Bayesian local exchangeability design for phase II basket trials

We propose an information borrowing strategy for the design and monitoring of phase II basket trials based on the local multisource exchangeability assumption between baskets (disease types). We construct a flexible statistical design using the proposed strategy. Our approach partitions potentially heterogeneous baskets into non-exchangeable blocks. Information borrowing is only allowed to occur locally, i.e., among similar baskets within the same block. The amount of borrowing is determined by between-basket similarities. The number of blocks and block memberships are inferred from data based on the posterior probability of each partition. The proposed method is compared to the multisource exchangeability model and Simon's two-stage design, respectively. In a variety of simulation scenarios, we demonstrate the proposed method is able to maintain the type I error rate and have desirable basket-wise power. In addition, our method is computationally efficient compared to existing Bayesian methods in that the posterior profiles of interest can be derived explicitly without the need for sampling algorithms.