A basket trial in oncology often enrolls patients with a particular molecular status, such as biomarker or gene alterations among cancer types, and evaluates the efficacy and safety of the targeted therapy to the corresponding molecular characteristic across cancer types. Because of the limited number of patients enrolled for each cancer type, statistical inference for treatment effects using such sparse data and evaluation of its homogeneity across cancer types can be challenging. A hierarchical Bayesian model shrinks the effect of the targeted therapy for each cancer type to the global effect under the assumption that the effects among cancer types are inherently exchangeable and correlated. The exchangeability and non-exchangeability model, which is an extension of the hierarchical Bayesian model, allows each cancer-specific parameter to be exchangeable with other similar cancer-type parameters, or to be non-exchangeable with any of them. Apart from these hierarchical modeling methods, an approach based on Bayesian model averaging for testing the effectiveness/ineffectiveness of treatment for each cancer type has been proposed. In this paper, we provide an overview these statistical methodologies along with a software application.
