When Does IPCW Help? Simulation and Real-World Evidence on Censoring Adjustment in Survival Analysis

Estimating treatment effects from time-to-event data in observational studies requires careful adjustment for both confounding and informative censoring. While inverse probability of treatment weighting (IPTW) and inverse probability of censoring weighting (IPCW) have been used to address these sources of bias separately, their combined application remains underexplored, especially in high-dimensional, real-world datasets. In this paper, we benchmark IPTW, IPCW, and their combination to estimate survival curves, restricted mean survival time (RMST), and hazard ratios (HR). Our simulation studies vary strengths of informative censoring and introduce non-proportional hazards, while our real-world study uses a large-scale electronic health record (EHR) dataset (~ 50,000 covariates and >40,000 patients). Our simulations showed that IPCW reduces survival curve estimation error in the presence of informative censoring, but only reduces HR and RMST bias when the strength of informative censoring additionally differs by treatment group. In our real-world study, IPTW alone was typically sufficient for HR estimation, suggesting that when confounding is the primary source of bias and well-addressed through large-scale adjustment, censoring adjustment may yield limited additional benefit. Ultimately, the utility of IPCW likely depends on the underlying data-generating process, the relative magnitude of censoring bias, and the estimand of interest.