedicalDevices/DeviceRegulationandGuidance/GuidanceDocuments/U
CM393978 
 
10http://www.fda.gov/medicaldevices/deviceregulationandguidance/guidancedocuments/ucm
071072.htm 
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  Contains Nonbinding Recommendations 
 
 
 
according to a pre-specified plan.  Such designs, while more complicated, offer additional 
advantages in certain situations.  
E.  Dropping a Treatment Arm  
In a study in which there is more than one experimental arm, with proper planning 
one may plan to drop one of these experimental arms during the course of the study based on 
poor effectiveness performance.  Dropping such an arm can increase study efficiency and 
focus resources on aspects of the study most likely to prove beneficial and successful.   
F.  Changing the Randomization Ratio  
An adaptive randomization plan that allows for a change in the randomization ratio 
between the control and treatment arms based on treatment outcomes is called response 
adaptive randomization.  Response adaptive randomization can mitigate some ethical 
concerns by reducing the probability that a patient will be exposed to products that are less 
effective or less safe.  It can improve study efficiency (e.g. a Bayesian approach that adapts 
based on sufficiency of information from the control arm).  Such adaptive designs can 
enhance patient protection by planned allocation to the treatment that, during the course of 
the study, is found to be either more effective or safer.  Response adaptive randomization can 
sometimes lead to slightly larger studies but could facilitate investigator and patient 
enrollment. 
G.  Investigating both superiority and non-inferiority 
It is possible to plan a study to investigate both the superiority and the non-inferiority 
of a new treatment to an active control.  Two different strategies may be used: one is to plan 
the study as a superiority trial and have a fallback hypothesis of non-inferiority; the other is 
to plan (and size) the study originally as non-inferiority but allow for an investigation of 
superiority.   
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A superiority study designed to investigate non-inferiority in the event that the 
superiority hypothesis fails should be prospectively planned; in particular, the non-inferiority 
margin should be pre-specified and agreed upon in advance before any unblinding.  
Additionally, a prospective plan could incorporate sample size reassessment with the change 
in claim.   
Generally if the original plan is for non-inferiority, investigating superiority is 
possible without additional preplanning since the superiority margin is already pre-specified.   
However, study planners may wish to incorporate a preplanned interim assessment and pre-
specified sample size reassessment in case mid-course results are sufficiently promising that 
a superiority claim may be within reach; such adaptations should be pre-specified and 
appropriate analyses provided.   
H.  Adaptive Enrichment 
Another type of adaptive design is one that plans to investigate, using unblinded data, 
at one or more interim looks, pre-specified patient subgroups that might have differing 
responses to the experimental device.  Such analyses could be used in a preplanned way to 
modify the inclusion/exclusion criteria after an interim analysis.  For example, suppose that it 
was anticipated that there may be a differential effect due to a demographic factor such as 
sex.  Then at a preplanned interim look, the difference could be assessed and the trial 
potentially modified to include only men or women from that point onwards.  Another type 
of adaptation would be to incorporate a sample size reassessment to ensure a sufficient 
sample to carry out analyses separately for men and women in the case where the interim 
data suggest that the two groups should not be pooled.  Preplanned methods could also 
change the sample size based on the decision to narrow the population indication.  In all 
cases it is important that the chance of erroneous findings (the overall probability of a Type I 
error) be well-controlled in a prospective manner.   
I.  Planning to Adapt Based on the Total Information  
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For this novel type of design, the stopping rule is based on the amount of information 
in the unblinded data and this information is usually measured in terms of the variance of the 
primary endpoint.  That is, the study continues until a pre-specified amount of information is 
obtained.  Because the stopping rule is based only on the amount of available information, 
there is no penalty associated with repeated looks.  For example, the decision about when to 
stop could be based on the estimated standard error of the mean for the difference between 
the mean responses of the investigational and control groups.  Typically, this would 
correspond to stopping when a fixed confidence interval width for the difference has been 
achieved.  This total information approach safeguards against the misspecification of the 
parameters that one might have in a non-adaptive design study.  The study is always 
correctly powered, and there is no statistical penalty for looking early.  This design does not 
suffer from the problem of the non-adaptive study design, which sometimes is too large and 
other times not large enough; in fact, it can guarantee that a study is always right-sized.  
This approach could be particularly helpful in some one-arm studies and some studies for 
diagnostic devices. 
In this design it is important to meticulously abide by the pre-specified stopping rule.   
Intentionally overrunning the sample size can result in a variance that is smaller than agreed 
upon, and thus increases the chances of a statistically significant result for a clinically 
unimportant difference.  Also, whereas this total information approach does control the Type 
I error rate, it would no longer to do so in the case of an overrun.  (In that way, it is similar to 
the unplanned study extension that was discussed in Section 6.B.)   
J.  Adaptation of the Device or Endpoint 
Preplanned device or endpoint adaptations are rare for pivotal studies.  On the other 
hand unplanned changes to the device or the endpoint are quite common in feasibility 
studies, especially early feasibility ones.  For unplanned changes to the device or to the 
endpoint, see Section 7.B.  For planned changes, study planners are advised to prespecify the 
changes (or anticipated types of changes) and account for them in a pre-specified statistical 
plan with appropriate consultation with the FDA in advance.   
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K.  Seamless Studies 
Device development and evaluation plans may include a feasibility investigation that 
smoothly transitions to a pivotal study in a preplanned manner, if no significant changes to 
the device or study are made.   For example the feasibility portion of a seamless study could 
include several possible device configurations; only the configuration that showed the most 
favorable results in the feasibility stage would be continued (seamlessly) into the pivotal 
study.  In such cases, if the study is designed to be inferentially seamless, all data may be 
included in the final analysis.  Prospective study planning to combine the feasibility and 
pivotal study phases should occur before the feasibility data are accessed in an unblinded 
manner; the plan needs to control the overall Type I error for the combined two stages.  If the 
two studies are combined for operational purposes but are inferentially distinct then this 
would not be considered an adaptive design. 
7.  Special Considerations 
A.  Changes to Pivotal Clinical Studies that are Not Preplanned 
Using Blinded Data 
Under certain circumstances, a number of scientifically-valid changes to the study 
design can be entertained even if they are not preplanned.  Such changes should utilize 
complete masking of the outcome results by treatment group, so that no one representing the 
sponsor (or the FDA) has access to the coded or unblinded outcomes by treatment group.  A 
major advantage of conducting a study where the outcome by coded or unblinded treatment 
groups is fastidiously guarded is that changes to the study based entirely on outside 
information can be reasonably entertained.  For example, if only an independent statistician 
and the DMC had access to the outcomes by coded or unblinded treatment groups and the 
sponsor could provide evidence that the results were limited to only those people, the 
sponsor or the Steering Committee could propose scientifically-valid modifications to the 
design of the study based on information entirely from outside the study.  Note that it is not 
  26 
 
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appropriate for those with access to the outcome data by treatment group, including the 
DMC, to propose or provide input concerning study revisions.  Inflation of Type I error can 
occur whenever an unplanned revision is suggested as a result of an interim analysis, 
regardless of where the suggestion arises.  The discussion of firewalls to prevent 
inappropriate disclosure of information is discussed further in Section 10.B.  Unplanned 
study changes under appropriate circumstances are scientifically viable and should be 
discussed with the FDA for approval before implementation.     
B.  Changes to Pivotal Clinical Studies that are Not Preplanned with 
Unblinded Data 
If outcome results are not blinded or masked (as in an open label study), study design 
changes become problematic due to the fact that the scientific integrity of the study may be 
endangered.  Sponsors are strongly encouraged not to implement such changes and to meet 
with the FDA if such changes are being considered. 
In general, any proposed modification to the protocol or the Statistical Analysis Plan will 
be problematic if it will affect the validity of the data or information generated in the study or the 
scientific soundness of the plan. 
For studies conducted under an IDE, if the validity of the data/information generated in 
the study or the scientific soundness of the plan are affected, such modifications will generally 
require prior approval though an IDE supplement.  Note that there are exceptions to the 
requirement for prior approval for changes effected for emergency use, changes effected with 
notice to the FDA within 5-days, and changes submitted in an annual report, when those changes 
meet certain criteria as specified in 21 CFR 812.35 (a)(2)-(4). Changes to essential device 
functionality based on data should be limited to feasibility studies, if at all possible. There are 
limitations to the extent of allowable device changes for a pivotal study, as significant device 
modifications can undermine the scientific validity of the pivotal trial data and the legitimacy of 
combining pre- and post-device modification data.  Sponsors are encouraged to engage the 
Agency regarding possible fundamental device modifications during a study, as delayed 
disclosure of device modifications can lead to longer review times and lower likelihood of study 
success.  Additional complexity is introduced by evolving device modifications (e.g. an 
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evolving algorithm) that may be more appropriate for a feasibility study rather than a pivotal 
study.  For example, the use of pivotal study data to assess, modify, and finalize an algorithm for 
a diagnostic device may produce biased performance results and therefore is not recommended 
unless a preplanned analysis is put in place to control the Type I error rate.  When determining 
whether pooling of data from different device versions is acceptable, an analysis as to whether 
there is homogeneity between the outcomes (both safety and effectiveness) for the different 
versions of the device, as discussed more broadly in Section 8.B., is critical.   
C.  Simulations in Planning an Adaptive Design 
Computer simulations can play a crucial role in adaptive designs and can provide the 
operating characteristics of the study design under different scenarios.  The simulations can 
evaluate different scenarios with a variable number and timing of the interim analyses and 
can be used to weigh the advantages and disadvantages of different adaptive designs or an 
adaptive design compared to a non-adaptive design.  Simulations can provide insights into 
required samples sizes, operating characteristics, and interrelationships between trial design 
choices and patient characteristics that cannot be obtained by analytical methods. 
Computer simulations used in planning adaptive study designs have limitations.  
First, their utility and quality are dependent on the ability to model realistic scenarios.  
Second, programming mistakes by the sponsor in the simulation software code, which may 
be difficult to detect, can lead to poor study design choices.  Third, complex study designs, 
such as those that involve multiple endpoints or a complicated null hypothesis boundary may 
be difficult to perform.   
Nonetheless, simulations for adaptive design trials, although complex, are 
encouraged.  Good programming practices and careful consideration of the trial design can 
help prevent errors in the simulations.  When a sufficiently wide range of relevant scenarios 
can be covered, simulations can provide useful insight on adaptive trial design, conduct, and 
analysis.  Simulations can provide finer coverage of the parameter space (including nuisance 
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parameters) and more detailed descriptions of the operating characteristics than analytical 
methods can usually provide.11 
D.  Adaptive Designs for Safety Endpoints  
While many adaptive study designs focus on the effectiveness endpoint, it is also 
possible to design adaptive clinical studies for safety endpoints.  For example, an adaptive 
design could be developed to demonstrate that a device had an overall serious adverse event 
rate of less than 8%.  Specifically, a group sequential approach could be used to allow for 
one or more interim looks and an early study termination for success if the serious adverse 
event rate was much less than 8%.  Alternatively, one could develop a stopping rule that 
would terminate the study if there were no adverse events in a pre-specified number of 
patients but would allow for continuation to a later stage with one or more events.  The 
preplanned rule would need to demonstrate that it controlled the chance of the erroneous 
conclusion that the serious adverse event rate was at most 8%, for this hypothetical example. 
E.  Adaptive Designs for Open-Label Randomized Studies   
Unlike drug trials, many scientifically-valid medical device studies are not, or cannot, 
be blinded.  For example, the medical device may have visible parts or treatment features 
(e.g., electrical stimulation) that can make it obvious to the patient and the medical staff that 
a device (or which device) is being used.  While in some cases, patients, third-party 
                                                 
 
 
11 Some discussion on simulations in the Bayesian context, also applicable to frequentist 
trials, can be found in the Food and Drug Administration (2010) Guidance for the Use of 
Bayesian Statistics in Medical Device Clinical Trials (issued February 5, 2010)  Appendix, 
page 40.  
http://www.fda.gov/medicaldevices/deviceregulationandguidance/guidancedocuments/ucm0
71072.htm 
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assessors, or even the health care provider can be blinded, there are many instances where 
this is not possible.  Studies where blinding does not occur are called open-label. 
Using an adaptive design for an open-label study presents additional difficulties 
because operational bias can be introduced when patients or trial personnel know the 
treatment assignment and either consciously or subconsciously change how they behave.  
This potential for bias is not unique to adaptive trials but rather is true of open-label studies, 
in general.  However, extra care may be warranted with an adaptive design where there is a 
potential to alter the trial midcourse. 
The importance of pre-specified adaptations is paramount for open-label studies that 
incorporate an adaptive design.  At the design stage, every effort should be made to spell out 
in detail all possible intended changes and the corresponding adaptations, with appropriate 
checks on the trials operating characteristics.  For example, for a classical group sequential 
design, before the start of the trial, one should clearly pre-specify in the protocol the number 
and timing of the interim analyses, and the corresponding alpha-spending function.  
Although such pre-specification may not address the problem of operational biases in an 
open-label trial, a pre-specified protocol greatly reduces the possibility of unplanned changes 
being made based on interim trial findings.  Unplanned modifications that were not 
anticipated during the planning stages can be problematic if they occur during the course of 
the open label study. 
F.  Adaptive Designs for Observational Comparative Studies 
Adaptive designs may also be used in studies designed with an historical or non-
randomized concurrent control.  Typically, a comparison is conducted of baseline covariates 
in the treatment group compared to the control group.  In an adaptive design, such a 
comparison should be pre-specified and performed in a manner such that the personnel who 
conduct the comparability evaluation are blinded/masked to outcomes of all arms.  If the 
comparability evaluation indicates that the control group is not comparable to the treatment 
group with the investigational device, a change or modification to the control group may be 
possible.  Even if the control group is appropriate, the sample size and power estimation 
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could be reevaluated and modified as long as unblinded access to the outcome data has not 
occurred.    
G.  Adaptive Designs for One-Arm Studies without a Control 
Although every effort should be made to conduct a randomized concurrent controlled 
trial when possible, sometimes a medical device trial will compare the treatment arm to a 
performance goal because it is not ethical or feasible to have a placebo (sham) device or an 
active comparator device serve as the control arm.  Although there are additional biases 
(including operational bias) that may be introduced by a one-arm study, a pre-specified 
adaptive design may still be possible.  To control the operational bias, the knowledge of the 
outcome data should be carefully restricted.  A screening log of all incoming subjects 
(including those not included in the study) at each clinical site can ensure that no overt 
selection occurred and thus help to reduce possible manipulation of the trial findings.  
8.  Additional Considerations for Diagnostic Devices  
While issues discussed in other sections of this guidance also apply generally to 
diagnostic medical devices, there are some unique issues with adaptive study designs for 
diagnostic devices. A thorough discussion of general design considerations can be found in 
the FDA guidance Design Considerations for Pivotal Clinical Investigations for Medical 
Devices4 and would be useful to review if considering an adaptive design for a diagnostic 
device.  Diagnostic performance is often evaluated using estimation and confidence interval 
approaches rather than hypothesis testing.  The adaptive design methods described above can 
be translated into appropriate confidence intervals for diagnostic studies.  As noted in 
Section I, this guidance does not apply to clinical studies of combination products or co-
development of a pharmaceutical product with an unapproved diagnostic test.  However, the 
underlying principles may be applicable to such studies.  
Unlike studies of therapeutic devices where study completion may be challenged by 
slow enrollment or long follow-up times, many clinical performance studies of diagnostic 
devices are cross-sectional, in which enrollment is quite rapid.  Thus, in many cases, the 
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rationale for pursuing an adaptive study for a therapeutic device may not be relevant for a 
study of a diagnostic device.   
Nevertheless, diagnostic devices are heterogeneous in scope and include devices that 
may be invasive or pose risks to a patient (e.g., devices used during surgery).  In this case an 
adaptive design can be advantageous since fewer patients may be exposed to an inferior or 
less safe device.  Diagnostic clinical outcome studies that evaluate diagnostics for patients 
with a rare condition with slow accrual may also benefit from an adaptive design.  
A.  Adaptation to prevalence and the entire disease spectrum 
Studies may be designed to be adaptive to the prevalence of the disease in the study.  
For example, disease prevalence could be monitored (using a clinical reference standard12  , 
not the investigational device) until the requisite numbers of diseased and non-diseased 
subjects are enrolled.   
In some diagnostic device studies, the frequency of certain critical subgroups may be 
less than expected; a prospective adaptive study can use a planned interim look to assess and 
adapt to assure appropriate subgroup representation.  Such adaptations could entail the 
addition of new clinical sites to obtain a different patient mix, e.g., adding a family practice 
rather than a specialty clinic if more patients with early stage disease are sought.  When only 
the clinical reference standard results are known to the group making adaptation decisions, 
no correction for confidence level is needed.  A similar approach can be used when device 
performance is being estimated by hypothesis testing.  As always, pre-specification and 
careful documentation of procedures to maintain the necessary blinding is recommended 
(See Section 11.C.).   
                                                 
 
 
12 For purposes of this guidance, the clinical reference standard is defined as the best 
available method for establishing a subjects true status with respect to a target condition; 
please refer to FDA Guidance Design Considerations for Pivotal Clinical Investigations for 
Medical Devices 
 
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B.  Blinded Sample Size Reassessment Based on Interim Estimates 
for the Comparator 
Some diagnostic device studies are designed to compare a new, investigational 
diagnostic (or a marketed diagnostic for a new indication) to an already cleared or approved 
device.  In some cases, an adaptive study design may increase study efficiency and the 
likelihood of success by prespecifying an interim analysis and potential sample size 
adjustment.  For example, if the study or intended use population has a different prevalence 
from that of the population previously studied, a study adaptation may assure that there are a 
sufficient number of  subjects  with the target condition of interest.  With appropriate pre-
specifications and well-documented blinding, such an adaptation would not require statistical 
multiplicity adjustments in the calculation of confidence intervals.  However, if the rationale 
for increasing the sample size is performance-based and not pre-specified, a multiplicity 
adjustment may be required to maintain scientific integrity of the study.   
Other adaptive designs for studies evaluating diagnostic devices are feasible. 
Sponsors are encouraged to consult with the Agency in advance of any adaptive design 
submission. 
9.  Principles in the Analysis of Data from Adaptive 
Designs 
While previous sections focused on the importance of prospective planning during the 
design phase of adaptive studies to control the risk of operational bias and erroneous 
conclusions, this section considers some of the specific challenges of analysis of data from 
adaptively-designed studies; however, a detailed discussion is beyond the scope of this 
guidance. 
                                                                                                                                                       
 
 
http://www.fda.gov/MedicalDevices/DeviceRegulationandGuidance/GuidanceDocuments/uc
m373750.htm. 
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  Contains Nonbinding Recommendations 
 
 
 
A.  Bias Control in the Estimates 
Even when the Type I error rate is well controlled, estimators of treatment effect for 
adaptive designs are frequently biased.  For example, in a group sequential design, if the 
stopping boundary is crossed and the study is stopped at the interim for success, the naive 
(point) estimate of the treatment effect is upwardly biased, even though the overall Type I 
error rate of the study is controlled.  The same type of bias occurs in many confidence 
intervals.  In some cases the amount of bias can be estimated by simulation.  Efforts to adjust 
for this bias (unbiased estimators, bias-adjusted estimators, repeated confidence intervals) 
can be prospectively planned in the Statistical Analysis Plan. 
B.  Homogeneity of Results after a Modification 
Studies that undergo modifications during their conduct, whether planned or unplanned, 
should be analyzed to determine whether there are detectable differences in study 
participants, investigational device performance, study outcomes, or other important study 
aspects before and after the study modifications.  Although such analyses may not be able to 
detect subtle operational bias, they would allow detection of gross changes in patient 
characteristics or outcomes.  Some adaptations might be expected to result in changes (e.g., 
when there is a change in the population of interest).  In other cases, a difference before and 
after might be observed when no difference was expected or desired.  Such a result may be 
an indication of study operational bias and can undermine the scientific validity and 
interpretation of the study.   This may also be a problem in non-adaptive design studies. 
C.  No adaptation made 
If no adaptation was performed during the course of the study that was designed to be 
adaptive, the study would still be considered adaptive by the FDA.  Therefore, the study 
should be analyzed according to its pre-specified analysis plan and be reported as such to the 
FDA. 
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  Contains Nonbinding Recommendations 
 
 
 
10.   Challenges of Adaptive Studies 
A.  Data Monitoring Committees 
Data Monitoring Committees (DMCs) play an important role in protecting the safety 
of trial participants.  In some cases, the DMC may be prospectively selected as the 
appropriate entity to implement all pre-specified study adaptation decisions.  Even in cases 
where another entity is charged with the logistics of the adaptation, the DMC is tasked with 
safeguarding the trial participants and should monitor their safety during the adaptive trial.  
The DMC should be appropriately constructed to assure that its members possess the 
necessary expertise and experience for an adaptive study design, if such adaptations are part 
of the study plan.  In cases where adaptations are based on interim analyses of unblinded 
outcomes, robust pre-specified and well-documented procedures should be in place before 
initiation of the clinical trial or review of the data.  Critical aspects include but are not 
limited to: (1) assurance of a robust firewall for managing access to unblinded interim 
data/analysis, since DMC interactions with a sponsor have the potential to adversely impact 
study integrity, and (2) the shielding of investigators and study participants as much as 
possible from knowledge of the adaptive changes that are implemented.  The DMC charter 
should include a complete description of standard operating procedures relating to 
implementation of the adaptive design protocol.  The protocol should state the role of the 
DMC, with particular emphasis on how the DMC will be involved in the conduct/analysis of 
the adaptation.  A clarification on whether or not a DMC will review any interim analyses 
and who will conduct the adaptation of the design should be provided.     
While the use of the DMC to manage the adaptations during an adaptive design 
clinical trial may be an acceptable option, a sponsor may instead consider assigning the 
responsibility for adaptation decisions to an independent statistician, a contract research 
organization, or some other independent clinical trial body.  In any case, the underlying 
validity and integrity of the study depends on appropriate decision-making and 
implementation and should always be paramount when planning adaptive designs.   
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  Contains Nonbinding Recommendations 
 
 
 
Although the DMC may be tempted to recommend changes to the adaptive design or 
to the fundamental study type (e.g., from a non-adaptive study to an adaptive one) during the 
conduct of the study, once the DMC has access to coded or unblinded outcomes, such 
recommendations can imperil the scientific integrity of the study.  Fundamentally, the DMC 
is tasked to protect the subjects in the study and should always act accordingly to protect the 
subjects in the trial.  
For further information, refer to the FDA guidance The Establishment and Operation 
of Clinical Trial Data Monitoring Committees for Clinical Trial Sponsors.13  
B. Techniques to Minimize Operational Bias  
Operational bias is a major concern in adaptive designs.  It can exist even in the 
group sequential setting.  In general, to reduce operational bias in studies with adaptive 
designs, one should limit the access to outcomes by coded or unblinded treatment groups.  
One way to do that is to set up firewalls that guarantee that access to such data is restricted 
only to those for whom it is absolutely essential.  This should be done if the sponsor wishes 
to retain the ability to suggest scientifically-valid changes to the design during the course of 
the study.  In addition, to limit operational bias and depending on the type of adaptation, it is 
recommended that the precise details of the adaptation algorithm be separated from the 
investigator-shared protocol and placed in a detailed Statistical Analysis Plan for the 
adaptive design.  This information may be shared with the DMC, the Institutional Review 
Board (IRB), and the FDA.  This can help maintain the scientific integrity of the study and 
reduce the ability of study observers to reverse engineer the interim study results based on 
knowledge of the adaptation protocol.   
Several examples illustrate the importance of avoiding operational bias.  In a study 
with a pre-specified sample size reassessment, someone with knowledge of the sample size 
                                                 
 
 
13http://www.fda.gov/downloads/RegulatoryInformation/Guidances/ucm127073.pdf 
  36 
 
  Contains Nonbinding Recommendations 
 
 
 
adjustment protocol and the sample size adjustment may be able to easily calculate the 
observed treatment effect at the time of adaptation.  In a study with an adaptive 
randomization ratio, the relative performance in each treatment arm can be inferred with 
knowledge of the protocol and observed study modification.  Even in a classical adaptive 
design such as a group sequential one, biases could be introduced through inference that a 
large treatment effect was not observed, since the study continues to the next stage instead of 
stopping at the interim analysis.   
Although one cannot completely eliminate such information leakage, extra care 
should be taken to control the information so that only those who must have access to it 
know about the trial modification.  For example, if the study sample size is increased after 
the interim analysis, clinical study site personnel can continue to enroll subjects and be 
notified that the final enrollment number has not been reached.  In addition, the protocol 
could specify a categorized sample size change instead of a precisely calculated change to 
make the back calculation less informative.  In an adaptive randomization design, when a 
centralized randomization mechanism is used, each clinical site can be notified of the 
treatment assignment for the next subject rather than being notified of the randomization 
ratio change.  For a group sequential trial, not all principal investigators need to know that an 
interim analysis has been performed and a decision has been made to continue the trial to the 
next stage.  A seamless analysis performed in the background ensures the study follows the 
protocol and minimizes the bias associated with the interim analysis.  Similarly, for a trial 
with an adaptive selection of primary endpoints or an adaptive change of hypotheses, 
assuming all needed variables are collected according to the preplanned protocol, the 
decision of the change does not need to be communicated to each clinical site.  
In the conduct of an adaptive design, an effective and well-documented firewall 
increases the likelihood that trial modifications will be scientifically valid, maintain integrity 
of the data and trial, and be acceptable for regulatory purposes.   
  37 
 
  Contains Nonbinding Recommendations 
 
 
 
C. Institutional Review Boards  
Institutional Review Board (IRB) oversight (21 CFR part 56) is an important 
component of assuring that human research subjects receive adequate protections before and 
during study conduct.  There are several steps that study sponsors can take in advance of 
initiating an adaptive clinical study that can minimize or avoid critical IRB-related delays 
during the study. 
As an initial step when seeking IRB approval, sponsors should clearly describe the 
adaptive nature of the study and provide an informed consent document that accurately 
reflects the studys risks and meets other informed consent requirements.  Potential planned 
adaptations should be described to the IRB and sponsors are encouraged to clearly articulate 
the circumstances under which protocol amendments will be submitted to the IRB for 
review. 
An IRBs familiarity with adaptive design clinical studies may impact the efficiency 
with which they are able to review such studies and study modifications.  For example, some 
IRBs may require the resubmission of the study protocol for full board review when an 
adaptation is made.  If pre-specified adaptations were not disclosed to the IRB during the 
initial approval process, the sponsor risks critical IRB-related delays that can hinder study 
progress.  Failure to disclose the adaptive nature of the study and its associated risks in the 
initial informed consent document may result in an IRB-mandated reconsenting of study 
subjects or subject notification related to the study modifications or identified risks.  
Advanced planning and good communication with the IRB can mitigate these 
potential IRB-related issues.  
D. Logistical Challenges  
The conduct of an adaptive clinical study creates several logistical challenges.  A robust 
infrastructure is needed to ensure that the adaptive design is implemented appropriately.  All 
parties that will be involved in the management and implementation of the study should have 
a thorough understanding of the principles of adaptive design.  Efficient and reliable data 
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  Contains Nonbinding Recommendations 
 
 
 
management should be a priority.  Mid-course changes to the sample size may create 
challenges regarding the timely availability of a sufficient number of investigational devices.  
A robust and comprehensive set of standard operating procedures to ensure that the outcome 
results remain sufficiently blinded or masked should be used.   
11.  Regulatory Considerations 
A. Interactions with FDA  
The FDA is committed to timely evaluation of clinical study protocols through its 
IDE program.  Sponsor - FDA interactions and communication are the best and most 
efficient ways to ensure that the Agency understands the sponsors plans and device 
development strategy and that sponsors understand the FDAs recommendations regarding 
maximizing study efficiency and chances for success.   
Although a study sponsor may directly submit an IDE for Agency evaluation, the 
likelihood of success is increased through interactions with the relevant FDA review division 
and statistical staff during the study planning phase.  These pre-submission meetings are 
intended to promote dialogue and interactive exchange of perspectives, and allow sponsors to 
understand the FDA expectations for a pivotal adaptive design clinical study.  The FDA 
guidance entitled Requests for Feedback on Medical Device Submissions: The Pre-
Submission Program and Meetings with Food and Drug Administration Staff14 outlines the 
procedures that sponsors can follow when seeking the FDAs feedback on specific questions 
relating to a proposed adaptive design clinical study.   
Sponsors can use this pre-submission program to obtain Agency feedback on both 
investigational studies of significant risk (SR) devices as defined in 21 CFR 812.3 (which 
require FDA approval of an IDE application) as well as studies of non-significant risk (NSR) 
  39 
 
  Contains Nonbinding Recommendations 
 
 
 
devices (which require only IRB oversight) or device studies that will be conducted outside 
of the United States (OUS).  For studies of SR devices conducted in the U.S., the adaptive 
design clinical study protocol, including the statistical analysis plan, will be recorded within 
the approved IDE and/or subsequent IDE supplements.  In the case of certain NSR and OUS 
device studies, sponsors may choose to submit the final version of the study protocol as a 
submission that incorporates Agency feedback obtained from the pre-submission, but are not 
required to do so.  Such documentation may assist in assuring a mutual understanding of the 
proposed study by the sponsor and the FDA.   
During the course of the conduct of an adaptive design clinical study involving a SR 
device, the FDA should be informed of any deviations from the planned adaptive process 
and/or procedures for maintaining study integrity in a timely fashion.15 The FDA should also 
be made aware of any breaches of the study firewall that was established and described in the 
approved investigational protocol. 
B. Sponsor Monitoring  
Sponsors are advised to have a risk-based  monitoring plan in place which focuses on 
specific aspects of adaptive studies that are of particular importance and may not be present 
in traditional (non-adaptive) trial designs.  The FDA has issued a guidance document entitled 
Guidance for Industry Oversight of Clinical Investigations:  A Risk-Based Approach to 
Monitoring16 in which the FDA recommends for all clinical investigations, adaptive or not, 
that sponsors consider adopting a risk-based monitoring approach that focuses on critical 
study parameters and relies on a combination of monitoring techniques (such as on-site 
monitoring and centralized monitoring) to oversee the study.  For adaptive studies, sponsors 
                                                                                                                                                       
 
 
14http://www.fda.gov/downloads/MedicalDevices/DeviceRegulationandGuidance/Guidance
Documents/UCM311176.pdf 
15 Please refer to 21 CFR 812.30, which describes when these changes must be submitted in 
an IDE Supplement.   
16 http://www.fda.gov/RegulatoryInformation/Guidances/ucm373750.htm 
  40 
 
  Contains Nonbinding Recommendations 
 
 
 
should have a pre-determined monitoring plan in place to ensure adequate monitoring if the 
preplanned changes do occur.  When an adaptation is planned, sponsors should consider 
adopting procedures such as preplanned site visits scheduled to verify adequate 
documentation and execution of blinding procedures in order to ensure blinding was 
appropriately maintained.  Additionally the monitoring plan should include procedures that 
confirm that data firewalls have not been breached and that statistical changes were made 
according to the study Statistical Analysis Plan.  
C. Best Practices to Protect Study Blinding (Masking) 
Sponsors should provide to the FDA, in advance, sufficient evidence of a firewall and 
documented policies and information that will ensure that personnel are appropriately 
blinded/masked during the conduct of the adaptive study.  Unplanned changes in study 
design that occur after an unblinded interim analysis of study data are not considered 
adaptive and in many cases, may undermine the scientific validity of the study.  Additional 
principles and details are available in Design Considerations for Pivotal Clinical 
Investigations for Medical Devices,4 including, in particular, Section 9, Sustaining the 
Quality of Clinical Studies, and the subsections on Handling Clinical Data, Study Conduct, 
and Study Analysis, and Anticipating Changes to the Pivotal Study. 
D. Content of an Adaptive Design Submission to the FDA 
Submissions to the FDA for an adaptive study design should clearly identify that the 
clinical study employs an adaptive design and should provide details of the proposed 
adaptations.  Information provided should address what, when, how, and why the adaptation 
will be performed.  The adaptation should be prospectively described at least generally in the 
protocol and in detail in the Statistical Analysis Plan, which should include the operating 
characteristics of the design.   
Submissions should also address key issues related to study monitoring (see Section 
11.B.) and role of the DMC (see Section 10.A.).  Decision points should be delineated and 
documented for inclusion in the final study report to be submitted as evidence of safety and 
effectiveness to the FDA. 
  41 
 
  Contains Nonbinding Recommendations 
 
 
 
If a firewall is part of the design, a mechanism and an implementation plan for the 
firewall should be provided.  If the intent is to limit the information provided to the 
investigators, a general clinical protocol and a separate detailed Statistical Analysis Plan 
(SAP) could be used, with the SAP not widely distributed.  Computer systems can be 
employed to monitor, document and limit access and can provide audit trails and firewalls. 
At the conclusion of an adaptive study, the documentation that should be sent to the 
FDA should include a description of how the adaptation was implemented, the data sets for 
the study, the baseline subject characteristics for pre and post-adaptation subgroups, the pre-
specified statistical analysis, and any deviations that may have occurred from the protocols 
adaptive plan and how they have been addressed in additional analyses.  
12.  Conclusion 
Adaptive clinical study designs for investigational medical devices can improve 
efficiency and increase the likelihood of study success when conducted in a pre-specified, 
thoughtful, scientifically-valid manner.  Adaptive studies may also optimize the treatment of 
subjects enrolled in the study and safeguard their welfare from ineffective or unsafe 
treatments and interventions at the earliest possible stage. A well-planned adaptive study, 
designed with thoughtful anticipation of situations that would warrant changes during the 
study period, can substantially improve the chances that the study will demonstrate device 
effectiveness.Procedures to ensure the proper conduct of adaptively-designed studies should 
be put into place so the study will provide valid scientific evidence that can be relied upon 
by the FDA to assess the benefits and risks of the investigational medical device.  Sponsors 
are strongly encouraged to discuss the planning of adaptive clinical study designs with the 
appropriate FDA review division in advance; the Agency has established mechanisms to 
enable such interactions in a timely and efficient manner.  
13.  References 
General 
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  Contains Nonbinding Recommendations 
 
 
 
Campbell, G., Yue, L., Gao, Y., Ho, M., Li, H., Mukhi, V., Thompson, L. (2012) Quality 
Statistical Review Checklist of Investigational Device Exemption (IDE) Submissions for 
Therapeutic and Aesthetic Medical Devices, Proceedings of the Joint Statistical Meetings, 
Biopharmaceutical Section, Alexandria, VA, American Statistical Association, 845-861. 
Group Sequential Methods 
Lan, K.K.G., and DeMets, D.L. (1983), "Discrete sequential boundaries for clinical trials." 
Biometrika 70, 659-663. 
Lan, K.K.G., and DeMets, D.L. (1987), Group sequential procedures: calendar versus 
information time. Statistics in Medicine, 8, 1191-98. 
O'Brien, P.C., and Fleming, T.R. (1979), "A multiple testing procedure for clinical trials." 
Biometrics 35, 549-556. 
Pocock, S.J. (1977), "Group sequential methods in the design and analysis of clinical trials." 
Biometrika, 64, 191-199. 
Rosenberger, W.F., Stallard,  N., Ivanova, A., Harper, C.N., and Ricks, M.L. (2001). 
Optimal adaptive designs for binary response trials. Biometrics 57, 909-913. 
Sample Size Reassessment (With or Without Group Sequential) 
Bauer, P., and Koenig, F. (2006), The reassessment of trial perspectives from interim data
a critical view, Statistics in Medicine 25, 2336. 
Chen, Y.H.J., DeMets, D.L., and Lan, K.K.G. (2004), Increasing the sample size when the 
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Cui, L., Hung, H.M., and Wang, S. (1999). "Modification of sample size in group sequential 
clinical trials." Biometrics 55, 853-857. 
Denne, J.S. (2001), Sample size recalculation using conditional power, Statistics in 
Medicine, 20, 26452660.  
Gao, P., Ware, J.H., and Mehta, C. (2008), "Sample size re-estimation for adaptive sequential 
design in clinical trials." Journal of Biopharmaceutical Statistics 18, 1184-1196. 
Jennison, C., and Turnbull, B.W. (2006) Adaptive and nonadaptive group sequential tests, 
Biometrika 93(1), 1-21.  
Lehmacher, W., and Wassmer, G. (1999), Adaptive sample size calculations in group 
sequential trials, Biometrics 55, 12861290. 
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Li, G., Shih, W.J., Xie, T., and Lu, J. (2002), A sample size adjustment procedure for 
clinical trials based on conditional power, Biostatistics 3, 277287. 
Mehta, C.R., and Pocock, S.J. (2011), "Adaptive increase in sample size when interim results 
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Proschan, M.A. and Hunsberger, S.A. (1995),  "Designed extension of studies based on 
conditional power." Biometrics 51, 1315-1324. 
Tsiatis, A.A., and Mehta, C. (2003), On the inefficiency of the adaptive design for 
monitoring clinical trials, Biometrika 90, 367378. 
Information based adaptation 
Mehta, C.R., and Tsiatis, A.A. (2001), Flexible sample size considerations using 
information based interim monitoring,   Drug Information Journal 35:1095-1112. 
Covariate Adaptive Randomization 
Efron, B. (1971), Forcing a sequential experiment to be balanced, Biometrika 58, 403-417. 
Response Randomization 
Hu, F., and Rosenberger, W. (2006).  The Theory of Response-Adaptive Randomization in 
Clinical Trials. New York: Wiley. 
Berry, D.A., Eick, S.G. (1995), Adaptive assignment versus balanced randomization in 
clinical trials: a decision analysis, Statistics in Medicine 14, 231-246. 
Bayesian (Adaptive) Design 
Berry, S.M., Carlin, B.P., Lee, J.J., and Muller, P. (2010). Bayesian Adaptive Methods for 
Clinical Trials. Boca Raton, FL: CRC Press. 
Campbell, G. (2013), Similarities and differences of Bayesian designs and adaptive designs 
for medical devices: A regulatory view, Statistics in Biopharmaceutical Research, 5, 356-
368. 
DMC 
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  Contains Nonbinding Recommendations 
 
 
 
Food and Drug Administration. (2006). The Establishment and Operation of Clinical Trial 
Data Monitoring Committees for Clinical Trial Sponsors (issued March, 2006). 
http://www.fda.gov/downloads/RegulatoryInformation/Guidances/ucm127073.pdf
Antonijevic Z, Gallo P, Chuang-Stein C, Dragalin V, Loewy J, Menon S, Miller E, Morgan 
C, & Sanchez M (2013). Views on Emerging Issues Pertaining to Data Monitoring 
Committees for Adaptive Trials, Therapeutic Innovation & Regulatory Science, 47: 495-
502. 
o Sanchez-Kam M, Gallo P, Loewy J, Menon S, Antonijevic Z, Christensen J, Chuang-Stein 
C, & Laage T (2014). A Practical Guide to Data Monitoring Committees in Adaptive 
Trials, Therapeutic Innovation & Regulatory Science, 48: 316-326. 
  45