Power Priors Based on Multiple Historical Studies for Binary Outcomes

TL;DR

This paper introduces an adaptive empirical Bayes method using power priors to incorporate multiple historical studies into current clinical trial analysis for binary outcomes, improving data integration and decision-making.

Contribution

It develops a novel empirical Bayes approach for adaptively weighting multiple historical studies in Bayesian analysis of binary data, enhancing flexibility and robustness.

Findings

The method performs well in simulations compared to alternatives.

It effectively adapts weights based on data similarity.

Application to antibiotic trials demonstrates practical utility.

Abstract

Incorporating historical information into the design and analysis of a new clinical trial has been the subject of much recent discussion. For example, in the context of clinical trials of antibiotics for drug resistant infections, where patients with specific infections can be difficult to recruit, there is often only limited and heterogeneous information available from the historical trials. To make the best use of the combined information at hand, we consider an approach based on the multiple power prior which allows the prior weight of each historical study to be chosen adaptively by empirical Bayes. This choice of weight has advantages in that it varies commensurably with differences in the historical and current data and can choose weights near 1 if the data from the corresponding historical study are similar enough to the data from the current study. Fully Bayesian approaches are also considered. The methods are applied to data from antibiotics trials. An analysis of the operating characteristics in a binomial setting shows that the proposed empirical Bayes adaptive method works well, compared to several alternative approaches, including the meta-analytic prior.