Optimal-Balanced Randomization for Controlled Clinical Trials That Incorporate Historical Controls

• Activity Number: 653
• Type: Topic Contributed
• Date/Time: Thursday, August 4, 2011 : 10:30 AM to 12:20 PM
• Sponsor: Section on Bayesian Statistical Science
• Author(s): Brian P. Hobbs*+ and Bradley P. Carlin
• Companies: The University of Texas MD Anderson Cancer Center 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. We investigate adaptive randomization schemes rooted in commensurate prior methods that balances the allocation ratio with respect to the amount of incorporated historical information. We refer to our approach as optimal-balance randomization, since we use current patients "optimally", with respect to the amount of information existing on the control therapy. This approach depends upon a reliable measure of the historical data's influence, namely the effective sample size of the historical controls. The method is illustrated using LMs and GLMs in context of two successive clinical trials.