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 model. Such model specification helps avoid type I error inflation caused by excessive shrinkage under typical hierarchical models. The operating characteristics of RoBoT are assessed through computer simulations and are compared with existing methods. Finally, we apply RoBoT to data from two recent phase II basket trials of imatinib and vemurafenib, respectively.
Keywords: Dirichlet process; Hierarchical model; Multiplicity; Oncology; Targeted therapy.
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