Bayesian Basket Trial Design in Early Oncology: A Practical Local Power Prior Framework

Music City Center 

Basket trials have emerged as a powerful tool in early-phase oncology drug development, enabling the evaluation of targeted therapies across multiple tumor types within a single study. Bayesian methods are widely used to facilitate adaptive information borrowing while maintaining statistical rigor. This talk reviews recent advancements in Bayesian basket trial designs and introduces a novel 3-component local power prior framework that offers modeling flexibility, computational efficiency, and explicit interpretability to facilitate cross-functional discussions. The framework incorporates global borrowing control, dynamic pairwise similarity assessment, and a threshold to restrict borrowing in highly heterogeneous settings. It also accommodates unequal sample sizes across baskets. Simulations demonstrate that the proposed approach achieves performance comparable to or better than established methods, including EXNEX and other MCMC-based approaches, while significantly reducing computational burden. We will also discuss practical considerations in tuning the borrowing parameters and illustrate how the proposed approach can be effectively implemented in early phase oncology trials.

Bayesian basket trial design

Local power prior

Dynamic borrowing

Early oncology 

Biopharmaceutical Section