Faculty Collaboration Database

Instrumental variable with competing risk model. Stat Med 2017 Apr 15;36(8):1240-1255

Date

01/09/2017

Pubmed ID

28064466

Pubmed Central ID

PMC5479873

DOI

10.1002/sim.7205

Scopus ID

2-s2.0-85008471466 (requires institutional sign-in at Scopus site)   11 Citations

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.

Author List

Zheng C, Dai R, Hari PN, Zhang MJ

Authors

Parameswaran Hari MD Adjunct 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

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

Clinical Trials as Topic
Humans
Models, Statistical
Proportional Hazards Models
Randomized Controlled Trials as Topic
Risk Factors
Sample Size
Treatment Outcome