Inverse probability weighting to estimate exposure effects on the burden of recurrent outcomes in the presence of competing events.

Recurrent events - outcomes that an individual can experience repeatedly over the course of follow-up - are common in epidemiologic and health services research. Studies involving recurrent events often focus on time to first occurrence or on event rates which assume constant hazards over time. In this paper, we contextualize recurrent event parameters of interest using counterfactual theory in a causal inference framework and describe an approach for estimating a target parameter referred to as the mean cumulative count. This approach leverages inverse probability weights to control measured confounding with an existing (and underutilized) nonparametric estimator of recurrent event burden first proposed by Dong et al. in 2015. We use simulations to demonstrate the unbiased estimation of the mean cumulative count using the weighted Dong-Yasui estimator in a variety of scenarios. The weighted Dong-Yasui estimator for the mean cumulative count allows researchers to use observational data to flexibly estimate and contrast the expected number of cumulative events experienced per individual by a given time point under different exposure regimens. We provide a SAS macro to ease application of this method.