title: Instrumental variable with competing risk model.
abstract: In this paper, we discuss causal inference on the efficacy of a treatment or medication on a time-to-event outcome with competing risks. Although the treatment group can be randomized, there can be confoundings between the compliance and the outcome. Unmeasured confoundings may exist even after adjustment for measured covariates. Instrumental variable methods are commonly used to yield consistent estimations of causal parameters in the presence of unmeasured confoundings. On the basis of a semiparametric additive hazard model for the subdistribution hazard, we propose an instrumental variable estimator to yield consistent estimation of efficacy in the presence of unmeasured confoundings for competing risk settings. We derived the asymptotic properties for the proposed estimator. The estimator is shown to be well performed under finite sample size according to simulation results. We applied our method to a real transplant data example and showed that the unmeasured confoundings lead to significant bias in the estimation of the effect (about 50% attenuated). Copyright © 2017 John Wiley & Sons, Ltd.
authors.affiliation: Joseph. J. Zilber School of Public Health, University of Wisconsin-Milwaukee, Milwaukee, WI, U.S.A.
authors.forename: Cheng
authors.identifier: 0000-0002-6562-870X
authors.initials: C
authors.lastname: Zheng
authors.affiliation: Department of Statistics, University of Chicago, Chicago, IL, U.S.A.
authors.forename: Ran
authors.initials: R
authors.lastname: Dai
authors.affiliation: Division of Hematology and Oncology, Medical College of Wisconsin, Milwaukee, WI, U.S.A.
authors.forename: Parameswaran N
authors.initials: PN
authors.lastname: Hari
authors.affiliation: Division of Biostatistics, Medical College of Wisconsin, Milwaukee, WI, U.S.A.
authors.forename: Mei-Jie
authors.initials: MJ
authors.lastname: Zhang
mesh_terms: D002986:Clinical Trials as Topic / Q000379:methods; D006801:Humans; D015233:Models, Statistical; D016016:Proportional Hazards Models; D016032:Randomized Controlled Trials as Topic / Q000379:methods; D012307:Risk Factors; D018401:Sample Size; D016896:Treatment Outcome
keywords: additive hazard model; competing risk; instrumental variable; survival analysis