  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). In our proposed local-MEM
framework, information borrowing is only allowed to occur locally, i.e., among
baskets with similar response rate and the amount of information borrowing is
determined by the level of similarity in response rate, whereas baskets not
considered similar are not allowed to share information. We construct a
two-stage design for phase II basket trials using the proposed strategy. The
proposed method is compared to competing Bayesian methods and Simon's two-stage
design in a variety of simulation scenarios. We demonstrate the proposed method
is able to maintain the family-wise type I error rate at a reasonable level and
has desirable basket-wise power compared to Simon's two-stage design. 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.
