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

Stat Med. 2009 Jul 30;28(17):2185-201. doi: 10.1002/sim.3609.

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).

MeSH terms

  • Bayes Theorem*
  • Biometry
  • Breast Neoplasms / drug therapy
  • Clinical Trials as Topic / statistics & numerical data*
  • Clinical Trials, Phase II as Topic / statistics & numerical data
  • Clinical Trials, Phase III as Topic / statistics & numerical data
  • Female
  • Humans
  • Likelihood Functions
  • Magnesium Sulfate / therapeutic use
  • Models, Statistical
  • Monte Carlo Method
  • Myocardial Infarction / drug therapy
  • Myocardial Infarction / mortality
  • Neoplasm Recurrence, Local / prevention & control
  • Odds Ratio
  • Randomized Controlled Trials as Topic / statistics & numerical data
  • Sample Size
  • Selective Estrogen Receptor Modulators / therapeutic use
  • Tamoxifen / therapeutic use

Substances

  • Selective Estrogen Receptor Modulators
  • Tamoxifen
  • Magnesium Sulfate