Phase II clinical trials play a pivotal role in drug development by screening a large number of drug candidates to identify those with promising preliminary efficacy for phase III testing. Trial designs that enable efficient decision-making with small sample sizes and early futility stopping while controlling for type I and II errors in hypothesis testing, such as Simon’s two-stage design, are preferred. Randomized multi-arm trials are increasingly used in phase II settings to overcome the limitations associated with using historical controls as the reference. However, how to effectively balance efficiency and accurate decision-making continues to be an important research topic. A notable development in phase II randomized design methodology is the Bayesian pick-the-winner (BPW) design that extends a Simon’s two-stage based multi-arm design with a Bayesian winner-selection strategy. Despite multiple appealing features, this method cannot easily control for overall type I and II errors for winner selection. Here, we introduce an improved randomized two-stage Bayesian pick-the-winner (IBPW) design that formalizes the winner-selection based hypothesis testing, optimizes sample sizes and decision cut-offs by strictly controlling the type I and type II errors under a set of flexible hypotheses for winner-selection across two treatment arms. Simulation studies demonstrate that our new design offers improved operating characteristics for winner selection while retaining the desirable features of the BPW design.


Applied Statistics | Biostatistics | Clinical Trials | Statistical Methodology