A Bayesian basket trial design using a calibrated Bayesian hierarchical model.

ABSTRACT: BACKGROUND:The basket trial evaluates the treatment effect of a targeted therapy in patients with the same genetic or molecular aberration, regardless of their cancer types. Bayesian hierarchical modeling has been proposed to adaptively borrow information across cancer types to improve the statistical power of basket trials. Although conceptually attractive, research has shown that Bayesian hierarchical models cannot appropriately determine the degree of information borrowing and may lead to substantially inflated type I error rates. METHODS:We propose a novel calibrated Bayesian hierarchical model approach to evaluate the treatment effect in basket trials. In our approach, the shrinkage parameter that controls information borrowing is not regarded as an unknown parameter. Instead, it is d

SUBMITTER: Chu Y 

PROVIDER: S-EPMC5891374 | biostudies-literature | 2018 Apr

REPOSITORIES: biostudies-literature