Simulating longitudinal data from marginal structural models using the additive hazard model [Image: see text]

Observational longitudinal data on treatments and covariates are increasingly used to investigate treatment effects, but are often subject to time-dependent confounding. Marginal structural models (MSMs), estimated using inverse probability of treatment weighting or the g-formula, are popular for ha...

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Detalles Bibliográficos
Autores principales: Keogh, Ruth H., Seaman, Shaun R., Gran, Jon Michael, Vansteelandt, Stijn
Formato: Online Artículo Texto
Lenguaje:English
Publicado: 2021
Materias:
Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7612178/
https://www.ncbi.nlm.nih.gov/pubmed/33983641
http://dx.doi.org/10.1002/bimj.202000040

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7612178/
https://www.ncbi.nlm.nih.gov/pubmed/33983641
http://dx.doi.org/10.1002/bimj.202000040

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