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 (...

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Detalles Bibliográficos
Autores principales: Liu, Yang, De, Anindya
Formato: Online Artículo Texto
Lenguaje:English
Publicado: 2015
Materias:
Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4945131/
https://www.ncbi.nlm.nih.gov/pubmed/27429686
http://dx.doi.org/10.6000/1929-6029.2015.04.03.7

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4945131/
https://www.ncbi.nlm.nih.gov/pubmed/27429686
http://dx.doi.org/10.6000/1929-6029.2015.04.03.7

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