Multiple imputation of missing data under missing at random: including a collider as an auxiliary variable in the imputation model can induce bias

Epidemiological studies often have missing data, which are commonly handled by multiple imputation (MI). In MI, in addition to those required for the substantive analysis, imputation models often include other variables (“auxiliary variables”). Auxiliary variables that predict the partially observed...

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Detalles Bibliográficos
Autores principales: Curnow, Elinor, Tilling, Kate, Heron, Jon E., Cornish, Rosie P., Carpenter, James R.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: 2023
Materias:
Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7615309/
https://www.ncbi.nlm.nih.gov/pubmed/37974561
http://dx.doi.org/10.3389/fepid.2023.1237447

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7615309/
https://www.ncbi.nlm.nih.gov/pubmed/37974561
http://dx.doi.org/10.3389/fepid.2023.1237447

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