Bayesian hierarchical models for adaptive basket trial designs

Basket trials evaluate a single drug targeting a single genetic variant in multiple cancer cohorts. Empirical findings suggest that treatment efficacy across baskets may be heterogeneous. Most modern basket trial designs use Bayesian methods. These methods require the prior specification of at least one parameter that permits information sharing across baskets. In this study, we provide recommendations for selecting a prior for scale parameters for adaptive basket trials by using Bayesian hierarchical modeling. Heterogeneity among baskets attracts much attention in basket trial research, and substantial heterogeneity challenges the basic assumption of exchangeability of Bayesian hierarchical approach. Thus, we also allowed each stratum-specific parameter to be exchangeable or nonexchangeable with similar strata by using data observed in an interim analysis. Through a simulation study, we evaluated the overall performance of our design based on statistical power and type I error rates. Our research contributes to the understanding of the properties of Bayesian basket trial designs.

Automated summary

Basket trials in cancer research evaluate the effects of a single drug targeting a specific genetic variant across multiple cancer cohorts. Heterogeneity in treatment efficacy among these cohorts poses a challenge to the exchangeability assumption in Bayesian hierarchical modeling, which is commonly used in modern basket trial designs. This study provides recommendations for selecting prior distributions for scale parameters in adaptive basket trials using Bayesian hierarchical modeling, and evaluates the performance of the design through simulation studies on statistical power and type I error rates. Our research contributes to the understanding of the properties of Bayesian basket trial designs.