A Propensity-Score Integrated Approach to Bayesian Dynamic Power Prior Borrowing

Abstract

Use of historical control data to augment a small internal control arm in a randomized control trial (RCT) can lead to significant improvement of the efficiency of the trial. It introduces the risk of potential bias, since the historical control population is often rather different from the RCT. Power prior approaches have been introduced to discount the historical data to mitigate the impact of the population difference. However, even with a Bayesian dynamic borrowing which can discount the historical data based on the outcome similarity of the two populations, a considerable population difference may still lead to a moderate bias. Hence, a robust adjustment for the population difference using approaches such as the inverse probability weighting or matching, can make the borrowing more efficient and robust. In this article, we propose a novel approach integrating the propensity score for the covariate adjustment and Bayesian dynamic borrowing using power prior. The proposed approach uses Bayesian bootstrap in combination with the empirical Bayes (EB) method using quasi-likelihood for determining the power prior. The performance of our approach is examined by a simulation study. We apply the approach to two Acute Myeloid Leukemia (AML) studies for illustration.

KEYWORDS:

• Bayesian bootstrap
• Dynamic borrowing
• Empirical Bayes
• Power prior
• Propensity score

Acknowledgments

The authors would like to thank the associate editor and two referees for detailed comments and suggestions, which led to substantial improvement of the manuscript.

Disclosure Statement

The authors confirm that there are no competing interests to declare.

Additional information

Funding

The author(s) reported there is no funding associated with the work featured in this article.