  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.
