Bayesian monitoring of Phase II trials in cancer chemoprevention

Early randomized Phase II cancer chemoprevention trials which assess short- term biological activity are critical to the decision process to advance to late Phase II/Phase III trials. We have adapted published Bayesian interim analysis methods (Spiegelhalter et al., J. R. Statist. Soc A, 1994; 157: 3 57-416) which give greater flexibility and simplicity of inference to the m onitoring of randomized controlled Phase II trials using intermediate endpo ints. The Bayesian stopping rule is designed to stop the trial more quickly when the evidence suggests ineffectiveness rather than when it suggests bi ological activity, thus allowing resources to be concentrated on those agen ts that show the most promise in this early stage of testing. We investigat e frequentist performance characteristics of the proposed method through si mulation of randomized placebo controlled trials with a growth factor inter mediate end-point using mean and variance values derived from the literatur e. Simulation results show expected error rates and trial size similar to o ther commonly used group sequential methods for this setting. These results suggest that the Bayesian approach to interim analysis is well suited for monitoring small randomized controlled Phase II chemoprevention trials fur early detection of either inactive or promising agents. J CLIN EPIDEMIOL 52 ;8:705-711, 1999. (C) 1999 Elsevier Science Inc.