Bayesian clinical trial design using historical data that inform the treatment effect.

ABSTRACT: We consider the problem of Bayesian sample size determination for a clinical trial in the presence of historical data that inform the treatment effect. Our broadly applicable, simulation-based methodology provides a framework for calibrating the informativeness of a prior while simultaneously identifying the minimum sample size required for a new trial such that the overall design has appropriate power to detect a non-null treatment effect and reasonable type I error control. We develop a comprehensive strategy for eliciting null and alternative sampling prior distributions which are used to define Bayesian generalizations of the traditional notions of type I error control and power. Bayesian type I error control requires that a weighted-average type I error rate not exceed a prespecified

SUBMITTER: Psioda MA 

PROVIDER: S-EPMC6587921 | biostudies-literature | 2019 Jul

REPOSITORIES: biostudies-literature