Adaptive clinical trials have risen in popularity and gained more attention since the FDA Critical Path Initiative (2004) and Critical Path Opportunity List (2006) called for innovative solutions to transform the way medicinal products are developed, evaluated, and manufactured. **Disclaimer: This article was previously published. Sciformix is now a Covance company.

1. Redefining the Gold Standard
Adaptive clinical trials are being increasingly recognised as a means to
predict the success of a drug in early development stages, though they
are not without their complications
Adaptive clinical trials have risen in popularity and gained
more attention since the FDA Critical Path Initiative (2004)
and Critical Path Opportunity List (2006) called for innovative
solutions to transform the way medicinal products are
developed, evaluated, and manufactured. They are now
being commonly used to quickly predict the likelihood
of the success or failure of a drug in early stages of
drug development.
The release of the FDA’s draft guidance in 2010 on “adaptive
design clinical trials for drugs and biologics” provided a
suggested framework for designing such studies and for
using them to make decisions that would allow the innovator
to “fail early.” EMA’s Committee for Medicinal Products for
Human Use released a similar draft in 2006 (1).
Adaptive designs have been widely used over the past
decade, largely focussed on early development, especially
Phase 2 studies, although they have also been used in
confirmatory clinical trials. Adaptive clinical trials provide
more flexibility and look to improve the efficiency and
speed of clinical trials.
Due to the complexity of adaptive designs, the FDA
encourages more extensive interactions between the
sponsor and the FDA, therefore, sponsors may seek feedback
from the FDA. However, the EMA reflection paper on adaptive
trials has advised a more cautionary approach, suggesting
use in late Phase 2 (1). The EMA acknowledged the problems
increased flexibility can cause. Furthermore, a joint workshop
organised by US and EU regulators, academia, and industry
experts advised the use of adaptive designs in exploratory
studies only. The workshop did not recommend use in
confirmatory studies (2).
Certain types of adaptive designs are used more commonly,
eg, adaptation for sample size re-estimation, testing for
futility, etc. However, many other types of adaptive designs
exist, which may not be as well-known. This article will
provide a summary of the most common adaptations used,
along with their different objectives, benefits, and challenges.
Applying Adaptations
The FDA draft guidance defines adaptive designs as studies
that “include a prospectively planned opportunity for
modification of one or more specified aspects of the study
design and hypothesis based on analysis of data (usually
interim data, ie, study data obtained until a specified
timepoint, even while the study is still ongoing) from subjects
in the study” (3). Analyses of the accumulating study data are
performed at prospectively planned timepoints within the
study, either in a blinded or unblinded manner.
An adaptation is a change made to the trial procedure and/
or statistical procedure during the conduct of a clinical
trial. The procedures that can be adapted include the
eligibility criteria, patient population, study dose, number of
treatments, treatment duration, study endpoints (efficacy,
safety, biomarkers, etc), laboratory testing procedures,
diagnostic procedures, criteria for evaluation and assessment
of clinical responses, number of interim analyses, and study
hypotheses. Adaptations commonly employed in clinical
trials can be classified into the categories of prospective
adaptation, concurrent (or ad hoc) adaptation, and
retrospective adaptation as shown in Figure 1 (see page 23).
The prospective, ad hoc, and retrospective adaptations are
implemented by study protocol, protocol amendments, and
regulatory reviewer’s consensus, respectively. Due to the
level of flexibility involved, these trial designs are also termed
'flexible designs'. However, flexibility here does not mean that
the trial can be modified any time at will. The modification
and adaptations have to be preplanned and have to use the
data collected from the study.
Types of Adaptive Trial Designs
Adaptive design clinical trials can be broken down into
categories based on four major rules (see Table 1):
• Allocation rule
• Sampling rule
• Stopping rule
• Decision rule
In certain rare scenarios, a fifth rule is added consisting
of multiple adaptations or multiple trials. For example,
Phase 2 and Phase 3 can be seamlessly combined into
one adaptive trial.
Alternatively, adaptive trial designs can be grouped by the
methods employed in the trial. The following are the most
Dr Chitra Lele and Dr Ritu
Budania at Sciformix,
a Covance Company
22 ICT l February 2019
ICT Adaptive Trial Designs

2. common adaptive study designs – a combination of
these could also be used in a single study and often
the strategies overlap.
Group Sequential Design
This is a method for early termination of a study. Unblinded
interim analysis of accruing study data is used in a planned
and confidential manner to decide whether to continue or
terminate the trial. Unblinding of the treatment effect may
potentially introduce bias into the conduct of the study
or into subsequent decisions in regard to this. Hence, the
implementation of this strategy has to be done carefully.
For example, if it is then altered to be a smaller study for
a shorter duration, it could result in lost data relating to
safety. However, group sequential designs are deemed
well accepted by the FDA, as the statistical parameters and
methods are known, particularly in regard to controlling the
type 1 error (4).
Sample Size Re-Estimation Design
Based on interim analysis, sample size can be adjusted in
this adaptive design. Incorrect initial parameters may lead to
underpowered designs and inappropriate sample size at the
clinical trial planning stage. In contrast to group sequential
designs, this alternative allows for a smaller initial sample
size, but with the option to increase it later should the need
arise. Using this approach later in a study is not advisable
because a large percentage increase in sample size at that
point is inefficient. It is advised that trials using this method
should only do so to increase the sample size, not to
decrease it.
Adaptive Seamless Design
This is combining multiple study phases and objectives into
a single study design, which has been described as occurring
between Phases 1 and 2 or, more commonly, between Phases
2 and 3. In a seamless trial, data from a phase are accrued.
Based on the interim analysis, certain treatment arms may be
dropped, with the remaining arms entering the next phase of
the trial without any lag time. This results in a reduction in the
duration of the clinical development programme by reducing
the lag time between completion of the Phase 2 trial and
initiation of the Phase 3 trial.
Adaptive Dose-Finding Design
This selects multiple doses across a range, with the objective of
identifying the maximum tolerable dose to be used in future
trials. The design may eliminate unsuitable or uninformative
doses, but can also add more preferable doses based on
interim analyses. Therefore, the dose assignment of individuals
enrolled later in the study is based on the results of the
responses of the previous participants. This makes allocating
patients to doses in a range of interest possible, with the
Figure 1: Clinical trial adaptations based on the time that they are applied
Rule Definition
Allocation rule
Defines how the subjects will be allocated
to different arms in a trial and comprises
response-adaptive randomisation and
covariate-adaptive allocation
Sampling rule
Defines how many subjects will be sampled
at the next stage and consists of sample size
re-estimation design (both blinded and
unblinded) and drop-the-loser design
Stopping rule
Defines when to stop the trial and consists
of group sequential design and adaptive
treatment-switching design
Decision rule
Comprises of changes not covered under
the other three categories and consists of
hypothesis-adaptive design and change
in the primary endpoint or statistical
method or patient population design
Table 1: Adaptive design categories
Retrospective
Modifications and/or changes made to statistical analysis plan prior to database lock or unblinding
of treatment codes
Concurrent
Modifications in inclusion/exclusion criteria, dose/regimen and treatment duration, changes in
hypotheses and/or study endpoints
Prospective
Modifications like adaptive randomisation, stopping a trial early due to safety, futility, or efficacy at
interim analysis, dropping the losers (or inferior treatment groups), sample size re-estimation, etc
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3. ultimate goal that more patients would be enrolled in
more meaningful doses. This study has an ethical
advantage, as fewer people are assigned to doses at
the extremes, meaning major toxicity levels and drug
ineffectiveness would be avoided. Dose escalation designs
and Bayesian sequential designs are often used in oncology.
By the very nature of such designs, those who join at the
beginning of the trial are at a disadvantage, as they have a
higher chance of receiving a dose that is either ineffective
or at the extreme of toxicity.
Drop-The-Loser Design
Interim analysis is performed, and inferior treatment groups
can be dropped out based on some pre-specified criteria. The
winners will then proceed to the next stage. This design can be
used in Phase 2 trials, especially when uncertainties regarding
the dose levels exist.
Adaptive Randomisation Design
Randomisation can be modified based on treatment,
covariate, and response to improve the chance of success,
leading to treatment-adaptive, covariate-adaptive, and
response-adaptive designs.
Biomarker Adaptive Design
This design allows adaptation based on responses of
biomarkers, including pharmacokinetic, pharmacodynamic,
and genomic markers. This is useful when looking to select
the right patient population, identify natural course of
disease, and for early detection of disease. One thing to note
when opting for this design is that a gap exists between
identifying biomarkers associated with clinical outcomes
and establishing a predictive model between relevant
biomarkers and clinical outcomes in clinical development.
Adaptive Treatment-Switching Design
This allows the investigator to switch a patient’s treatment
from an initial assignment to an alternative treatment if lack
of efficacy or safety of the initial treatment is evident. This is
a common practice in oncology studies. However, estimation
of survival rate can become very difficult if the disease has
a poor prognosis. If this is the case, sample size adjustment
can overcome the problem.
Adaptive Hypotheses Design
The study hypothesis can be changed based on results of the
interim analysis by, for example, switching from superiority to
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4. www.samedanltd.com l ICT 25
Dr Chitra Lele is Chief Scientific Officer at
Sciformix, a Covance Company, with over
20 years of experience in the healthcare
industry. She has been part of the
company’s leadership from its inception
and has been instrumental in establishing
and growing the organisation. Prior to
Sciformix, Chitra was Executive Director
responsible for Indian operations of Pfizer Global RD.
With a PhD in statistics from Stanford University, US,
her prior experience includes work as a biostatistician
in cancer epidemiology at both Stanford and University
of California, US.
Email: chitra.lele@sciformix.com
Dr Ritu Budania is a Drug Safety Physician
for global medical safety operations at
Sciformix, a Covance Company. She
completed her MBBS from Topiwala
National Medical College, India, and
MD Pharmacology from Government
Medical College, India. Ritu has assisted
in preparing ‘Desk View Clinical Trials
in India’ for the WHO and prepared signal reports for
Pharmacovigilance Programme of India (PvPI). She was in
charge of the adverse drug monitoring centre under PvPI,
at King Edward Memorial Hospital, India. Ritu has
expertise in pharmacovigilance, clinical pharmacology,
medical writing, clinical research, and clinical trials. She is
also an experienced industry guest speaker in the
subject of pharmacology.
Email: ritu.budania@sciformix.com
About the authors
non-inferiority, or changing the hypothesis by altering
the endpoint.
Advantages and Challenges
Both pros and cons of adaptive designs are well-
known, especially for the commonly used designs.
Reducing time to clinical development and reducing
patient exposure to ineffective treatments are huge
advantages. An additional benefit is that modifications
and changes are pre-approved by the regulatory authority,
therefore, no requirement to file protocol amendments
exists. Flexibility is also an advantage of adaptive clinical
trials, as they enable reactions to unexpected events.
Even more so, while many compounds in drug
development fail, adaptive trials often enable
earlier detection of problems and can stop the
trial, saving substantial costs and resources.
Furthermore, as treatment can be altered, trial
subjects can be managed efficiently, with fewer
subjects staying on an ineffective dose (5).
There are challenges while implementing adaptive
designs, such as introduction of bias based on unblinded
analyses or ad hoc adaptations; some challenges are
design-related, such as the risk of Type 1 error (false
positives) and loss of information when treatment arms
are dropped based on quick interim analyses, and some
are application-related, such as the results of the adaptive
design being generally inapplicable if the target
population is shifted through the adaptations.
Potential to Redefine
Adaptive clinical trials have the potential to redefine
the existing gold standard of randomised clinical trials
for clinical development of new molecules. Increased
focus on personalised medicine also increases the
importance of adaptive trial designs. Adaptive clinical
trial designs may significantly reduce sample size in
studies on pharmacogenetic biomarkers. Even a
modest reduction in sample size of 10-20% would
reduce cost significantly, without any ethical concerns.
Adaptive study designs can drive efficiency by
detecting effective treatment options (if they exist)
at the earliest possible point. A major disadvantage
is around decision-related complexities in determining
whether to make a change, when to make the change,
what kind of change, and arriving at the decision as
independently as possible. Choice of the appropriate
modifications and careful execution of the adaptations
are key to successful outcomes. Credibility of adaptive
clinical trial designs can be maintained by blinding
results and utilising firewalls, in which only relevant
people would have access to the results.
As pharmaceutical companies embrace adaptive clinical
trial designs, ensuring effective planning and management
of these complex adaptive trial designs is equally critical
for robust solutions. Regulatory authorities will continue to
support these developments and a wider acceptance among
stakeholders involved in drug and therapeutic development
can be expected.
References
1. EMA, Reflection paper on methodological issues in confirmatory
clinical trials with flexible design and analysis plan, Committee for
Medicinal Products for Human Use: 2018
2. EMA, Adaptive design in confirmatory clinical trials, EMA/EFPIA: 2009
3. Bauer P et al, Twenty-five years of confirmatory adaptive designs:
Opportunities and pitfalls, Stat Med 35(3): pp325-47, 2016
4. Elman SA et al, Adaptive clinical trial design: An overview and
potential applications in dermatology, J Invest Dermatol 136(7):
pp1,325-9, 2016
5. Mahajan R and Gupta K, Adaptive design clinical trials: Methodology,
challenges and prospect, Indian J Pharmacol 42(4): pp201-7, 2010