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

TL;DR

This paper introduces a new method combining propensity scores with Bayesian power priors to improve the use of historical control data in clinical trials, aiming to reduce bias and enhance efficiency.

Contribution

It proposes a novel integrated approach that uses propensity scores and Bayesian dynamic borrowing with power priors, incorporating Bayesian bootstrap and empirical Bayes methods.

Findings

The method effectively reduces bias from population differences.

Simulation studies demonstrate improved efficiency over existing methods.

Application to AML studies illustrates practical utility.

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 paper, we propose a novel approach integrating 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 method utilizing 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.