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    Rights statement: This is the peer reviewed version of the following article: Brueckner, M. , Titman, A. and Jaki, T. (2019), Instrumental variable estimation in semi‐parametric additive hazards models. Biometrics 75 doi: 10.1111/biom.12952 which has been published in final form at https://onlinelibrary.wiley.com/doi/full/10.1111/biom.12952 This article may be used for non-commercial purposes in accordance With Wiley Terms and Conditions for self-archiving.

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Instrumental variable estimation in semi-parametric additive hazards models

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Instrumental variable estimation in semi-parametric additive hazards models. / Brueckner, Matthias; Titman, Andrew Charles; Jaki, Thomas Friedrich.

In: Biometrics, Vol. 75, No. 1, 01.03.2019, p. 110-120.

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@article{613197829aa44f579cf2ed789ce0b971,
title = "Instrumental variable estimation in semi-parametric additive hazards models",
abstract = "Instrumental variable methods allow unbiased estimation in the presence of unmeasured confounders when an appropriate instrumental variable is available. Two-stage least-squares and residual inclusion methods have recently been adapted to additive hazard models for censored survival data. The semi-parametric additive hazard model which can include time-independent and time-dependent covariate effects is particularly suited for the two-stage residual inclusion method, since it allows direct estimation of time-independent covariate effects without restricting the effect of the residual on the hazard.In this article we prove asymptotic normality of two-stage residual inclusion estimators of regression coefficients in a semi-parametric additive hazard model with time-independent and time-dependent covariate effects. We consider the cases of continuous and binary exposure. Estimation of the conditional survival function given observed covariates is discussed and a resampling scheme is proposed to obtain simultaneous confidence bands. The new methods are compared to existing ones in a simulation study and are applied to a real data set. The proposed methods perform favourably especially in cases with exposure-dependent censoring.",
author = "Matthias Brueckner and Titman, {Andrew Charles} and Jaki, {Thomas Friedrich}",
note = "This is the peer reviewed version of the following article: Brueckner, M. , Titman, A. and Jaki, T. (2019), Instrumental variable estimation in semi‐parametric additive hazards models. Biometrics 75 doi: 10.1111/biom.12952 which has been published in final form at https://onlinelibrary.wiley.com/doi/full/10.1111/biom.12952 This article may be used for non-commercial purposes in accordance With Wiley Terms and Conditions for self-archiving.",
year = "2019",
month = mar,
day = "1",
doi = "10.1111/biom.12952",
language = "English",
volume = "75",
pages = "110--120",
journal = "Biometrics",
issn = "0006-341X",
publisher = "Wiley-Blackwell",
number = "1",

}

RIS

TY - JOUR

T1 - Instrumental variable estimation in semi-parametric additive hazards models

AU - Brueckner, Matthias

AU - Titman, Andrew Charles

AU - Jaki, Thomas Friedrich

N1 - This is the peer reviewed version of the following article: Brueckner, M. , Titman, A. and Jaki, T. (2019), Instrumental variable estimation in semi‐parametric additive hazards models. Biometrics 75 doi: 10.1111/biom.12952 which has been published in final form at https://onlinelibrary.wiley.com/doi/full/10.1111/biom.12952 This article may be used for non-commercial purposes in accordance With Wiley Terms and Conditions for self-archiving.

PY - 2019/3/1

Y1 - 2019/3/1

N2 - Instrumental variable methods allow unbiased estimation in the presence of unmeasured confounders when an appropriate instrumental variable is available. Two-stage least-squares and residual inclusion methods have recently been adapted to additive hazard models for censored survival data. The semi-parametric additive hazard model which can include time-independent and time-dependent covariate effects is particularly suited for the two-stage residual inclusion method, since it allows direct estimation of time-independent covariate effects without restricting the effect of the residual on the hazard.In this article we prove asymptotic normality of two-stage residual inclusion estimators of regression coefficients in a semi-parametric additive hazard model with time-independent and time-dependent covariate effects. We consider the cases of continuous and binary exposure. Estimation of the conditional survival function given observed covariates is discussed and a resampling scheme is proposed to obtain simultaneous confidence bands. The new methods are compared to existing ones in a simulation study and are applied to a real data set. The proposed methods perform favourably especially in cases with exposure-dependent censoring.

AB - Instrumental variable methods allow unbiased estimation in the presence of unmeasured confounders when an appropriate instrumental variable is available. Two-stage least-squares and residual inclusion methods have recently been adapted to additive hazard models for censored survival data. The semi-parametric additive hazard model which can include time-independent and time-dependent covariate effects is particularly suited for the two-stage residual inclusion method, since it allows direct estimation of time-independent covariate effects without restricting the effect of the residual on the hazard.In this article we prove asymptotic normality of two-stage residual inclusion estimators of regression coefficients in a semi-parametric additive hazard model with time-independent and time-dependent covariate effects. We consider the cases of continuous and binary exposure. Estimation of the conditional survival function given observed covariates is discussed and a resampling scheme is proposed to obtain simultaneous confidence bands. The new methods are compared to existing ones in a simulation study and are applied to a real data set. The proposed methods perform favourably especially in cases with exposure-dependent censoring.

U2 - 10.1111/biom.12952

DO - 10.1111/biom.12952

M3 - Journal article

VL - 75

SP - 110

EP - 120

JO - Biometrics

JF - Biometrics

SN - 0006-341X

IS - 1

ER -