A Bayesian G-formula Approach for Dynamic Borrowing in Hybrid Controlled Trials with Partially Missing Data

Music City Center 

Using external control data can improve the efficiency of randomized controlled trials (RCTs). However, the heterogeneity of distributions of covariates between current and external control data complicates direct borrowing. Covariate adjustment methods, such as outcome regression or propensity score approaches, combined with dynamic borrowing techniques, have been proposed to mitigate this problem. However, key prognostic factors may be partially missing in real world data, forcing researchers to rely on complete case analyses. Under certain covariate-dependent missingness mechanisms, outcome regression models can still yield unbiased estimates, whereas propensity score methods may fail. In this study, we propose a Bayesian g-formula approach that dynamically borrows external control data with partially missing data. Through simulation study, we evaluate the performance of our proposed method relative to existing approaches, including propensity score matching with dynamic borrowing. Our findings highlight the potential advantages of the Bayesian g-formula framework in complete case analyses while preserving the benefits of external data utilization.

Dynamic Borrowing