Understanding Marginal Structural Models for Time-Varying Exposures: Pitfalls and Tips

Epidemiologists are increasingly encountering complex longitudinal data, in which exposures and their confounders vary during follow-up. When a prior exposure affects the confounders of the subsequent exposures, estimating the effects of the time-varying exposures requires special statistical techni...

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Detalles Bibliográficos
Autores principales: Shinozaki, Tomohiro, Suzuki, Etsuji
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Japan Epidemiological Association 2020
Materias:
Special Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7429147/
https://www.ncbi.nlm.nih.gov/pubmed/32684529
http://dx.doi.org/10.2188/jea.JE20200226

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7429147/
https://www.ncbi.nlm.nih.gov/pubmed/32684529
http://dx.doi.org/10.2188/jea.JE20200226

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