Abstract
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.
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