TY - JOUR

T1 - Instrumental variable estimation of the causal hazard ratio

AU - Wang, Linbo

AU - Tchetgen, Eric Tchetgen

AU - Martinussen, Torben

AU - Vansteelandt, Stijn

N1 - This article is protected by copyright. All rights reserved.

PY - 2023

Y1 - 2023

N2 - Cox's proportional hazards model is one of the most popular statistical models to evaluate associations of exposure with a censored failure time outcome. When confounding factors are not fully observed, the exposure hazard ratio estimated using a Cox model is subject to unmeasured confounding bias. To address this, we propose a novel approach for the identification and estimation of the causal hazard ratio in the presence of unmeasured confounding factors. Our approach is based on a binary instrumental variable, and an additional no-interaction assumption in a first stage regression of the treatment on the IV and unmeasured confounders. We propose, to the best of our knowledge, the first consistent estimator of the (population) causal hazard ratio within an instrumental variable framework. A version of our estimator admits a closed-form representation. We derive the asymptotic distribution of our estimator, and provide a consistent estimator for its asymptotic variance. Our approach is illustrated via simulation studies and a data application. This article is protected by copyright. All rights reserved.

AB - Cox's proportional hazards model is one of the most popular statistical models to evaluate associations of exposure with a censored failure time outcome. When confounding factors are not fully observed, the exposure hazard ratio estimated using a Cox model is subject to unmeasured confounding bias. To address this, we propose a novel approach for the identification and estimation of the causal hazard ratio in the presence of unmeasured confounding factors. Our approach is based on a binary instrumental variable, and an additional no-interaction assumption in a first stage regression of the treatment on the IV and unmeasured confounders. We propose, to the best of our knowledge, the first consistent estimator of the (population) causal hazard ratio within an instrumental variable framework. A version of our estimator admits a closed-form representation. We derive the asymptotic distribution of our estimator, and provide a consistent estimator for its asymptotic variance. Our approach is illustrated via simulation studies and a data application. This article is protected by copyright. All rights reserved.

U2 - 10.1111/biom.13792

DO - 10.1111/biom.13792

M3 - Journal article

C2 - 36377509

VL - 79

SP - 539

EP - 550

JO - Biometrics

JF - Biometrics

SN - 0006-341X

IS - 2

ER -