Medical College of Wisconsin
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Instrumental variable with competing risk model. Stat Med 2017 04 15;36(8):1240-1255 PMID: 28064466 PMCID: PMC5479873

Pubmed ID





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.

Author List

Zheng C, Dai R, Hari PN, Zhang MJ


Parameswaran Hari MD Chief, Professor in the Medicine department at Medical College of Wisconsin
Mei-Jie Zhang PhD Professor in the Institute for Health and Equity department at Medical College of Wisconsin


2-s2.0-85008471466   4 Citations

MESH terms used to index this publication - Major topics in bold

Clinical Trials as Topic
Models, Statistical
Proportional Hazards Models
Randomized Controlled Trials as Topic
Risk Factors
Sample Size
Treatment Outcome
jenkins-FCD Prod-331 a335b1a6d1e9c32173c9534e6f6ff51494143916