#268 
 
Adaptive and Other Innovative 
Designs for Effectiveness Studies of 
New Animal Drugs 
 
Guidance for Industry 
 
Submit comments on this guidance at any time.  Submit electronic comments to 
https://www.regulations.gov.  Submit written comments to the Dockets Management Staff 
(HFA-305), Food and Drug Administration, 5630 Fishers Lane, Rm. 1061, Rockville, MD  
20852.  All comments should be identified with the docket number FDA-2020-D-1401. 
 
For further information regarding this document, contact AskCVM@fda.hhs.gov. 
 
Additional copies of this guidance document may be requested from the Policy and Regulations 
Staff (HFV-6), Center for Veterinary Medicine, Food and Drug Administration, 7500 Standish 
Place, Rockville MD 20855, and may be viewed on the Internet at https://www.fda.gov/animal-
veterinary, https://www.fda.gov/regulatory-information/search-fda-guidance-documents or 
https://www.regulations.gov. 
 
 
 
 
U.S. Department of Health and Human Services 
Food and Drug Administration 
Center for Veterinary Medicine (CVM) 
October 2021 Contains Nonbinding Recommendations 
 
Table of Contents 
 
I.  Introduction ....................................................................................................................... 3 
II.  Background ....................................................................................................................... 3 
III.  Scope................................................................................................................................... 4 
IV.  Adaptive Designs and Complex Adaptive Designs ........................................................ 5 
A.  General Recommendations ........................................................................................ 5 
B.  Group Sequential Design ............................................................................................ 7 
C.  Sample Size Re-estimation (SSR) .............................................................................. 8 
D.  Other Study Design Adaptations ............................................................................... 9 
E.  Combining Several Adaptive Features ................................................................... 11 
V.  Other Innovative Designs ............................................................................................... 11 
A.  Enrichment Strategies .............................................................................................. 11 
B.  Designs to Mitigate the Placebo Effect .................................................................... 15 
C.  Expansion Cohorts .................................................................................................... 16 
D.  Master Protocols ....................................................................................................... 16 
E.  Bayesian Adaptive Designs....................................................................................... 17 
VI.  Other Considerations...................................................................................................... 18 
A.  Protocol ...................................................................................................................... 18 
B.  Documentation .......................................................................................................... 18 
VII.  Obtaining CVM Feedback on Use of Adaptive and Other Innovative Designs ........ 19 
A.  When to submit information regarding the use of adaptive and other innovative 
designs ........................................................................................................................ 19 
B.  How to submit information regarding the use of adaptive and other innovative 
designs ........................................................................................................................ 20 
VIII.  Glossary ........................................................................................................................... 21 
VIX.  References ........................................................................................................................ 22 
  2  Contains Nonbinding Recommendations 
 
Adaptive and Other Innovative Designs for Effectiveness Studies of 
New Animal Drugs 
 
Guidance for Industry 
 
This guidance represents the current thinking of the Food and Drug Administration (FDA or 
Agency) on this topic.  It does not establish any rights for any person and is not binding on FDA 
or the public.  You can use an alternative approach if it satisfies the requirements of the 
applicable statutes and regulations.  To discuss an alternative approach, contact the FDA staff 
responsible for this guidance as listed on the title page. 
 
I.  Introduction 
FDA is issuing this Guidance for Industry (GFI), as required under section 305 of the Animal 
Drug and Animal Generic Drug User Fee Amendments of 2018 (Pub. L. 115-234), to assist 
sponsors in incorporating complex adaptive and other novel investigation designs into proposed 
clinical investigation protocols and applications for new animal drugs under the Federal Food, 
Drug, and Cosmetic Act (FD&C Act).  Section 305 of Pub. L. 115-234, among other things, 
directed FDA to hold a public meeting for interested parties to discuss innovative animal drug 
investigation designs and to issue guidance addressing the incorporation of the use of such 
elements of investigations as complex adaptive and other novel investigation designs, data from 
foreign countries, real-world evidence (including ongoing surveillance activities, observational 
studies, and registry data), biomarkers, and surrogate endpoints into clinical investigation 
protocols and applications to support the effectiveness of new animal drugs. 
 The contents of this document do not have the force and effect of law and are not meant to bind 
the public in any way, unless specifically incorporated into a contract. This document is intended 
only to provide clarity to the public regarding existing requirements under the law. FDA 
guidance documents, including this guidance, should be viewed only as recommendations, unless 
specific regulatory or statutory requirements are cited.  The use of the word should in Agency 
guidances means that something is suggested or recommended, but not required. 
II.  Background 
In the Federal Register of July 9, 2019 (84 FR 32749), FDAs Center for Veterinary Medicine 
(CVM) published a notice of a public meeting entitled Incorporating Alternative Approaches in 
Clinical Investigations for New Animal Drugs giving interested persons until August 17, 2019, 
to comment on the topics discussed at the public meeting and the questions published in the 
meeting notice (84 FR at 32750-32751).1  On August 13, 2019, we published a notice 
announcing the extension of the comment period to September 16, 2019 (84 FR 40071).  CVM 
 
1 https://www.fda.gov/animal-veterinary/workshops-conferences-meetings/public-meeting-incorporating-alternative-
approaches-clinical-investigations-new-animal-drugs 
  3  Contains Nonbinding Recommendations 
received numerous comments on the topics discussed at the public meeting and the questions 
published in the meeting notice.  Those comments were considered as draft guidance was 
developed. 
This document describes recommendations for designing, conducting, and reporting the results 
for investigations or studies including adaptive design features to demonstrate substantial 
evidence of effectiveness or a reasonable expectation of effectiveness of drugs intended for use 
in animals and to support the approval of a new animal drug application (NADA) or an 
application for conditional approval of a new animal drug (CNADA).2  This guidance also 
provides information about obtaining feedback from CVM with respect to incorporating adaptive 
design features in investigations and study protocols for new animal drugs.  Other centers within 
FDA, including the Center for Drug Evaluation and Research (CDER), the Center for Biologics 
Evaluation and Research (CBER), and the Center for Devices and Radiological Health (CDRH), 
have released guidance documents on the topics of adaptive design and other innovative designs, 
such as master protocols, enrichment strategies, and the use of Bayesian statistics. 
CVM will consider all established and accepted adaptive design methodologies in submissions to 
investigational new animal drug (INAD) files, new animal drug applications (NADA), and 
applications for conditional approval of a new animal drug (CNADA) to demonstrate substantial 
evidence of effectiveness or a reasonable expectation of effectiveness.  This guidance document 
provides CVMs recommendations specific to investigations for animal drugs. 
Some concepts and language in the recommendations for animal drugs are intended to be similar 
or the same as those in other guidance documents issued by FDA on the same or similar topics.  
Because these recommendations are specific to investigations for animal drugs, they have been 
tailored to the unique aspects of and considerations for animal drug development. 
III.  Scope 
The purpose of this guidance is to provide recommendations to animal drug sponsors for whom 
it may be beneficial to use adaptive and other innovative designs to demonstrate substantial 
evidence of effectiveness or a reasonable expectation of effectiveness of drugs intended for use 
in animals and to support the approval of an NADA or a CNADA.  The general 
recommendations may also be applied to non-pivotal studies (e.g., pilot studies and exploratory 
studies).  This guidance does not address the use of adaptive and other innovative designs to 
support technical sections other than Effectiveness or Reasonable Expectation of Effectiveness.  
In addition, this guidance describes how sponsors may obtain feedback from CVM on technical 
issues related to the use of adaptive and innovative designs before the submission of an 
application. 
The decision to use adaptive or innovative elements in a clinical effectiveness study design will 
depend on a number of factors, including the potential advantages and limitations described later 
in this guidance.  CVM encourages sponsors to explore a variety of design options in the 
planning of clinical effectiveness study designs, and to proactively discuss their considerations 
 
2 21 CFR 514.4; 21 U.S.C. 360ccc(a)(2)B) 
  4  Contains Nonbinding Recommendations 
with CVM.  Some recommendations in this guidance may be technically involved and we 
recommend you consult with the appropriate CVM experts to help facilitate use of these 
recommendations. 
The following sections outline considerations specific to adaptive and other innovative designs 
for effectiveness studies for new animal drugs.  
IV.  Adaptive Designs and Complex Adaptive Designs 
For the purposes of this guidance, an adaptive design is defined as a clinical effectiveness study 
design that allows for prospectively planned modifications to one or more aspects of the design 
based on accumulating data from subjects in the study.  These modifications may affect sample 
size, intended target population, treatment arm selection, allocation to treatments, endpoint 
selection, and other design features.  A complex adaptive design may include more than one of 
these modifications. 
A.  General Recommendations 
An adaptive clinical study should be consistent with the following general 
recommendations when it is intended to provide substantial evidence of effectiveness or a 
reasonable expectation of effectiveness.  There should be procedures to adequately 
control the chance of erroneous conclusions and minimize the risk of statistical and 
operational biases.  Additionally, the details of the study design should be pre-specified. 
Study adaptations from one or more interim analyses can introduce multiplicity concerns, 
which refers to the potential inflation of the Type I error rate as a result from multiple 
testing (Type I error is the probability of erroneously concluding an effect when the truth 
is that there is no effect).  Multiplicity concerns can arise from repeated testing of 
multiple endpoints, subgroups, or interventions (e.g., dosages).  All adaptive study 
proposals should address the possibility of inflating the Type I error probability and 
control for this error.  In order to ensure that the study has adequate power, the proposal 
should also consider Type II error (Type II error is the probability that a true effect will 
not be detected by the test).  The likelihood of these types of erroneous conclusions 
should be investigated as part of the examination of the operating characteristics of the 
design.  Analytical statistical methods and simulations can be used to evaluate the 
operating characteristics of an adaptive study design. 
Some adaptive design elements, such as response adaptive randomization, can lead to 
statistical bias in the estimation of treatment effects and related quantities such as p-
values, confidential intervals, etc.  Also, operational bias3 may be introduced when the 
changes resulting from the interim analysis results are known (e.g., increased sample 
size) to study participants, resulting in different behaviors before and after the interim 
 
3 For the purposes of this guidance, operational bias is the bias that arises if some or all participants (e.g., 
investigators, owners, and caretakers) in the study have access to study results and this information influences the 
ongoing operation of the study. 
  5  Contains Nonbinding Recommendations 
analysis.  It is important to reduce the potential for statistical and operational bias because 
these biases can distort the findings of a study and undermine its scientific validity.  For 
some designs there are known methods for adjusting estimates to reduce or remove bias 
associated with adaptations (Jennison and Turnbull, 1999; Wassmer and Brannath, 2016).  
However, operational bias may not be quantifiable and cannot be overcome by statistical 
adjustments to account for its presence.  Measures to reduce the potential for bias (e.g., 
masking procedures and controlling access to interim results) should be developed during 
study design. 
To help avoid such biases, the details of an adaptive design should be completely pre-
specified in the protocol prior to initiation of the study.  The protocol should describe the 
number and timing of the interim analyses, the analysis methods to be used, and the 
type(s) of adaptations, the specific algorithm governing adaptation decision(s), and how 
information from interim analyses will be controlled.  Complete pre-specification is 
critical to ensure that appropriate statistical methods can be applied to control the chance 
of erroneous results and provide reliable estimates for treatment effect.  Unplanned 
adaptations based on accumulated data may compromise the validity of the statistical 
inference and interpretability of the results. 
1.  Advantages 
There are advantages that an adaptive design can provide over a non-adaptive design. 
Some of these advantages of a successful study adaptation are due to promotion of 
some of the principles of the three Rs in animal research:  Replacement, Reduction, 
and Refinement (Russell and Burch, 1959).4 
  Design adaptations can improve efficiency of studies by saving time, money, and 
resources, as well as supporting the reduction principle for animal research.  A 
study with interim analyses could stop early for effectiveness or lack of 
effectiveness (futility).  In some cases, an adaptive design can provide a greater 
chance to detect a true drug effect (i.e., greater statistical power) than a 
comparable non-adaptive design.  For example, when assumptions regarding the 
parameters used for designing the study are somewhat uncertain, the study may 
benefit from an adaptive approach to re-estimate sample size based on interim 
data.  Sample size re-estimation can correct an under-powered study and improve 
the chance of detecting a clinically relevant effect, thus potentially avoiding the 
cost, and further risk to animals, with a new clinical effectiveness study.  
  An adaptive design can provide ethical advantages over a non-adaptive design, 
consistent with the reduction principle.  For example, the ability to stop a study 
early, if it becomes clear that the study is unlikely to demonstrate effectiveness, 
can reduce the number of animals exposed to the unnecessary risk of an 
ineffective investigational treatment. 
 
4 We support the principles of the 3Rs. The concept of adaptation is to potentially reduce the number of animals and 
to make prospective refinements to the study design to increase inferential value. 
  6  Contains Nonbinding Recommendations 
  An adaptive enrichment design may make it possible to demonstrate effectiveness 
in either a given population of animals or a targeted subgroup of that population, 
where a non-adaptive alternative might require infeasibly large sample sizes, and 
potentially expose certain subgroups of animals to treatments that are unlikely to 
be beneficial to those subgroups. (See section V.A. Enrichment Strategies.) 
2.  Limitations 
The following are some of the possible limitations associated with an adaptive design 
study: 
  Adaptive study designs that are overly complicated can be difficult to plan.  Pre-
planning adaptive design modifications can require more effort at the design 
stage.  Additionally, analytical methods to investigate the operating characteristics 
of the design and establish adequate control of erroneous conclusions may not be 
readily available or implementable. 
  The use of an adaptive design adds logistical challenges in ensuring appropriate 
study conduct and study integrity.  Approaches to appropriately limit access to 
interim results may be complex and add to study costs. 
  If not done correctly, adaptive designs can introduce operational or statistical bias, 
making it difficult to characterize the true effect of the investigational new animal 
drug. 
  An adaptive change to a study design may lead to results before the adaptation 
that substantively differ from the results after the adaptation that may lead to 
challenges in interpretability of the overall results.  These differences in the 
results should be addressed in the final study report.  See section VI.B. 
Documentation. 
  Adaptations predominantly benefit studies where outcomes are assessed shortly 
after enrollment so that study changes have time to take effect on future 
enrollments. 
  The maximum sample size and/or study duration with an adaptive design may be 
greater than a non-adaptive design. 
B.  Group Sequential Design 
Group sequential designs allow the total sample size of the study to be flexible, as data is 
sequentially evaluated over time.  These designs allow for one or more prospective 
interim analyses of the outcomes that use treatment group information, with pre-specified 
criteria for stopping the study for success or futility.  This design can provide ethical and 
efficiency advantages by reducing the expected sample size and calendar time of studies.  
  7  Contains Nonbinding Recommendations 
If there is a real possibility that the treatment may perform better than expected, using a 
group sequential design may be considered to allow for the possibility of stopping the 
study early due to successful demonstration of effectiveness.  The preferred approach 
would be to design an adaptive study to allow for an interim look with a possibility of 
stopping early if there is a sufficient number and composition of subjects to support a 
scientifically valid inference to the target population. 
In a group sequential study, performing each of the multiple statistical hypothesis tests 
for effectiveness would inflate the Type I error probability and increase the chance of 
erroneous conclusions.  Therefore, the design should include a pre-specified statistical 
plan that accounts for the interim analyses and makes appropriate adjustments to 
distribute the alpha so that the overall Type I error is controlled.  For example, the 
OBrien-Fleming approach tends to require very persuasive early results to stop the study 
for effectiveness (OBrien and Fleming, 1979).   Approaches such as those proposed by 
Pocock require less persuasive early results and have higher probabilities of early 
stopping (Pocock, 1977).  The Lan-DeMets alpha-spending approach allows for 
specifying a function for how the Type I error probability is spent throughout the study, 
while also allowing for flexibility in determining the number and timing of interim 
analyses (Lan and DeMets, 1983). 
It is important to adhere to the prospective analysis plan and terminate the study only if 
the stopping criteria are met.  In some cases, there should be a limit on how early group 
sequential interim analyses can occur or whether they should occur at all because a 
minimum sample size is needed for generalizability of effectiveness results to the target 
population, for inferential value and independent substantiation of evidence, as well as a 
reliable evaluation of safety.  
C.  Sample Size Re-estimation (SSR) 
One common study adaptation is to prospectively modify sample size based on interim 
analysis results.  SSR may be conducted with or without using treatment group 
information (also called unblinded versus blinded SSR). 
Adaptive design using SSR can help avoid under-powering studies, particularly in 
situations where substantial uncertainty exists concerning the variance or effect size.  For 
example, the SSR could indicate that a larger sample size is needed because the effect 
size is more modest, although still clinically relevant, than initially anticipated.  It is 
crucial that the discussion concerning the clinically important effect size occur 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 a completed study that has failed to 
provide conclusive results, but instead can help prevent a study from failing to provide 
conclusive results in the first place. 
  8  Contains Nonbinding Recommendations 
SSR that does not use treatment group information is generally believed to have limited 
or no effect on the Type I error probability.5  However, SSR that uses treatment group 
information, like the group sequential design, may inflate the Type I error probability.  
Specifically, if the SSR results in a sample size increase, the Type I error rate may be 
inflated, depending on the conditional power of the study at the time of SSR and the 
percent of the sample size increment (Chen et al., 2004; Mehta and Pocock, 2011; 
Broberg, 2013).  There are a variety of existing methods that can be used to appropriately 
control Type I error probability.  For example, hypothesis testing approaches have been 
developed based on combining test statistics or p-values from the different stages of the 
study in a preplanned manner or through preservation of the conditional Type I error 
probability (Bauer and Kohne, 1994; Fisher, 1998; Cui et al., 1999; Denne, 2001; Muller 
and Schafer, 2001; Chow and Chang, 2011). 
Under certain conditions (Chen et al., 2004; Mehta and Pocock, 2011), SSR without the 
intention to decrease the sample size may be acceptable without statistical adjustment for 
Type I error.   In this case, the SSR should be performed by an independent party with the 
only allowable outcomes being stopping for futility, continuing as is, or increasing the 
sample size. 
In addition to Type I error rate control, there are also challenges in maintaining study 
integrity in the presence of sample size adaptation.  Usually, knowledge of the adaptation 
rule and the adaptively chosen sample size allows at least a qualitative deduction of the 
interim estimate of treatment effect.  Operational bias might be introduced into the study 
if such knowledge were somehow revealed to the investigators and lead to deviations 
from the intended study protocol.  Therefore, additional steps should be taken to limit 
personnel with this detailed knowledge so that study integrity can be maintained.  For 
example, one way to reduce the risk of this type of operational bias is by using an 
independent statistician for SSR in accordance with the prespecified interim analysis 
procedures. 
The additional considerations that were discussed in the section on group sequential 
designs regarding adherence to the adaptation plan, the evaluation of safety, and 
generalizability of conclusions also apply to designs with sample size adaptations that use 
treatment group information.  The design should include pre-specification of the 
statistical hypothesis testing method that will be used and the specific rules governing the 
sample size modification.  
D.  Other Study Design Adaptations 
Other study design adaptations may be considered as appropriate and are briefly 
discussed here. 
1.  Adaptive treatment arm selection 
 
5 Some exceptions may apply to noninferiority and equivalence tests where SSR, even if it does not use treatment 
group information, may lead to some inflation of the Type I error rate (Friede and Kieser, 2003). 
  9  Contains Nonbinding Recommendations 
In cases where more than one dose (or dose regimen) may be effective, an adaptive 
design with sequential analyses allowing early termination of one or more treatment 
arms may be more efficient than a non-adaptive design (similar to expansion cohorts 
described below).  Such an adaptive design could, in principle, allow interim 
modifications to additional aspects of the design, such as the number of additional 
animals that will be enrolled and the randomization ratio for the treatment arms 
carried forward.  
Statistical hypothesis testing methods (e.g., multiplicity adjustments) should account 
for the adaptive selection of a best dose or doses from among the multiple doses 
evaluated in the study, as well as any additional adaptive modifications, such as the 
potential to stop the study early or to modify sample size. 
2.  Adaptations to endpoint selection 
This is a design that allows adaptive modification to the choice of the primary 
endpoint based on interim results that use treatment group information.  Such a design 
might be motivated by uncertainty about the treatment effect sizes on multiple 
treatment outcomes that would be considered acceptable primary endpoints by CVM.  
As with other adaptive designs, the adaptation rule should be pre-specified, and 
statistical hypothesis testing methods should account for the adaptive endpoint 
selection, including appropriate alpha adjustments to control for Type 1 error.  
Because endpoint selection involves important clinical considerations, early 
discussions with CVM is recommended when such designs are being considered.  In 
particular, the criteria for selecting the primary endpoint should also consider the 
clinical relevance of the magnitude of the treatment effects observed at the interim 
analysis. 
3.  Adaptations to subject allocation 
There are two types of adaptations to subject allocation:  adaptations based on 
comparative baseline characteristics data and adaptations based on outcome data that 
use treatment group information (e.g., response-adaptive randomization).  
A covariate-adaptive treatment assignment is an adaptation intended to promote 
balance between treatment groups on baseline covariates.  In this adaptation, a 
subjects treatment assignment may depend on its baseline characteristics, (e.g., 
breed, gender, disease type or stage) and the baseline characteristics and treatment 
assignments of previously enrolled subjects.  Studies that employ covariate adaptive 
treatment assignments should utilize techniques to reduce predictability of treatment 
assignments.  Additionally, results should be analyzed using randomization or 
permutation tests or other appropriate statistical methods that control for Type I error 
probability. 
Subjects can also be assigned to treatment using response-adaptive randomization, in 
which the chance of a newly-enrolled subject being assigned to a treatment arm varies 
over the course of the study based on accumulating outcome data for subjects 
  10  Contains Nonbinding Recommendations 
previously enrolled.  There are potential statistical, ethical, and pragmatic advantages 
in a design where a newly-enrolled subject is more likely to be assigned to a 
treatment arm with favorable outcomes (Rosenberger and Lachin, 1993).  However, 
concerns have been raised about using inconclusive interim results to alter 
randomization in an ongoing study (Hey and Kimmelman, 2015).  Sponsors should 
discuss with CVM the potential advantages and risks associated with a specific 
proposed design at the planning stage. 
Response-adaptive randomization alone does not generally increase the Type I error 
probability of a study when used with appropriate statistical analysis techniques.  It is 
important to ensure that the analysis methods appropriately take the design of the 
study into account. 
4.  Adaptations in time-to-event studies 
There are additional considerations specific to adaptive studies in which the primary 
endpoint is the time to occurrence of a certain event, such as time to death or time to 
progression.  In these studies, power is dependent on the number of events rather than 
the number of subjects.  It is, therefore, common to target a fixed number of events 
rather than a fixed number of subjects.  Sample size adjustment in these studies has 
the purpose of modifying the number of events and, therefore, may take the form of 
modifying the number of subjects, the length of the follow-up period for each subject, 
or both. 
E.  Combining Several Adaptive Features 
It is possible to employ more than one adaptation in a study.  For example, a group 
sequential design for a study with more than one dose that includes interim looks for 
potential stopping of treatment arm(s) can also include a sample size reassessment.  
Typical group sequential testing methods can be used, along with a multiple testing 
approach to control the Type I error probability across the multiple doses evaluated.  
Studies with multiple adaptations should be carefully considered, because the operating 
characteristics of these complex studies may be difficult to ascertain.  Additionally, study 
logistics and the statistical analysis will also increase in complexity, along with higher 
risks of operational bias or unforeseen complications in study implementation.  
V.  Other Innovative Designs 
A.  Enrichment Strategies 
The purpose of enrichment strategies is to select the study population in which the 
potential effect of a drug can more readily be demonstrated.  Enrichment is the 
prospective use of any characteristic to select a study population in which detection of a 
drug effect (if one is in fact present) is more likely than it would be in an unselected 
population.  These characteristics are over and above the typical characteristics used as 
inclusion criteria.  Enrichment is directed at improving the ability of a study to detect a 
drugs effectiveness, resulting in increased study efficiency or feasibility in drug 
  11  Contains Nonbinding Recommendations 
development.  Depending on the enrichment strategy selected, specific label language 
may be needed regarding the enrichment factor, including appropriate limitations, if the 
enrichment factor is important for informing the end user of the appropriate use of the 
drug in the intended target population. 
The enrichment strategies are discussed primarily in the context of randomized controlled 
studies.  Enrichment strategies are prospectively planned and fixed prior to study 
initiation.  These strategies generally do not compromise the statistical validity of the 
studies or the meaningfulness of the conclusions reached for the population studied.  A 
study intended to provide substantial evidence of effectiveness to support approval could 
include a broad range of subjects but be prospectively designed to evaluate in its primary 
analysis the effect in the enriched population subset. 
The principal concerns with the use of enrichment strategies are the generalizability and 
applicability of the study results to the population expected to receive the drug once 
approved (i.e., inferential value to the intended target population).  Given the potentially 
complex interpretation of studies using enrichment designs, plans to use them should be 
discussed with CVM early in product development. 
Three broad goals of enrichment strategies are described listed below.  
1.  Decrease variability 
Approaches to increasing study power (the ability of a clinical study to demonstrate a 
true treatment effect) by decreasing heterogeneity (nondrug-related variability) 
include the following: 
  Selecting subjects with consistent baseline measurements used to diagnosis the 
disease of interest or using baseline measurements within a narrow range. This 
type of selection is intended to decrease variability within or among study 
animals; 
  Defining entry criteria carefully to ensure that enrolled subjects actually have the 
disease that is being studied; 
  Using a challenge infection or infestation in a model study to produce a study 
population with a more consistent level of disease or infestation; 
  Using placebo lead-in periods before randomization to eliminate subjects that 
improve spontaneously, have large placebo responses, or improve for reasons 
other than response to treatment; 
  Identifying and selecting study animals/owners likely to adhere to treatment 
administration to decrease variability in drug exposure; 
  Excluding study animals unlikely to tolerate the drug; 
  12  Contains Nonbinding Recommendations 
  Excluding study animals/owners likely to drop out for non-medical reasons (e.g., 
pet owners who have difficulty bringing the pet to the study site for follow-up 
evaluations); and 
  Excluding study animals with a comorbid illness that would make completing the 
treatment period unlikely. 
Some strategies to decrease variability can result in studies that provide too little 
inferential value to the intended target population that will receive a drug in clinical 
practice. Therefore, prior to study conduct, the limitations of using these strategies 
should be carefully considered and balanced against the need for information in the 
broader population. 
2.  Prognostic enrichment strategies 
Prognostic enrichment strategies are designed to increase the proportion of study 
animals likely to have a particular disease-related endpoint event or a worsening 
condition, in other words identifying study animals at high risk.  These strategies 
allow a treatment effect to be more readily determined.  Drugs more frequently 
evaluated using this design are those intended to slow disease development or 
progression.  Prognostic enrichment is intended to increase the number of events 
occurring within a shorter time period, generally allowing for a smaller sample size.  
For any given desired power in an event-based study, the appropriate sample size will 
depend on effect size and the event rate in the placebo group. 
Prognostic indicators include clinical and laboratory measures, medical history, 
genomic, and/or proteomic measures, among others.  For example: 
  Animals with cardiovascular disease can have clinical findings more often 
associated with rapid worsening of their disease; 
  Animals with certain stages of cancer that is more likely to progress; or 
  Farm history may be used to select a herd at high risk to evaluate drugs intended 
to control an infectious disease such as bovine respiratory disease. 
3.  Predictive enrichment strategies 
Predictive enrichment strategies are designed to include subjects that are more likely 
to respond to the treatment than other subjects with the same condition being treated. 
Identification of a population with a high rate of response or a larger response 
increases the chance that a study evaluating an effective investigational new animal 
drug will be able to detect a treatment effect, if one exists.  Such selection can lead to 
a larger effect size and can permit use of a smaller study population than would be 
needed for a study in an unselected population. 
Identifying a more responsive population does not necessarily indicate that a benefit 
does not exist for the remaining population.  It may be desirable to include and collect 
  13  Contains Nonbinding Recommendations 
data on the non-selected (non-enriched) population to determine the response rate in 
that population.  These data also can provide an assessment of safety in the non-
selected population if a broader population of animals is expected to receive the drug 
after approval. 
There are many possible ways to identify study animals more likely to respond to a 
particular treatment, such as selection of study animals based on a specific aspect of 
pathophysiology, past history of response to a similar class of drugs, a biomarker, or a 
disease characteristic that is related in some way to the study drugs mechanism of 
action.  Identification of a responder population may avoid exposure and potential 
toxicity in study animals that would not benefit from the drug.  The strategy may be 
useful for pilot effectiveness studies or for studies that may demonstrate a reasonable 
expectation of effectiveness for a conditional approval. 
For example: 
  Antibacterial drug effects are evaluated only in study animals from which a target 
organism has been detected.  Study animals may have been randomized before the 
specific infecting organism is known.  The detection of the target organism from 
the study animal is used as a baseline measure or characteristic for inclusion of 
the case in the final analysis, even though the assessment occurs post-
randomization. 
  Enroll study animals appearing to have previously responded to a drug in the 
same class (e.g., dogs that previously responded to an NSAID are enrolled when 
evaluating an investigational new NSAID in dogs). 
  Protein or genetic markers related to a drugs mechanism of action can be used to 
identify potential responders.  For example, a drug targeting the cKit mutation in 
mast cell tumors may be more effective in the study population with cKit 
mutations. 
  A protein or genetic marker shown to predict response, even without a 
documented mechanism of action. 
Enrichment Study Design Considerations 
The study protocol should explicitly describe an enrichment design.  For both 
prognostic and predictive enrichment factors (markers), understanding the accuracy 
and performance characteristics of the method used to identify subjects or marker-
defined subgroups for enrichment is important.  In addition to assay validity, for any 
biomarker used to select subjects, even a familiar one, the biomarkers clinical 
  14  Contains Nonbinding Recommendations 
sensitivity, specificity, and positive and negative predictive values should be well 
characterized.6 
The principal concerns with the use of enrichment strategies are the generalizability 
and applicability of the study results to the population expected to receive the drug 
once approved (the intended target population).  Enrichment strategies will typically 
result in a study population that is a well-defined subset of the broader population for 
which a new animal drug may be intended or in which an approved drug may be 
used.  The expected response rate in the broader population may be different than that 
in the enriched study population, particularly when the enriched population is 
expected to be more responsive.  The inference of the study results and the best use of 
the drug in the broader population should be carefully considered. 
A critical question when enrichment is used is whether to include the marker-negative 
population in the study, particularly with predictive enrichment designs. Studies can 
be designed to include either: (1) only marker-positive subjects; or (2) both marker-
positive and marker-negative subjects.  If the marker-negative subjects are not 
expected to respond, their inclusion may dilute the effect response.  Also, the 
presence of significant toxicity associated with the drug should be considered when 
using it in a marker-negative study population that may not respond to the drug. 
B.  Designs to Mitigate the Placebo Effect 
One significant problem in many placebo-controlled studies for the clinical assessment of 
investigational new animal drugs intended for symptomatic improvement, such as pain, is 
a high rate of placebo response, also called placebo effect.  High placebo responses may 
induce false expectations regarding the drugs effectiveness and result in an erroneous 
conclusion that a drug is ineffective when the drug actually is effective.  This section 
discusses some proposed study designs to mitigate the placebo effect, including placebo 
lead-in studies and randomized withdrawal studies.  In addition, Ivanova and Tamura 
(2015) proposed a two-way enriched design, which combine the placebo lead-in design 
and the randomized withdrawal design. 
1.  Placebo lead-in studies 
One possible approach to mitigate the placebo effect is a placebo lead-in design (also 
referred to as placebo run-in).  In this design, all subjects receive placebo first, which 
is referred to as the lead-in period.  After this period, only subjects with signs or 
symptoms that remain above some threshold value (i.e., placebo non-responders) 
remain in the study and are randomized to receive drug or placebo.  Subjects who 
show a meaningful symptomatic reduction are excluded from study.  The intention is 
that by eliminating subjects that would respond to placebo before randomization, 
there should be a reduction in the percentage of subjects responding among those 
 
6 See CVM GFI #267, Biomarkers and Surrogate Endpoints in Clinical Studies for New Animal Drugs, (October 
2021) 
  15  Contains Nonbinding Recommendations 
randomized to the post-randomization placebo arm (Faries et al., 2001).  Also, 
through the lead-in period, because many signs and symptoms vary spontaneously, 
subjects with initial screening values representing random high intensity of the 
disease condition then showing regression to the mean may be identified and 
excluded. 
2.  Randomized withdrawal studies 
Another potential design to mitigate the placebo effect is a randomized withdrawal 
design.  In a randomized withdrawal study, subjects that have an apparent response to 
treatment in an open-label period or in the treatment arm of a randomized study are 
randomized to continued drug treatment or to placebo treatment.  The logic behind 
the design is that a subject that has shown symptomatic improvement to an active 
drug is more at risk to lose that benefit when switched to placebo as opposed to 
remaining on drug (Ivanova and Tamura, 2015).  Because such studies generally 
involve only subjects that appear to have responded, this is a study enriched with 
apparent responders.  A randomized withdrawal design in which the study population 
is on treatment for an extended duration followed by masked, randomized withdrawal 
of treatment for a short duration can provide evidence of prolonged effectiveness with 
only brief exposure to the placebo.  Also, this design can allow a subject to be 
removed from the study (for having reached an endpoint) when the condition returns 
at a specified severity, avoiding long-term exposure to an ineffective treatment. 
C.  Expansion Cohorts 
Expansion cohort study designs are studies that use multiple, concurrently accruing study 
animal cohorts, where individual cohorts assess different aspects such as the safety, 
pharmacokinetics, or anti-tumor activity of the drug.  These studies can utilize a single 
protocol with different cohorts that have cohort-specific objectives.  For example, 
objectives can be to assess anti-tumor activity for different tumor types or stages of 
disease, assessment of different doses for safety or effectiveness, evaluation of different 
dosing schedules, or evaluation of a predictive value for a potential biomarker.  In 
general, comparison of activity between cohorts is not planned except where a 
prespecified randomization and analysis plan are part of the protocol design. 
Challenges associated with use of expansion cohorts include timely dissemination of 
safety information to the clinical investigators including updating owner informed 
consent forms, exposure of enrolled subjects to potentially toxic or ineffective therapies 
based on the dose assessed, and potential inappropriate interpretation of study cohort 
results.  Typically, these study designs should not be used for investigational new animal 
drugs with a narrow margin of safety. 
The protocol should incorporate a priori, stopping rules based on lack of effectiveness or 
toxicity.  The protocol should also address planned sample size for each cohort and 
adaptions to the sample size in each cohort. 
D.  Master Protocols 
  16  Contains Nonbinding Recommendations 
A master protocol is a single protocol with multiple sub-studies designed to evaluate 
multiple hypotheses regarding one or more investigational new animal drugs in one or 
more (sub)population(s) within the overall study structure.  Subpopulations may be 
defined by disease subtypes, tumor types, histologic types, biomarkers, etc.  The 
study(ies) may be exploratory (dose finding) or support substantial evidence of 
effectiveness, may have one common control arm or multiple control groups, and may be 
fixed or adaptive.  Examples of master protocols are basket designs and umbrella designs 
which are described below.  
These protocols provide flexibility during the study to allow adaptation such as 
discontinuation of ineffective therapies, adjustments in randomization, or changes in 
standard of care.  These studies can share a control group which may reduce the number 
of animals enrolled in the overall study. 
Challenges associated with master protocols include potential over-interpretation of the 
findings due to the multiple study groups; attribution of adverse events to one or more 
investigational new animal drugs when multiple drugs are administered within arms and 
the study does not employ a single control arm; or limited assessment of the safety profile 
of one of the investigational new animal drugs. 
1.  Basket designs 
A master protocol for a basket design evaluates a single investigational new animal 
drug in different populations defined by, for example, tumor type, disease stage, 
histology, number of prior therapies, or genetic or other biomarkers. 
2.  Umbrella designs 
A master protocol for an umbrella design evaluates multiple investigational new 
animal drugs in a single disease population.  These studies can use randomized 
controlled study arms to compare the activity of the investigational new animal drugs 
with a common control group. 
E.  Bayesian Adaptive Designs 
The term Bayesian adaptive design refers to clinical study designs that use Bayesian 
statistical reasoning to facilitate the studies in various ways (Berry, et al., 2010).  Some 
examples are: 
  Use of Bayesian predictive models to plan the timing and decision criteria for interim 
analyses; 
  Use of external information (e.g., previous studies, observational studies, case studies, 
or expert opinion) via informative prior distributions to improve the efficiency of a 
study; and 
  Use of posterior probability distributions to form study success criteria. 
  17  Contains Nonbinding Recommendations 
Different from studies based entirely on Bayesian inference, a Bayesian adaptive design 
is inherently a frequentist study governed by Type I error control and power 
consideration.  Therefore, the same statistical principles apply to Bayesian adaptive 
designs as to adaptive designs without Bayesian features.  As with any complex adaptive 
design proposal, early discussion with CVM is recommended for Bayesian adaptive 
designs that formally borrow information from external sources. 
One common usage of most Bayesian adaptive designs is to establish the operating 
characteristics of the study through numerical simulations.  Many simulations in 
Bayesian methods rely on computationally demanding algorithms such as Markov chain 
Monte Carlo.  It is sometimes advisable to use less resource-intensive techniques such as 
conjugate priors to overcome the limitation.  
VI.  Other Considerations 
Pre-planning and meetings with CVM  As stated above, adaptive designs should be 
planned a priori and incorporated into the study protocol.  CVM recommends that 
sponsors discuss these potential designs with CVM early in the development phase of the 
investigational new animal drug.  See section VII. Obtaining CVM Feedback on Use of 
Adaptive and Other Innovative Designs. 
A.  Protocol 
The protocol should clearly specify the type of adaptation or enrichment strategy 
proposed during the study conduct, provide appropriate methods to control for bias, and 
control Type I error.  If the study will utilize an interim analysis, the protocol should 
specify if a third party will be involved with assessing the adaptation and whether the 
assessment will be conducted with prior knowledge of the treatment assignments.  If 
treatment assignments are known by the third party, the protocol should specify how the 
results will be communicated to the sponsor and clinical investigators to minimize bias 
and protect masking. 
B.  Documentation 
During the planning and design stage, study simulations are likely needed to choose the 
number and timing of interim analyses and adaptations, and to evaluate study operating 
characteristics.   These simulations should be carefully constructed over a reasonable 
range of study scenarios and statistical assumptions to demonstrate that the operating 
characteristics, for example Type I error and power, meet the desired levels. 
It is important that sponsors document the simulations that support the selected design.  
CVM will review the documentation to confirm that the estimates were taken over a 
robust set of conditions and may also perform its own simulations. 
The final study report should fully detail the adaptive design, enrichment strategies, or 
other features used in the study and their effect on the interpretation of results.  The final 
study report should address potential reasons and explanations for differences in pre- and 
  18  Contains Nonbinding Recommendations 
post-adaptation results, such as changes in the study populations or introduction of 
operational biases.  See section IV.A.2. Limitations. 
An important consideration in the conduct of these simulations is the choice of software.  
FDA does not endorse any particular software package.  However, there are advantages 
to using commonly available (commercial and non-commercial) software packages that 
can be freely exchanged or inspected both so that simulation programs can be developed 
and shared by sponsors and CVM. 
VII.  Obtaining CVM Feedback on Use of Adaptive and Other Innovative Designs 
There are various approaches that sponsors may take to open a discussion with CVM on the use 
of adaptive and other innovative designs as part of their development program to demonstrate 
effectiveness or a reasonable expectation of effectiveness.  The sponsors decision regarding 
which approach to select may be affected by where the project is in the development process.  
Communication about adaptive and other innovative designs may occur at any point in the 
development process. 
The Office of New Animal Drug Evaluation (ONADE) project managers (PMs) serve as a 
central point of contact for drug sponsors and can provide information about the new animal 
drug review process and ONADEs regulatory procedures.  If you have questions about the 
approval process and do not have an ONADE PM assigned to your company, you can contact the 
PMs through the CVM mailbox AskCVM@fda.hhs.gov. 
A.  When to submit information regarding the use of adaptive and other innovative 
designs 
There are a variety of points in the development process and a variety of submission 
types that can be used to obtain feedback.  CVM encourages sponsors interested in using 
adaptive and other innovative designs as part of their development program for a new 
animal drug to inform CVM as early in the product development process as possible. 
Sponsors planning to incorporate adaptive and other innovative designs to demonstrate 
effectiveness or reasonable expectation of effectiveness are encouraged to inform CVM 
of their intent either as part of their initial request to open a General Correspondence 
(GC) file or an INAD file (A-0000), or as part of their initial presubmission conference 
with CVM to discuss the drug product development plan (Z-submission product 
development meeting).  If one or more studies incorporating adaptive and other 
innovative designs are already complete, CVM recommends sponsors submit the 
information described in section VII.B. How to submit information regarding the use of 
adaptive and other innovative designs prior to the initial presubmission conference.  
While CVM cannot make a determination if existing data satisfies technical section 
requirements outside of a data submission, if the sponsor submits sufficient information 
about the existing data early, we can provide feedback to help inform the development 
  19  Contains Nonbinding Recommendations 
plan.7  Sponsors are also encouraged to contact their assigned PM for assistance in 
determining the most appropriate method for obtaining feedback from CVM. 
B.  How to submit information regarding the use of adaptive and other innovative 
designs 
There are several ways that sponsors may submit detailed information about plans for 
incorporating adaptive and other innovative designs into their development program to 
demonstrate substantial evidence of effectiveness or reasonable expectation of 
effectiveness.  The regulatory pathway selected (CNADA versus NADA), the stage of 
development, the information available, and the feedback being sought from CVM, 
among other factors, may influence the submission type selected. 
Sponsors may seek general guidance on the use of adaptive and other innovative designs 
in a GC file prior to opening an INAD file,  Sponsors may submit information to support 
use of adaptive and other innovative designs as part of their initial request to open an 
INAD file; as part of a meeting request for a presubmission conference (Z-submission) to 
discuss the Effectiveness technical section requirements; or as part of an information 
submission (H-submission) or meeting request (Z-submission) to discuss study protocol 
design. 
Sponsors considering incorporating adaptive and other innovative designs into future 
studies to demonstrate effectiveness or reasonable expectation of effectiveness should, 
prior to conducting a study, submit a study protocol for review (E-submission).  
Obtaining CVM input regarding study design will make reaching protocol concurrence 
more efficient.   
Sponsors may also open a Veterinary Master File (VMF) to hold detailed information 
regarding a specific study design, including those regarding pre-investigational 
discussions about the use of adaptive and other innovative designs, or if the information 
will be used in the development of multiple applications.8  The VMF is confidential and 
is typically used when a holder wishes the material in the VMF to remain proprietary, 
although the material may be referenced by multiple third-party products or files (INAD, 
NADA, or CNADA).  Alternatively, if multiple sponsors are cooperating on product 
development, sponsors may establish a Public Master File (PMF) to allow all cooperators 
to reference the information.  As suggested by the name, the information in a PMF is 
publicly available. 
Regardless of how information is submitted to CVM, sponsors should submit an 
organized and focused information package.  This will allow CVM the best opportunity 
to provide appropriate recommendations in response.  Although full information may not 
 
7 See CVM Program Policy and Procedures (P&P) Manual 1243.2200 Submission and Review of Early Information 
(EI) Prior to Presubmission Conferences and Protocol Review (June 2020) and CVM P&P Manual 1243.3050 
Determining Technical Section Requirements for New Animal Drug Product Approval (May 2019) 
8 See CVM P&P Manual 1243.2400 Veterinary Master Files with Manufacturing Information (August 2019) 
  20  Contains Nonbinding Recommendations 
be available in the early stages of the development process, the amount of information 
provided and the level of detail of the information provided should be commensurate 
with the submission type.  The information should address some or all of the following 
elements, as appropriate for the submission type:  
1.  The proposed study design that will utilize an adaptive design and how the study fits 
within the development plan for the product, including a discussion of how an 
adaptive design would be beneficial.  See section IV.A. General Recommendations. 
2.  The type of adaptation(s) proposed (e.g., sample size estimation, dropping treatment 
arms, determining futility, etc.), and the timing and number of the adaptation(s).  The 
algorithm/criteria for determining the specific change.  
3.  The operating characteristics of the design and the analytical methods or simulations 
used to explore these characteristics, if appropriate.  The description should provide 
for adequate control of Type I error and minimize the risk of statistical and 
operational biases.  The programs used should be included. 
VIII.  Glossary 
The following definitions are supplied to provide the reader with an understanding of the specific 
terms used in this guidance as applicable to new animal drugs.  These definitions should not be 
construed to be new interpretations or clarification of the use of similar words or phrases in the 
FD&C Act, related code or regulation, other Federal, State, or local laws, or other guidance 
documents. 
A general definition of bias can be found in Szklo and Nieto (2000).  In this guidance, bias is 
specifically used in the context of a systematic tendency for the estimate of the treatment effect 
to deviate from its true value and bias arising from differences in study conduct (operational 
bias). 
Conditional power:  The conditional probability of a statistically significant treatment effect at 
the end of the study calculated based on the results observed at an interim analysis. 
A fixed sample trial:  A clinical study with a targeted total sample size, or a targeted total 
number of events, that is specified at the design stage and not subject to prospectively planned 
adaptation. 
Frequentist inference:  A type of statistical inference that draws conclusions for the population 
from sample data by emphasizing the frequency or proportion of the data, using methodologies 
of statistical hypothesis testing or confidence intervals.  In comparison, Bayesian inference is a 
method of statistical inference in which Bayes' theorem is used to update the probability for a 
hypothesis as more evidence or information becomes available. 
In Bayesian statistics, an informative prior distribution provides specific, definite information 
about a variable of interest before a study.  The posterior probability distribution is the 
probability distribution of the variable estimated based on the prior distribution and the evidence 
from the study.  
  21  Contains Nonbinding Recommendations 
Generalizability:  The degree to which inference, based on the study or studies, is applicable to 
actual conditions of use in veterinary practice or animal management. 
An interim analysis:  Any examination of data obtained from subjects in a study while that 
study is ongoing and is not restricted to cases in which there are formal between-group 
comparisons.  The observed data used in the interim analysis can include one or more types, such 
as baseline data; safety outcome data; pharmacokinetic, pharmacodynamic, or biomarker data; or 
effectiveness outcome data. 
Multiplicity:  The potential inflation of the Type I error rate as a result from multiple testing.  
This arises when the study data are examined and analyzed multiple times during the study 
without appropriate statistical preplanning.  
A non-adaptive trial:  A clinical study without any prospectively planned opportunities for 
modifications to the design. 
Operating characteristics:  The properties for a given study that describe its performance.  For 
example, properties of interest might include Type I error probability; power; expected, 
minimum, and maximum sample size; bias of treatment effect estimates; and coverage of 
confidence intervals (the probability the confidence interval would include the true treatment 
effect if the clinical effectiveness study were repeated many times).  Typically, operating 
characteristics are examined by simulating studies that use the proposed design under a range of 
reasonable scenarios (e.g., effect size, sample size, and/or various subject characteristics).   
Prospective:  For the purposes of this guidance, the term prospective means that the 
adaptation is planned and details specified before any comparative analyses of accumulating 
study data are conducted.  In nearly all situations, potential adaptive design modifications should 
be planned and described in the clinical effectiveness study protocol (and in a separate statistical 
analysis plan, if used) before initiation of the study. 
Reliability:  The extent to which statistical inference from the clinical effectiveness study 
accurately and precisely evaluates the treatment effect. 
In a clinical effectiveness study, a statistical test is applied to assess the strength of evidence 
against a null hypothesis, where the null hypothesis is typically a statement of non-effect (e.g., 
no difference between two groups in the study).  If the null hypothesis is rejected at a specified 
level of significance (typically a two-sided level equal to 0.05), with demonstration of a 
clinically meaningful effect of the drug, the evidence generally supports a conclusion of 
effectiveness.  Sometimes, however, the null hypothesis is rejected even though the drug is 
ineffective.  This is called a Type I error.  We use the term Type I error probability to refer to 
the maximum probability of rejecting the null hypothesis when it is true (e.g., concluding that 
there is an effect when the truth is that there is no effect).  Type II error is the error of not 
rejecting the null hypothesis when it is false.  
IX.  References 
Bauer, P and K Kohne. 1994. Evaluation of Experiments with Adaptive Interim Analyses. 
Biometrics 50(4):10291041. 
  22  Contains Nonbinding Recommendations 
Berry, SM, BP Carlin, JJ Lee, and P Muller. 2010. Bayesian Adaptive Methods for Clinical 
Trials. CRC Press, ISBN 9781439825488. 
Broberg, P. 2013. Sample size re-assessment leading to a raised sample size does not inflate type 
I error rate under mild conditions. BMC Med Res Methodol 13:94. 
Chow, SC and M Chang. 2011. Adaptive Design Methods in Clinical Trials. CRC Press, ISBN 
9781439839874. 
Cui, L, HM Hung, and SJ Wang. 1999. Modification of Sample Size in Group Sequential 
Clinical Trials. Biometrics 55(3):853857. 
Chen, YH, DL DeMets, and KKG Lan. 2004. Increasing the Sample Size When the Unblinded 
Interim Result Is Promising. Statist Med 23(7):1023-1038. 
Denne, JS. 2001. Sample Size Recalculation Using Conditional Power. Statist Med 20(17-
18):26452660. 
Faries, DE, JH Heiligenstein, GD Tollefson, and WZ Potter. 2001. The Double-Blind Variable 
Placebo Lead-in Period: Results from Two Antidepressant Clinical Trials. J Clin 
psychopharmacology 21(6):561-568. 
Fisher, LD. 1998. Self-designing Clinical Trials. Statist Med 17(14):15511562. 
Friede, T and M Kieser. 2003. Blinded Sample Size Reassessment in Non-inferiority and 
Equivalence Trials. Statistics in Medicine 22(6): 995-1007. 
Hey, SP and J Kimmelman. 2015. Are Outcome-adaptive Allocation Trials Ethical? Clinical 
Trials 12(2):102-106. 
Ivanova, A and RN Tamura. 2015. A two-way enriched clinical trial design: combining 
advantages of placebo lead-in and randomized withdrawal. Statistical Methods in Medical 
Research 24(6):871-890. 
Jennison, C and BW Turnbull. 1999. Group Sequential Methods with Applications to Clinical 
Trials. New York: Chapman & Hall/CRC Press. 
Lan, KG and DL DeMets. 1983. Discrete Sequential Boundaries for Clinical Trials. Biometrika 
70(3):659663. 
Mehta, CR and SJ Pocock. 2011. Adaptive Increase in Sample Size When Interim Results Are 
Promising: A Practical Guide with Examples. Statist Med 30(28):3267-3284. 
OBrien, PC and TR Fleming. 1979. A Multiple Testing Procedure for Clinical Trials. 
Biometrics 35:549556. 
Pocock, SJ. 1977. Group Sequential Methods in the Design and Analysis of Clinical Trials. 
Biometrika 64(2):191199. 
  23  Contains Nonbinding Recommendations 
Rosenberger, WF and JM Lachin. 1993. The Use of Response-Adaptive Designs in Clinical 
Trials. Controlled Clinical Trials 14:471-4. 
Russell, WMS and RR Burch. 1959. The Principles of Humane Experimental Technique. 
London: Metheun and Co. [Reissued: 1992, Universities Federation for Animal Welfare, Herts, 
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Szklo M, and F Nieto. 2000. Epidemiology: Beyond the basics. Gaithersburg, MD: Aspen 
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Wassmer, G and W Brannath. 2016. Group Sequential and Confirmatory Adaptive Designs in 
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