Biomarker-Defined Subgroup Selection Adaptive Design for Phase III Confirmatory Trial with Time-to-Event Data: Comparing Group Sequential and Various Adaptive Enrichment Designs

Abstract

Predictive and prognostic biomarkers play an important role in personalized medicine to determine strategies for drug evaluation and treatment selection. In the context of continuous biomarkers, identification of an optimal cutoff for patient selection can be challenging due to limited information on biomarker predictive value, the biomarker’s distribution in the intended use population, and the complexity of the biomarker relationship to clinical outcomes. As a result, prespecified candidate cutoffs may be rationalized based on biological and practical considerations. In this context, adaptive enrichment designs have been proposed with interim decision rules to select a biomarker-defined subpopulation to optimize study performance. With a group sequential design as a reference, the performance of several proposed adaptive designs are evaluated and compared under various scenarios (e.g., sample size, study power, enrichment effects) where type I error rates are well controlled through closed testing procedures and where subpopulation selections are based upon the predictive probability of trial success. It is found that when the treatment is more effective in a subpopulation, these adaptive designs can improve study power substantially. Furthermore, we identified one adaptive design to have generally higher study power than the other designs under various scenarios.