Mixtures of prior distributions for predictive Bayesian sample size calculations in clinical trials

Abstract

In this paper we propose a predictive Bayesian approach to sample size determination (SSD) and re-estimation in clinical trials, in the presence of multiple sources of prior information. The method we suggest is based on the use of mixtures of prior distributions for the unknown quantity of interest, typically a treatment effect or an effects-difference. Methodologies are developed using normal models with mixtures of conjugate priors. In particular we extend the SSD analysis of Gajewski and Mayo (Statist. Med. 2006; 25:2554-2566) and the sample size re-estimation technique of Wang (Biometrical J. 2006; 48(5):1-13).