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Multiple Imputation by Fully Conditional Specification for Dealing with Missing Data in a Large Epidemiologic Study
Missing data commonly occur in large epidemiologic studies. Ignoring incompleteness or handling the data inappropriately may bias study results, reduce power and efficiency, and alter important risk/benefit relationships. Standard ways of dealing with missing values, such as complete case analysis (...
Gardado en:
| Publicado en: | Int J Stat Med Res |
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| Main Authors: | , |
| Formato: | Artigo |
| Idioma: | Inglês |
| Publicado: |
2015
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| Assuntos: | |
| Acceso en liña: | https://ncbi.nlm.nih.gov/pmc/articles/PMC4945131/ https://ncbi.nlm.nih.gov/pubmed/27429686 https://ncbi.nlm.nih.govhttp://dx.doi.org/10.6000/1929-6029.2015.04.03.7 |
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