Bayesian adaptive basket trial design using model averaging.

ABSTRACT: In this article, we develop a Bayesian adaptive design methodology for oncology basket trials with binary endpoints using a Bayesian model averaging framework. Most existing methods seek to borrow information based on the degree of homogeneity of estimated response rates across all baskets. In reality, an investigational product may only demonstrate activity for a subset of baskets, and the degree of activity may vary across the subset. A key benefit of our Bayesian model averaging approach is that it explicitly accounts for the possibility that any subset of baskets may have similar activity and that some may not. Our proposed approach performs inference on the basket-specific response rates by averaging over the complete model space for the response rates, which can include thousands of

SUBMITTER: Psioda MA 

PROVIDER: S-EPMC7846150 | biostudies-literature | 2021 Jan

REPOSITORIES: biostudies-literature