2012 EDUCATIONAL BOOK - American Society of Clinical Oncology

LIMITATIONS OF ADAPTIVE CLINICAL TRIALS
been called a “play-the-winner” strategy. 8 A crucial distinction
needs to be made between covariate-adaptive randomization
and outcome-adaptive randomization. Indeed,
although the former raises no particular issue, the latter is
fraught with problems.
First, the outcome that is used to adapt the randomization
has to be observed early and reliably, and it must be
reasonably predictive of important clinical endpoints for the
adaptation to succeed at placing more patients in the better
treatment group.
Second, adaptive randomization can result in major imbalances
among treatment arms, which in turn negatively
affects the statistical power of the trial.
Third, the statistical inference is complicated because the
treatment assignments and the responses are correlated; as
a consequence, rerandomization tests must be used instead
of traditional likelihood-based tests.
Fourth, adaptive randomization can cause accrual bias (if
patients wait for the probability of receiving the better
treatment to increase) and/or selection bias (if patients are
aware of the emerging difference among the treatment
groups).
Last but not least, it is incorrect to claim that adaptive,
randomization is ethically superior to fixed randomization
because equipoise mandates that allocation to any of the
treatment groups be considered equally desirable. It might
make sense to allocate more patients to the experimental
group than to the control group, but the justification for
doing so is that more information is needed about a new
treatment than about a well-established standard treatment.
When such is the case, a fixed unequal allocation ratio
(such as a 2:1 ratio in favor of the experimental group) will
do just as well as adaptive randomization, without being
subject to the problems listed above. 9
Other Types of Adaptive Designs
Fig. 1. Comparison between a group sequential design and an adaptive design.
There are many other types of adaptive designs, some of
which do not fall into the two clear-cut categories discussed.
We briefly mention two types of designs that have attractive
properties but have also been rarely used in practice.
CRM in Phase I Trials
Classic phase I cancer trials are aimed at determining the
maximum tolerated dose (MTD) of a new drug or combination
of drugs. 10 They are usually performed according to a
fixed design called the “3 � 3” design. The design proceeds in
cohorts of three patients, with the first cohort being treated
at the minimum dose of interest and the next cohorts being
treated at increasing dose levels according to a predetermined
dose escalation scheme. The dose escalation proceeds
until at least one dose-limiting toxic effect is observed in a
cohort of three patients, in which case a second cohort of
three patients is treated at the same dose level. The dose
escalation stops as soon as at least two patients experience
a dose-limiting toxic effect, either in the first cohort of three
patients treated at that dose level or in the two cohorts of
three patients treated at that dose level. Although this
design is used in almost every phase I cancer trial today, it
has several limitations. 11 First, too many patients may be
treated at low doses, with virtually no chance of efficacy.
Second, dose escalation may be too slow because of an
excessive number of escalation steps, resulting in trials that
take longer than needed to get to the MTD. Third, too few
patients may be treated near the MTD, resulting in substantial
residual uncertainty about the dose recommended for
further trials, which raises ethical concerns. Indeed, if the
recommended dose is chosen too low, it can fail to have
efficacy in phase II trials, whereas if it is chosen too high, it
can put patients at unacceptable risk in phase II trials. Last
but not least, the “3 � 3” design makes no allowance for
patient variability. An adaptive design known as the CRM
was originally proposed by O’Quigley et al 12 in the early
1990s. This design involves a statistical approach based on
an assumed dose-response relationship, which is described
through a mathematical function that links the probability
of a dose-limiting toxic effect and the dose level. The CRM
design is adaptive insofar as the dose to use for the next
patient is determined from the toxic effects experienced by
all the patients already treated so far. Many modifications to
the CRM have been proposed, and simulation studies have
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