Accounting for missing data in statistical analyses: multiple imputation is not always the answer

BACKGROUND: Missing data are unavoidable in epidemiological research, potentially leading to bias and loss of precision. Multiple imputation (MI) is widely advocated as an improvement over complete case analysis (CCA). However, contrary to widespread belief, CCA is preferable to MI in some situation...

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Detalles Bibliográficos
Autores principales: Hughes, Rachael A, Heron, Jon, Sterne, Jonathan A C, Tilling, Kate
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Oxford University Press 2019
Materias:
Methods
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6693809/
https://www.ncbi.nlm.nih.gov/pubmed/30879056
http://dx.doi.org/10.1093/ije/dyz032

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6693809/
https://www.ncbi.nlm.nih.gov/pubmed/30879056
http://dx.doi.org/10.1093/ije/dyz032

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