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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

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