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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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| Main Authors: | , , |
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| Formato: | Artigo |
| Idioma: | Inglês |
| Publicado em: |
BioMed Central
2012
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| Assuntos: | |
| Acesso em linha: | https://ncbi.nlm.nih.gov/pmc/articles/PMC3403931/ https://ncbi.nlm.nih.gov/pubmed/22489953 https://ncbi.nlm.nih.govhttp://dx.doi.org/10.1186/1471-2288-12-46 |
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