Instrumental variable estimation in semi-parametric additive hazards models

Brueckner, Matthias and Titman, Andrew Charles and Jaki, Thomas Friedrich (2019) Instrumental variable estimation in semi-parametric additive hazards models. Biometrics, 75 (1). pp. 110-120. ISSN 0006-341X

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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.

Item Type: Journal Article
Journal or Publication Title: Biometrics
Additional Information: 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.
Uncontrolled Keywords: /dk/atira/pure/subjectarea/asjc/2700
Subjects:
Departments: Faculty of Science and Technology > Mathematics and Statistics
ID Code: 126318
Deposited By: ep_importer_pure
Deposited On: 31 Jul 2018 13:00
Refereed?: Yes
Published?: Published
Last Modified: 22 Feb 2020 04:37
URI: https://eprints.lancs.ac.uk/id/eprint/126318

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