On doubly robust estimation of the hazard difference.
Additive hazards model
Causal inference
Doubly robust estimation
Lifetime and survival analysis
Semiparametric inference
Journal
Biometrics
ISSN: 1541-0420
Titre abrégé: Biometrics
Pays: United States
ID NLM: 0370625
Informations de publication
Date de publication:
03 2019
03 2019
Historique:
received:
01
06
2017
revised:
01
05
2018
accepted:
01
05
2018
pubmed:
23
8
2018
medline:
18
12
2019
entrez:
23
8
2018
Statut:
ppublish
Résumé
The estimation of conditional treatment effects in an observational study with a survival outcome typically involves fitting a hazards regression model adjusted for a high-dimensional covariate. Standard estimation of the treatment effect is then not entirely satisfactory, as the misspecification of the effect of this covariate may induce a large bias. Such misspecification is a particular concern when inferring the hazard difference, because it is difficult to postulate additive hazards models that guarantee non-negative hazards over the entire observed covariate range. We therefore consider a novel class of semiparametric additive hazards models which leave the effects of covariates unspecified. The efficient score under this model is derived. We then propose two different estimation approaches for the hazard difference (and hence also the relative chance of survival), both of which yield estimators that are doubly robust. The approaches are illustrated using simulation studies and data on right heart catheterization and mortality from the SUPPORT study.
Identifiants
pubmed: 30133696
doi: 10.1111/biom.12943
pmc: PMC7735191
mid: NIHMS1001906
doi:
Types de publication
Journal Article
Research Support, N.I.H., Extramural
Research Support, Non-U.S. Gov't
Langues
eng
Sous-ensembles de citation
IM
Pagination
100-109Subventions
Organisme : NIAID NIH HHS
ID : R01 AI104459
Pays : United States
Organisme : NIAID NIH HHS
ID : R01 AI127271
Pays : United States
Organisme : NCI NIH HHS
ID : R01 CA222147
Pays : United States
Informations de copyright
© 2018, The International Biometric Society.
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