Causal inference with recurrent data via inverse probability treatment weighting method (IPTW)

TL;DR

This paper extends propensity score methods to recurrent event data in observational studies, demonstrating through simulations that IPTW estimators can reliably estimate hazard ratios under certain conditions, with considerations for censoring and variance estimation.

Contribution

It introduces a novel extension of propensity score IPTW methods specifically for recurrent data, addressing bias and variance estimation issues.

Findings

IPTW estimators accurately estimate hazard ratios without censoring.

Censoring introduces bias in stabilized IPTW estimates.

Robust variance estimators improve variance estimation accuracy.

Abstract

Propensity score methods are increasingly being used to reduce estimation bias of treatment effects for observational studies. Previous research has shown that propensity score methods consistently estimate the marginal hazard ratio for time to event data. However, recurrent data frequently arise in the biomedical literature and there is a paucity of research into the use of propensity score methods when data are recurrent in nature. The objective of this paper is to extend the existing propensity score methods to recurrent data setting. We illustrate our methods through a series of Monte Carlo simulations. The simulation results indicate that without the presence of censoring, the IPTW estimators allow us to consistently estimate the marginal hazard ratio for each event. Under administrative censoring regime, the stabilized IPTW estimator yields biased estimate of the marginal hazard ratio, and the degree of bias depends on the proportion of subjects being censored. For variance estimation, the na\"ive variance estimator often tends to substantially underestimate the variance of the IPTW estimator, while the robust variance estimator significantly reduces the estimation bias of the variance.