A Generalized Calibrated Bayesian Hierarchical Modeling Approach to Basket Trials With Multiple Endpoints

Authors

Xiaohan Chi
Ying Yuan
Zhangsheng Yu
Ruitao Lin

Publication Date

3-1-2024

Journal

Biomedical Journal

Abstract

A basket trial simultaneously evaluates a treatment in multiple cancer subtypes, offering an effective way to accelerate the drug development in multiple indications. Many basket trials are designed and monitored based on a single efficacy endpoint, primarily the tumor response. For molecular targeted or immunotherapy agents, however, a single efficacy endpoint cannot adequately characterize the treatment effect. It is increasingly important to use more complex endpoints to comprehensively assess the risk–benefit profile of such targeted therapies. We extend the calibrated Bayesian hierarchical modeling approach (Chu and Yuan, 2018a) to monitor phase II basket trials with multiple endpoints. We propose two generalizations, one based on the latent variable approach and the other based on the multinomial-normal hierarchical model, to accommodate different types of endpoints and dependence assumptions regarding information sharing. We introduce shrinkage parameters as functions of statistics measuring homogeneity among subgroups, and propose a general calibration approach to determine the functional forms. Theoretical properties of the generalized hierarchical models are investigated. Simulation studies demonstrate that the monitoring procedure based on the generalized approach yields desirable operating characteristics.

Keywords

Humans, Bayes Theorem, Neoplasms, Computer Simulation, Molecular Targeted Therapy, Research Design, Basket trials, Bayesian hierarchical model, information borrowing, multiple outcomes, nested endpoints

Comments

Supplementary Materials

PMID: 38368277