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2017 ; 18
(1
): 420
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Bayesian dose selection design for a binary outcome using restricted response
adaptive randomization
#MMPMID28886745
Meinzer C
; Martin R
; Suarez JI
Trials
2017[Sep]; 18
(1
): 420
PMID28886745
show ga
BACKGROUND: In phase II trials, the most efficacious dose is usually not known.
Moreover, given limited resources, it is difficult to robustly identify a dose
while also testing for a signal of efficacy that would support a phase III trial.
Recent designs have sought to be more efficient by exploring multiple doses
through the use of adaptive strategies. However, the added flexibility may
potentially increase the risk of making incorrect assumptions and reduce the
total amount of information available across the dose range as a function of
imbalanced sample size. METHODS: To balance these challenges, a novel
placebo-controlled design is presented in which a restricted Bayesian response
adaptive randomization (RAR) is used to allocate a majority of subjects to the
optimal dose of active drug, defined as the dose with the lowest probability of
poor outcome. However, the allocation between subjects who receive active drug or
placebo is held constant to retain the maximum possible power for a hypothesis
test of overall efficacy comparing the optimal dose to placebo. The design
properties and optimization of the design are presented in the context of a phase
II trial for subarachnoid hemorrhage. RESULTS: For a fixed total sample size, a
trade-off exists between the ability to select the optimal dose and the
probability of rejecting the null hypothesis. This relationship is modified by
the allocation ratio between active and control subjects, the choice of RAR
algorithm, and the number of subjects allocated to an initial fixed allocation
period. While a responsive RAR algorithm improves the ability to select the
correct dose, there is an increased risk of assigning more subjects to a worse
arm as a function of ephemeral trends in the data. A subarachnoid treatment trial
is used to illustrate how this design can be customized for specific objectives
and available data. CONCLUSIONS: Bayesian adaptive designs are a flexible
approach to addressing multiple questions surrounding the optimal dose for
treatment efficacy within the context of limited resources. While the design is
general enough to apply to many situations, future work is needed to address
interim analyses and the incorporation of models for dose response.
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