A Bayesian approach to establishang sample size and monitoring criteria for phase II clinical trials

Thall and Simon [1] propose a Bayesian approach to phase II clinical trials with binary outcomes and continuous monitoring. The efficacy θ_E of an experimental treatment E is evaluated relative to that of a standard treatment S based on data from an uncontrolled trial of E, an informative prior for θ_S, and a noninformative prior for θ_E. The trial continues until E is shown with high posterior probability to be either promising or not promising, or until a predetermined maximum sample size is reached. Operating characteristics are evaluated under fixed values of the success probability of E. In this paper, we propose two extensions of this decision structure, describe sample size and monitoring criteria, and provide numerical guidelines for implementation. The first extension gives criteria from early termination of trials unlikely to yield conclusive results, based on the marginal (predictive) distribution of the observed success rate. The second extension allows early termination only if E is found to be not promising compared to S. Operating characteristics of each of these designs are evaluated numerically over a range of design parameterizations. We also examine the effects of intermittent monitoring on the design's properties. An application of this approach to a leukemia biochemotherapy trial is described.