Commensurate Priors for Incorporating Historical Information in Clinical Trials Using General and Generalized Linear Models

• Activity Number: 611
• Type: Topic Contributed
• Date/Time: Thursday, August 4, 2011 : 8:30 AM to 10:20 AM
• Sponsor: Biopharmaceutical Section
• Author(s): Brian P. Hobbs and Daniel J. Sargent and Bradley P. Carlin*+
• Companies: The University of Texas MD Anderson Cancer Center and Mayo Clinic and University of Minnesota
• Keywords: Adaptive designs ; Bayesian analysis ; Historical data ; Clinical trials

Abstract

Bayesian clinical trial designs offer the possibility of a substantially reduced sample size, increased statistical power, and reductions in cost and ethical hazard. However when prior and current information conflict, Bayesian methods can lead to higher than expected Type I error, as well as the possibility of a costlier and lengthier trial. The commensurate prior method proposed by Hobbs et al. (2009) offers an adaptive approach for incorporating historical data that is robust to prior information that reveals itself to be inconsistent with the accumulating experimental data. In this paper, we extend the general method to linear and linear mixed models as well as generalized linear and generalized linear mixed models. We also provide a sample analysis of a colon cancer trial comparing time-to-disease progression rates using a Weibull regression model and close by discussing adaptive randomization schemes that balance the allocation ratio with respect to the amount of incorporated historical information.