Instrumental Variable with Competing Risk Model

TL;DR

This paper develops an instrumental variable estimator within a semi-parametric additive hazard model to accurately assess treatment effects on competing risks, accounting for unmeasured confoundings, with proven asymptotic properties and validated through simulations and real data.

Contribution

It introduces a novel IV estimator for competing risk models that handles unmeasured confoundings, with theoretical and empirical validation.

Findings

Estimator performs well in finite samples

Unmeasured confoundings can bias effect estimates by about 50%

Method applied successfully to transplant data

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 co- variates. 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 pro- pose 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 per- formed 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).