A1503
Title: Structural nested recurrent event model for estimating the effects of time-varying exposure
Authors: Ashkan Ertefaie - University of Pennsylvania (United States) [presenting]
Daniel Mork - (United States)
Francesca Dominici - Harvard University (United States)
Robert Strawderman - University of Rochester (United States)
Abstract: Assessing the causal effect of time-varying exposures on recurrent event processes is challenging in the presence of a terminating event. The objective is to estimate both the short-term and delayed marginal causal effects of exposures on recurrent events while accounting for bias introduced by a potentially correlated terminal event. Existing estimators based on marginal structural models and proportional rate models are unsuitable for estimating delayed marginal causal effects for many reasons, and furthermore, they do not account for competing risks associated with a terminating event. To address these limitations, we propose a class of semiparametric structural nested recurrent event models and two estimators of short-term and delayed marginal causal effects of exposures. We establish the asymptotic linearity of these two estimators under regularity conditions through the novel use of modern empirical process and semiparametric efficiency theory. We examine the performance of these estimators via simulation and provide an R package sncure to apply our methods in real data scenarios. Finally, we present the utility of our methods in the context of a large epidemiological study of 299,661 Medicare beneficiaries, where we estimate the effects of fine particulate matter air pollution on recurrent hospitalizations for cardiovascular disease.
