Bayesian Adaptive Phase II Screening Design for Combination Trials

• Activity Number: 298
• Type: Contributed
• Date/Time: Tuesday, July 31, 2012 : 8:30 AM to 10:30 AM
• Sponsor: Biopharmaceutical Section
• Author(s): Chunyan Cai*+ and Ying Yuan and Valen E Johnson
• Companies: MD Anderson Cancer Center and MD Anderson Cancer Center and MD Anderson Cancer Center
• Keywords: Drug combination ; Screening trial ; Bayesian hypothesis testing ; Adaptive randomization

Abstract

Trials of combination therapies for the treatment of cancer are playing an increasingly important role in the battle against this disease. To more efficiently handle the large number of combination therapies that must be tested, we propose a novel Bayesian phase II adaptive screening design to simultaneously select among possible treatment combinations involving multiple agents. Our design is based on formulating the selection procedure as a Bayesian hypothesis testing problem in which the superiority of each treatment combination is equated to a single hypothesis. During the trial conduct, we use the current values of the posterior probabilities of all hypotheses to adaptively allocate patients to treatment combinations. Simulation studies show that the proposed design substantially outperforms the conventional multi-arm balanced factorial trial design. The proposed design yields a significantly higher probability for selecting the best treatment while at the same time allocating substantially more patients to efficacious treatments.