Instrumental Variable with Competing Risk Model

• Activity Number: 496
• Type: Contributed
• Date/Time: Wednesday, August 3, 2016 : 8:30 AM to 10:20 AM
• Sponsor: Biometrics Section
• Author(s): Cheng Zheng* and Ran Dai and Parameswaran Hari and Mei-Jie Zhang
• Companies: University of Wisconsin - Milwaukee and The University of Chicago and Medical College of Wisconsin and Medical College of Wisconsin
• Keywords: Causal inference ; Competing risk ; Instrumental variable ; Additive hazard model ; Survival analysis

Abstract

In this paper, we discuss causal inference on the efficacy of a treatment or medication on a time to event outcome with competing risk. Although the treatment group can be randomized, there can be confoundings between the compliance and the outcome. Unmeasured confoundings might exist even after adjustment for measured covariates. Instrumental variable (IV) methods are commonly used to yield consistent estimations of causal parameters in the presence of unmeasured confoundings. Based on a semi-parametric additive hazard model for the subdistribution hazard, we propose an instrumental variable estimator to yield consistent estimation of efficacy in the presence of unmeasured confounding for competing risks setting. We derive the asymptotic properties for the proposed estimator. The estimator is shown to be well performed under finite sample size according to simulation results. We applied our method to a real transplant data example and showed that the unmeasured confounding could lead to significant bias in the estimation of the effect (about 50% attenuated).