Bayesian adaptive model selection for optimizing group sequential clinical trials

This article presents a new approach to the problem of deriving an optimal design for a randomized group sequential clinical trial based on right-censored event times. We are motivated by the fact that, if the proportional hazards assumption is not met, then a conventional design's actual power can differ substantially from its nominal value. We combine Bayesian decision theory, Bayesian model selection and forward simulation (FS) to obtain a group sequential procedure that maintains targeted false-positive rate and power, under a wide range of true event time distributions. At each interim analysis, the method adaptively chooses the most likely model and then applies the decision bounds that are optimal under the chosen model. A simulation study comparing this design with three conventional designs shows that, over a wide range of distributions, our proposed method performs at least as well as each conventional design, and in many cases it provides a much smaller trial.