Multiple imputation of missing covariates with non-linear effects and interactions: an evaluation of statistical methods

BACKGROUND: Multiple imputation is often used for missing data. When a model contains as covariates more than one function of a variable, it is not obvious how best to impute missing values in these covariates. Consider a regression with outcome Y and covariates X and X(2). In 'passive imputati...

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Detalles Bibliográficos
Autores principales: Seaman, Shaun R, Bartlett, Jonathan W, White, Ian R
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2012
Materias:
Research Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3403931/
https://www.ncbi.nlm.nih.gov/pubmed/22489953
http://dx.doi.org/10.1186/1471-2288-12-46

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3403931/
https://www.ncbi.nlm.nih.gov/pubmed/22489953
http://dx.doi.org/10.1186/1471-2288-12-46

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