Bayesian Mixture Designs for Phase II Clinical Trials

In phase II efficacy trials, it is often desirable to assess patient response sequentially. Bayesian framework can be applied to develop sequential stopping rules. A single parametric model is often chosen to characterize the prior beliefs on a test drug, which might not capture adequately the variability associated with prior beliefs from multiple experts or multiple historic data sources. We use a class of mixture priors to develop robust Bayesian stopping rules. We present systematic methods to construct mixture priors and compare stopping rules of the mixture designs with existing designs.