Estimating the marginal hazard ratio by simultaneously using a set of propensity score models: A multiply robust approach

The inverse probability weighted Cox model is frequently used to estimate the marginal hazard ratio. Its validity requires a crucial condition that the propensity score model be correctly specified. To provide protection against misspecification of the propensity score model, we propose a weighted estimation method rooted in the empirical likelihood theory. The proposed estimator is multiply robust in that it is guaranteed to be consistent when a set of postulated propensity score models contains a correctly specified model. Our simulation studies demonstrate satisfactory finite sample performance of the proposed method in terms of consistency and efficiency. We apply the proposed method to compare the risk of postoperative hospitalization between sleeve gastrectomy and Roux-en-Y gastric bypass using data from a large medical claims and billing database. We further extend the development to multisite studies to enable each site to postulate multiple site-specific propensity score models.

Automated summary

The inverse probability weighted Cox model is used to estimate the marginal hazard ratio, requiring correct specification of the propensity score model. To address misspecification, a weighted estimation method rooted in empirical likelihood theory is proposed. The method demonstrates satisfactory performance in terms of consistency and efficiency in simulation studies and application to comparing postoperative hospitalization risks between two surgical procedures. Extending the method to multisite studies allows for site-specific propensity score models.