Bayesian Methods Incorporating Baseline Covariates for Borrowing Historical Control Data in Randomized Clinical Trials: A Neutral Comparison Study

The idea of leveraging historical control data in randomized clinical trials (RCTs) has gained a lot of attention, in particular for rare disease and pediatric indications where conducting traditional and fully powered RCTs can be challenging. In this regard, Bayesian dynamic borrowing (BDB) methods, which adapt the amount of borrowing based on the outcome heterogeneity between the current and historical data, are well established. In recent years, an increasing number of related methods have been published that combine the BDB framework with approaches that account for baseline covariates. The underlying rationale is that some of the heterogeneity may be explained systematically by variations in the distributions of these characteristics. While many such methods are now available, direct comparisons of them are scarce and there is no consensus on which one can be considered the state of the art. We address this issue by conducting a neutral comparison of three methods of this class, namely the propensity score weighted commensurate prior, propensity score weighted multi‐source exchangeability models, and Bayesian additive regression trees. We assess their performance for continuous outcomes through extensive simulations and further evaluate them using two examples based on real RCT data. Our study reveals potential issues with some of these approaches and provides recommendations for choosing a particular method for real RCTs.