P20 Propensity Score Weighted Restricted Mean Survival Time (RMST) Model for Marginal Causal Effect in Observational Data

The Restricted Mean Survival Time (RMST) is a preferred marginal effect measure in causal survival inference since it can provide valid causal interpretations and is robust to nonproportional hazard situations. Propensity score adjustments are popular methods to adjust for observed confounding, which usually includes stratification, matching and weighting. In comparison to propensity score stratification and matching methods, propensity score weighting demonstrates the potential to yield a doubly robust estimator under specific semiparametric theory. Furthermore, the weighting procedure can be seamlessly integrated with regression models, enabling convenient adjustment for baseline covariates.

In this paper, we propose a new estimation method that uses the RMST difference as a marginal causal effect measurement and incorporates propensity score weighting adjustment to address observed confounding. The general idea is to construct an augmented inverse probability of treatment weighting (IPTW) estimator based on estimating equations. We consider both the propensity score model and outcome model in the estimating equations, while adjusting for censoring using inverse probability of censoring weighting (IPCW).

The proposed propensity score weighted RMST estimation strategy yields a doubly robust causal effect estimator with adjustment for measured confounding bias, baseline covariates, and censoring. It exhibits robust performance in our simulation evaluation, accompanied by technical proof of its doubly robust property. To enhance practical understanding, we also apply the proposed method to examine the causal effect of Direct Oral Anticoagulants (DOACs) compared to Warfarin in reducing the risk of cardiovascular events.

RWE

ASA Biopharmaceutical Section Regulatory-Industry Statistics Workshop 2024