Subgroup-Based Adaptive (SUBA) Designs for Multi-Arm Biomarker Trials

• Activity Number: 173
• Type: Contributed
• Date/Time: Monday, August 4, 2014 : 10:30 AM to 12:20 PM
• Sponsor: Biopharmaceutical Section
• Author(s): Yanxun Xu*+ and Lorenzo Trippa and Peter Mueller and Yuan Ji
• Companies: and Harvard School of Public Health and University of Texas at Austin and NorthShore University HealthSystem
• Keywords: Adaptive designs ; Bayesisan inference ; Biomarkers ; Posterior ; Subgroup identification ; Targeted therapies

Abstract

Targeted therapies based on biomarker profiling are becoming mainstream directions of cancer research and treatment. Depending on the expression of specific prognostic biomarkers, the strategy of targeted therapies is to apply different cancer drugs to subgroups of patients even if they are diagnosed with the same type of cancer by traditional means, such as tumor location. For example, Herceptin is only applicable to the subgroup of patents with HER2+ breast cancer, but not other types of breast cancer. However, subgroups like HER2+ breast cancer with effective targeted therapies are rare and most cancer drugs are still being applied to a bulk of patients among whom some might not respond or benefit. Also, the behaviors of targeted agents in human are usually unpredictable. To address these issues, we propose SUBA, subgroup-based adaptive designs that simultaneously search for prognostic subgroups and allocate patients adaptively to the best subgroup-specific treatments throughout the course of the trial. The main features of SUBA include the continuous reclassification of patient subgroups based on a random partition model and the adaptive allocation of patients to the best treat