Abstract

The role of biomarkers has increased dramatically in cancer therapeutic trials, and with molecular markers now integrated into phase II and III studies, there is a need for novel clinical trial designs to efficiently answer questions of both drug effects and biomarker performance. Further, trials with integral markers need greater flexibility for the wider variety of potential outcomes. To address these needs, we propose a Bayesian hierarchical model for use in response-adaptive, randomized phase II studies integrating multiple agents and multiple biomarker sub-populations. This allows for a gradual and seamless approach to transition from a randomized block design to a biomarker-enrichment design, such that a greater fraction of participants are randomized to optimal therapy when therapeutics are effective. Compared to the use of traditional staged designs within biomarker sub-populations, our method is more robust against misspecification of marker prevalence, and has improved performance in identifying the subgroups where therapeutics are effective.

Disciplines

Clinical Trials | Statistical Models