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.
Accepted author manuscript, 284 KB, PDF document
Available under license: CC BY-NC: Creative Commons Attribution-NonCommercial 4.0 International License
Accepted author manuscript, 273 KB, PDF document
Final published version
Licence: CC BY: Creative Commons Attribution 4.0 International License
Research output: Contribution to Journal/Magazine › Journal article › peer-review
Research output: Contribution to Journal/Magazine › Journal article › peer-review
}
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 -