Propensity-score matching with GAN-generated observations from electronic health records: simulation study and application to the evaluation of prone positioning in COVID-19 patients under mechanical ventilation

Background Propensity score (PS) methods are widely used in observational studies to estimate causal effects, but they often exclude patients due to a lack of comparable counterparts, leading to reduced power and potential bias. Generative adversarial networks (GANs) have shown promise in creating synthetic data, but their application to causal inference remains underexplored. Synthetic data could be used as plausible counterfactuals, potentially mitigating the issues of the PS methods. This study evaluates the integration of GAN-generated synthetic observations into propensity score matching (PSM) to improve the emulation of RCTs, using both simulated and real-world electronic health record (EHR) data. Methods A simulation study was conducted using with predefined confounding structures to compare traditional PSM against two hybrid approaches incorporating CTGAN-generated synthetic patients: partial hybrid matching (PHM) and full hybrid matching (FHM). Treatment effects were estimated via logistic regression, and performance was assessed by bias, standard error, alpha risk, power, and confidence interval coverage. The methods were applied to a real-world dataset of mechanically ventilated COVID-19 patients to evaluate the impact of early prone positioning on 28-day mortality. Results In simulations, PHM and FHM retained nearly all patients compared to PSM, which dropped up to 60% in unbalanced scenarios. While synthetic augmentation improved sample size, unadjusted use of synthetic matches led to underestimated standard errors and inflated type I error. Down-weighting matched synthetic data improved error control but did not consistently outperform PSM in bias or power. In the real-world application (n=1399), treatment effect estimates for prone positioning were similar across all methods and did not reach statistical significance. Conclusion GAN-augmented propensity score matching can reduce sample loss. However, it's current application in causal inference through PS matching remains limited. Synthetic data do not contribute independent information and must be cautiously integrated to avoid misleading precision. While promising, current GAN implementations require methodological refinements before routine use in causal inference.