Bayesian Adaptive Trial Design with Multiple Efficacy Endpoints for Heterogeneous Subpopulations

• Activity Number: 633
• Type: Topic Contributed
• Date/Time: Thursday, August 13, 2015 : 8:30 AM to 10:20 AM
• Sponsor: Section on Bayesian Statistical Science
• Author(s): Lindsay Renfro* and Bradley P. Carlin and Daniel Sargent
• Companies: Mayo Clinic and University of Minnesota and Mayo Clinic
• Keywords: clinical trials ; adaptive design ; subpopulations ; multiple endpoints

Abstract

Clinical trials are the cornerstone of evidence-based medicine and the gold standard for establishing causal relationships between new treatments and improved patient outcomes. However, as diseases like cancer are increasingly understood on the molecular level, clinical trials that are designed for too-general patient populations will often fail to reveal subpopulations where a therapy is more or less effective. Inefficient or otherwise sub-optimal study endpoints compound the challenges to reaching early and precise conclusions regarding treatment benefit, particularly when multiple patient subgroups enrolled a trial respond to treatment via different endpoints, thereby "washing out" the overall effect. We develop a randomized clinical trial design capable of detecting efficacy through multiple endpoints (e.g., binary and time-to-event) and in the presence of possible patient subpopulations (e.g., biomarker defined). This design allows patients from different subgroups to respond to treatment via different mechanisms, i.e., through different endpoints. Using both simulations and patient-level data from a collection of cancer clinical trials, we derive the operating characteristi