Instrumental variables estimation with competing risk data

TL;DR

This paper develops a semi-parametric instrumental variables method for estimating cause-specific hazard effects in competing risks data, addressing unmeasured confounding and allowing flexible covariate adjustment.

Contribution

It introduces a novel approach to incorporate instrumental variables into competing risk analysis under weak assumptions, with closed-form estimators and practical applicability.

Findings

Method performs well in simulation studies

Enables covariate adjustment in competing risks

Applied to mammography screening data

Abstract

Time-to-event analyses are often plagued by both -- possibly unmeasured -- confounding and competing risks. To deal with the former, the use of instrumental variables for effect estimation is rapidly gaining ground. We show how to make use of such variables in competing risk analyses. In particular, we show how to infer the effect of an arbitrary exposure on cause-specific hazard functions under a semi-parametric model that imposes relatively weak restrictions on the observed data distribution. The proposed approach is flexible accommodating exposures and instrumental variables of arbitrary type, and enables covariate adjustment. It makes use of closed-form estimators that can be recursively calculated, and is shown to perform well in simulation studies. We also demonstrates its use in an application on the effect of mammography screening on the risk of dying from breast cancer