Instrumental variable estimation of the causal hazard ratio
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Instrumental variable estimation of the causal hazard ratio. / Wang, Linbo; Tchetgen, Eric Tchetgen; Martinussen, Torben; Vansteelandt, Stijn.
In: Biometrics, Vol. 79, No. 2, 2023, p. 539-550.Research output: Contribution to journal › Journal article › Research › peer-review
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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 -
ID: 327400014