Canonical Causal Diagrams to Guide the Treatment of Missing Data in Epidemiologic Studies

With incomplete data, the “missing at random” (MAR) assumption is widely understood to enable unbiased estimation with appropriate methods. While the need to assess the plausibility of MAR and to perform sensitivity analyses considering “missing not at random” (MNAR) scenarios has been emphasized, t...

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Detalles Bibliográficos
Autores principales: Moreno-Betancur, Margarita, Lee, Katherine J, Leacy, Finbarr P, White, Ian R, Simpson, Julie A, Carlin, John B
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Oxford University Press 2018
Materias:
Practice of Epidemiology
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6269242/
https://www.ncbi.nlm.nih.gov/pubmed/30124749
http://dx.doi.org/10.1093/aje/kwy173

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6269242/
https://www.ncbi.nlm.nih.gov/pubmed/30124749
http://dx.doi.org/10.1093/aje/kwy173

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