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: 357-416) which give greater flexibility and simplicity of inference to the monitoring of randomized controlled Phase II trials using intermediate endpoints. The Bayesian stopping rule is designed to stop the trial more quickly when the evidence suggests ineffectiveness rather than when it suggests biological activity, thus allowing resources to be concentrated on those agents that show the most promise in this early stage of testing. We investigate frequentist performance characteristics of the proposed method through simulation of randomized placebo controlled trials with a growth factor intermediate end-point using mean and variance values derived from the literature. Simulation results show expected error rates and trial size similar to other 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 for early detection of either inactive or promising agents.