A Two-Stage Bayesian Design with Sample-Size Re-Estimation and Subgroup Analysis for Phase II Binary Response Trials

• Activity Number: 178
• Type: Contributed
• Date/Time: Monday, August 5, 2013 : 10:30 AM to 12:20 PM
• Sponsor: Biopharmaceutical Section
• Author(s): Wei Zhong*+ and Joseph S. Koopmeiners and Bradley P. Carlin
• Companies: Genentech Inc. and University of Minnesota and University of Minnesota
• Keywords: Bayesian design ; clinical trial ; personalized medicine ; predictive approach ; sample size reestimation ; subgroup analysis

Abstract

Frequentist sample size determination for binary outcome data in a two-arm clinical trial requires initial guesses of the event probabilities. Misspecification of these event rates may lead to a poor estimate of the necessary sample size. In contrast, the Bayesian approach may offer a better, more flexible approach. The Bayesian sample size proposed by Whitehead et al.(2008) for exploratory studies justifies the acceptable minimum sample size by a "conclusiveness" condition. Here we introduce a new two-stage Bayesian design with sample size reestimation at the interim stage. Our design inherits the properties of good interpretation and easy implementation from Whitehead et al.(2008), generalizes their method to a two-sample setting, and uses a fully Bayesian predictive approach to reduce an overly large initial sample size when necessary. Moreover, our design can be extended to allow patient level covariates via logistic regression, adjusting sample size within each subgroup based on interim analyses. We illustrate the benefits of our approach with a design in non-Hodgkin lymphoma with a simple binary covariate, offering an initial step toward within-trial personalized medicine.