Application of Bayesian statistics to decision making during a clinical trial

Abstract

We describe the application of Bayesian methods to the monitoring and analysis of a trial of treatment for patients with advanced colorectal carcinoma. We discuss the choice of prior distribution and justify the use of a truncated normal distribution with a probability mass at zero difference. The stopping rule, based on the trials of the posterior distribution and a chosen range of equivalence, yields an upper boundary very close to the Pocock group sequential boundary. The Bayes stopping rule is quite sensitive to the amount of probability mass at zero in the prior distribution.