Doubly robust estimation and causal inference for recurrent event data |
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Authors: | Chien-Lin
Su Russell Steele Ian Shrier |
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Institution: | 1. Department of Mathematics and Statistics, McGill University, Montréal, Canada;2. Centre for Clinical Epidemiology, Lady Davis Institute, Jewish General Hospital, McGill University, Montréal, Canada |
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Abstract: | Many longitudinal databases record the occurrence of recurrent events over time. In this article, we propose a new method to estimate the average causal effect of a binary treatment for recurrent event data in the presence of confounders. We propose a doubly robust semiparametric estimator based on a weighted version of the Nelson-Aalen estimator and a conditional regression estimator under an assumed semiparametric multiplicative rate model for recurrent event data. We show that the proposed doubly robust estimator is consistent and asymptotically normal. In addition, a model diagnostic plot of residuals is presented to assess the adequacy of our proposed semiparametric model. We then evaluate the finite sample behavior of the proposed estimators under a number of simulation scenarios. Finally, we illustrate the proposed methodology via a database of circus artist injuries. |
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Keywords: | average causal effect confounder multiplicative rate model Nelson-Aalen estimator recurrent events |
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