Adaptive enrichment trials: What are the benefits?

Abstract

When planning a Phase III clinical trial, suppose a certain subset of patients is expected to respond particularly well to the new treatment. Adaptive enrichment designs make use of interim data in selecting the target population for the remainder of the trial, either continuing with the full population or restricting recruitment to the subset of patients. We define a multiple testing procedure that maintains strong control of the familywise error rate, while allowing for the adaptive sampling procedure. We derive the Bayes optimal rule for deciding whether or not to restrict recruitment to the subset after the interim analysis and present an efficient algorithm to facilitate simulation-based optimisation, enabling the construction of Bayes optimal rules in a wide variety of problem formulations. We compare adaptive enrichment designs with traditional nonadaptive designs in a broad range of examples and draw clear conclusions about the potential benefits of adaptive enrichment.

Keywords: Bayesian optimization; adaptive designs; adaptive enrichment; phase III clinical trial; population enrichment.

FIGURE 1

Regions of $\mathit{\theta }$ values in which each of the Fixed Full population (FF), Fixed Subpopulation (FS), and optimal Adaptive Enrichment (AE) designs give the highest value of $E\{G(\mathit{\theta },X)\}$ [Color figure can be viewed at wileyonlinelibrary.com]

FIGURE 2

An example of a Bayes optimal decision rule for an adaptive enrichment trial [Color figure can be viewed at wileyonlinelibrary.com]

FIGURE 3

Computation of an optimal decision rule [Color figure can be viewed at wileyonlinelibrary.com]

FIGURE 4

Computation of an optimal decision rule [Color figure can be viewed at wileyonlinelibrary.com]