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Instrumental variable with competing risk model
Author(s) -
Zheng Cheng,
Dai Ran,
Hari Parameswaran N.,
Zhang MeiJie
Publication year - 2017
Publication title -
statistics in medicine
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.996
H-Index - 183
eISSN - 1097-0258
pISSN - 0277-6715
DOI - 10.1002/sim.7205
Subject(s) - instrumental variable , estimator , covariate , econometrics , causal inference , outcome (game theory) , inference , statistics , hazard , variable (mathematics) , mathematics , computer science , artificial intelligence , mathematical analysis , chemistry , mathematical economics , organic chemistry
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.