biostudies-literatureBrueckner MMedical Research CouncilNational Institute for Health Research (NIHR)110-120https://www.ebi.ac.uk/biostudies/studies/S-EPMC7379316biostudies-literatureUnknown75(1)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-depBiometricsInstrumental variable estimation in semi-parametric additive hazards models.PMC7379316MR/M005755/1SRF-2015-08-001Brueckner MJaki TTitman AfalseInstrumental variable estimation in semi-parametric additive hazards models.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-dep2019-01-01T00:00:00Z2019 Mar2025-04-19T01:03:30.107Z2025-04-07T12:02:46.574ZS-EPMC73793163007366910.1111/biom.12952