A Bayesian Adaptive Design for Phase II Clinical Trials

• Activity Number: 555
• Type: Topic Contributed
• Date/Time: Wednesday, August 1, 2012 : 2:00 PM to 3:50 PM
• Sponsor: Section on Bayesian Statistical Science
• Author(s): Sijin Wen*+ and Donald Berry
• Companies: MD Anderson Cancer Center and MD Anderson Cancer Center
• Keywords: Bayesian Adaptive Design ; Clinical Trials ; Predictor ; Interim Analysis

Abstract

For a clinical trial with long-term outcome as its primary endpoint, it is difficult to use outcome-adaptive randomization in this trial. In practice, often information about a short-term response is quickly available during or shortly after treatment, and this short-term response is a good predictor for the primary outcome. We propose a new adaptive design for phase II clinical trials when such short-term response information is available. We model the relationship between short-term and long-term results with all available information, including some patients who have both short-term and long-term results and some patients who have only short-term results. We use the short-term information to "speed up" the adaptation of the randomization procedure. Interim analysis and final decision making are based upon inference on the primary outcome of long-term endpoint. Our simulation studies demonstrate that the new design can effectively assign patients to the better treatment arms and select better treatment arms with high probability.