Common Methods for Handling Missing Data in Marginal Structural Models: What Works and Why

Marginal structural models (MSMs) are commonly used to estimate causal intervention effects in longitudinal nonrandomized studies. A common challenge when using MSMs to analyze observational studies is incomplete confounder data, where a poorly informed analysis method will lead to biased estimates...

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Detalles Bibliográficos
Autores principales: Leyrat, Clémence, Carpenter, James R, Bailly, Sébastien, Williamson, Elizabeth J
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Oxford University Press 2020
Materias:
Practice of Epidemiology
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8631064/
https://www.ncbi.nlm.nih.gov/pubmed/33057574
http://dx.doi.org/10.1093/aje/kwaa225

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8631064/
https://www.ncbi.nlm.nih.gov/pubmed/33057574
http://dx.doi.org/10.1093/aje/kwaa225

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