Bayesian adaptive randomization design incorporating propensity score-matched historical controls.

Abstract

Incorporating historical control data to augment the control arm in randomized controlled trials (RCTs) is one way of increasing their efficiency and feasibility when adequate RCTs cannot be conducted. In recent work, a Bayesian adaptive randomization design incorporating historical control data has been proposed to reduce sample size according to the amount of information that could be borrowed, assessed at interim assessment in respect to prior-data conflict. However, the approach does not distinguish between the two sources of prior-data conflict: (1) imbalance in measured covariates, and (2) imbalance in unmeasured covariates. In this paper, we propose an extension of the Bayesian adaptive randomization design to incorporate propensity score-matched historical controls. At interim assessment, historical controls similar to the concurrent controls in terms of measured covariates are selected using propensity score matching. Then, final sample size of the control arm is adjusted according to the extent of borrowing from the matched historical controls quantified by effective historical sample size. The conditional power prior approach and commensurate prior approach are adopted for designing the prior, and addressing prior-data conflict due to unmeasured covariate imbalance. Simulation results show that the proposed method yields reduced bias in treatment effect estimates, type I error at the nominal level, and reduced sample size while maintaining statistical power. Even when residual imbalance exists due to unmeasured covariates, the proposed method borrowed more information without risking substantially inflated type I error and bias, providing meaningful implications for use of historical controls to facilitate the conduct of adequate RCTs.