{"database":"biostudies-literature","file_versions":[],"scores":null,"additional":{"submitter":["Brueckner M"],"funding":["Medical Research Council","National Institute for Health Research (NIHR)"],"pagination":["110-120"],"full_dataset_link":["https://www.ebi.ac.uk/biostudies/studies/S-EPMC7379316"],"repository":["biostudies-literature"],"omics_type":["Unknown"],"volume":["75(1)"],"pubmed_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-dep"],"journal":["Biometrics"],"pubmed_title":["Instrumental variable estimation in semi-parametric additive hazards models."],"pmcid":["PMC7379316"],"funding_grant_id":["MR/M005755/1","SRF-2015-08-001"],"pubmed_authors":["Brueckner M","Jaki T","Titman A"],"additional_accession":[]},"is_claimable":false,"name":"Instrumental variable estimation in semi-parametric additive hazards models.","description":"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-dep","dates":{"release":"2019-01-01T00:00:00Z","publication":"2019 Mar","modification":"2025-04-19T01:03:30.107Z","creation":"2025-04-07T12:02:46.574Z"},"accession":"S-EPMC7379316","cross_references":{"pubmed":["30073669"],"doi":["10.1111/biom.12952"]}}