2012 EDUCATIONAL BOOK - American Society of Clinical Oncology

Limitations of Adaptive Clinical Trials
Overview: Adaptive designs are aimed at introducing flexibility
in clinical research by allowing important characteristics
of a trial to be adapted during the course of the trial based
on data coming from the trial itself. Adaptive designs can be
used in all phases of clinical research, from phase I to phase
III. They tend to be especially useful in early development,
WITH THE large number of promising new molecules
that are currently available for clinical testing, clinical
trials must detect a drug’s benefit (or harm) as quickly
as possible. In parallel to the explosion in the number of
drugs awaiting clinical testing, the costs of clinical trials
have sky-rocketed, which adds to the pressure of optimizing
trials, to the extent possible, in terms of sample sizes,
timelines, and risk of failure. A new class of designs has
emerged to address these challenges, collectively known as
adaptive designs. In this chapter, we review different types
of adaptive designs and briefly mention some situations in
which such designs can be useful. Much of this chapter,
however, is devoted to a discussion of the limitations and
drawbacks of adaptive designs, which might partly explain
why these designs have not been commonly used and might
in the future have less of an effect on clinical research than
claimed by their advocates.
Types of Adaptive Designs
One of the difficulties surrounding adaptive designs is
that the term is used to encompass different situations. For
clarity, we divide group adaptive designs into three broad
categories.
The first category is treatment effect–independent adaptive
designs. In these designs, some of the design features
can be adapted on observation of predefined patient characteristics
(such as baseline prognostic factors) or outcomes
(such as response rate or hazard rate overall or in the control
group) but in ignorance of the treatment effect.
The second category is treatment effect–dependent adaptive
designs. In these designs, one or more of the design
features (such as the sample size, the patient inclusion
criteria, the treatment groups being compared, the treatment
allocation ratio, or even the primary endpoint) can be
adapted, depending on the observed treatment effect.
The third category is other types of adaptive designs,
which include the continual reassessment method (CRM) for
phase I trials and seamless phase II/III designs.
Treatment Effect–Independent Adaptive Designs
In these designs, adaptations do not depend on the treatment
effect. As such, these adaptations raise few issues and
have little effect on the statistical inference; in particular,
they do not inflate the type I error. In fact, such adaptations
are so mild that trials using them are not referred to
as “adaptive”. We provide two examples but do not discuss
these designs in detail.
Covariate-Adaptive Randomization
One instance of treatment effect–independent adaptation
is covariate-adaptive randomization, for which the probabil-
By Marc Buyse, ScD
when the paucity of prior data makes their flexibility a key
benefit. The need for adaptive designs lessened as new
treatments progress to later phases of development, when
emphasis shifts to confirmation of hypotheses using fully
prespecified, well-controlled designs.
ity of allocating the next patient to one of the trial’s treatment
groups is computed dynamically to ensure good
balance among the treatment groups with respect to important
prognostic factors (center or country, clinicopathologic
features, and, increasingly, biomarkers measured at baseline).
A common implementation of this approach is minimization,
for which a predefined algorithm is used to minimize
the imbalance between the distributions of important prognostic
factors at baseline among treatment groups. When
minimization is used, the treatment group the next patient
is allocated to can depend on the baseline characteristics of
previously accrued patients but not on their outcome. 1
Sample Size Increases
Another type of treatment effect–independent adaptation
consists of a sample size increase if the incidence of the event
of interest is much lower than expected in the control group
(to preserve the power of the trial) or if the event rate is
much lower than expected overall (to preserve the timelines
of event-driven analyses when the outcome of interest is a
time-to-event, such as disease-free survival or overall survival).
Again, these sample size increases are implemented
in ignorance of the treatment effect; hence, they generally
have no effect on type I error and, if implemented appropriately,
raise no special statistical concerns. 2
Treatment Effect–Dependent Adaptive Designs
These designs are truly adaptive insofar as adaptations
depend on the observed treatment effect, which requires
caution to be exercised and a proper statistical approach to
be used. If, for instance, the sample size of a trial was
increased (or decreased) simply because the observed treatment
effect was smaller (or larger) than anticipated, the
final results of the trial could be biased. 3 For instance, a
randomly large treatment effect could lead to a reduction
in sample size even though the true effect were as expected.
Note that a group sequential design is not subject to this
problem because its sample size is fixed and can only be
decreased if the trial is stopped for efficacy or futility at an
interim analysis. 4
From the International Drug Development Institute, Houston, TX; Interuniversity Institute
for Biostatistics and Statistical Bioinformatics, Hasselt University, Diepenbeek,
Belgium.
Author’s disclosures of potential conflicts of interest are found at the end of this article.
Address reprint requests to Marc Buyse, ScD, IDDI Inc, 363 N. Sam Houston Pkwy. E.,
Suite 1100, Houston, TX 77060; email: marc.buyse@iddi.com.
© 2012 by American Society of Clinical Oncology.
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