Covariate Adjustment in Basket Trials Borrowing Information Across Subgroups.

Basket trials are an efficient approach to simultaneously evaluate a single therapy across multiple diseases where patients share a common molecular target. Bayesian hierarchical models (BHMs) are widely used to estimate the treatment effects while accounting for heterogeneity between patient subgroups within a basket trial. However, the use of analysis of covariance (ANCOVA) with treatment-by-covariate interaction terms, in this context of patient heterogeneity and small samples, has been largely unexplored, despite the widespread use of ANCOVA for improving estimation precision in traditional settings from a frequentist perspective. In this paper, we propose two covariate-adjusted BHMs that incorporate ANCOVA into the data model to enhance the estimation precision in basket trials, wherein borrowing of information is permitted across subgroups to a certain extent. Specifically, both ANCOVA without treatment-by-covariate interaction terms and ANCOVA with interaction terms are explored in the analysis of basket trials. We perform a simulation study to demonstrate the advantages of covariate-adjusted BHMs compared to unadjusted BHMs, as well as frequentist ANCOVA models. The BHMs are then retrospectively applied to the analysis of the MAJIC study, a randomized controlled basket trial involving two subtypes of blood cancer.