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Abstract:
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A basket trial in oncology encompasses multiple “baskets” that simultaneously assess one treatment in multiple cancer types or subtypes. It is well recognized that hierarchical modeling methods, which adaptively borrow strength across baskets, can improve over simple pooling and stratification. We propose a novel Bayesian method, RoBoT (Robust Bayesian Hypothesis Testing), for the data analysis and decision making in phase II basket trials. In contrast to most existing methods that use posterior credible intervals to determine the efficacy of the new treatment, RoBoT builds upon a formal Bayesian hypothesis testing framework that leads to interpretable and robust inference. Specifically, we assume that the baskets belong to several latent subgroups, and within each subgroup, the treatment has similar probabilities of being more efficacious than controls, historical or concurrent. The number of latent subgroups and subgroup memberships are inferred by the data through a Dirichlet process mixture (DPM) model. Such model specification helps avoid type I error inflation caused by excessive shrinkage under typical hierarchical models. Examples will be given to demo RoBoT's performance.
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