Doubly robust estimation and causal inference for recurrent event data. (28th April 2020)

• Record Type: Journal Article
• Authors: Su, Chien‐Lin; Steele, Russell; Shrier, Ian
• Is Part Of: Statistics in medicine. Volume 39:Number 17(2020)
• Page Start: 2324
• Page End: 2338
• Publication Date: 2020-04-28
• Subjects: average causal effect -- confounder -- multiplicative rate model -- Nelson‐Aalen estimator -- recurrent events; Medical statistics -- Periodicals; Statistique médicale -- Périodiques; Statistiques médicales -- Périodiques; 610.727
• DOI: 10.1002/sim.8541
• Languages: English
• ISSNs: 0277-6715

Abstract

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.