ArticleBiometrics2019
On doubly robust estimation of the hazard difference.
Article in Biometrics, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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Who cites it
8 citing papers in PubMed.
- Post-hospitalization remote monitoring for patients with heart failure or chronic obstructive pulmonary disease in an accountable care organization.BMC health services research · 2024Article
- Inference for treatment-specific survival curves using machine learning.Journal of the American Statistical Association · 2024Article
- Efficient and flexible estimation of natural direct and indirect effects under intermediate confounding and monotonicity constraints.Biometrics · 2023Article
- Article
- The effects of midazolam or propofol plus fentanyl on ICU mortality: a retrospective study based on the MIMIC-IV database.Annals of translational medicine · 2022Article
- Comparison of Dimethyl Fumarate vs Fingolimod and Rituximab vs Natalizumab for Treatment of Multiple Sclerosis.JAMA network open · 2021Article
- Using generalized linear models to implement g-estimation for survival data with time-varying confounding.Statistics in medicine · 2021Article
- Adjusting for time-varying confounders in survival analysis using structural nested cumulative survival time models.Biometrics · 2020Article
Corrections and comments
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Authors and funding
4 authors.
Funding
Abstract
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.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.