Bayesian hierarchical models for adaptive basket trial designs

Author:

Chen Chian1ORCID,Hsiao Chin‐Fu1ORCID

Affiliation:

1. Institute of Population Health Sciences National Health Research Institutes Miaoli County Taiwan

Abstract

AbstractBasket 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.

Funder

Ministry of Science and Technology

National Health Research Institutes

Publisher

Wiley

Subject

Pharmacology (medical),Pharmacology,Statistics and Probability

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