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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Main Authors:	Liu, Yang, De, Anindya
Format:	Online
Language:	English
Published:	2015
Online Access:	https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4945131/

id	pubmed-4945131
recordtype	oai_dc
spelling	pubmed-49451312016-07-14 Multiple Imputation by Fully Conditional Specification for Dealing with Missing Data in a Large Epidemiologic Study Liu, Yang De, Anindya Article 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 (CCA), are generally inappropriate due to the loss of precision and risk of bias. Multiple imputation by fully conditional specification (FCS MI) is a powerful and statistically valid method for creating imputations in large data sets which include both categorical and continuous variables. It specifies the multivariate imputation model on a variable-by-variable basis and offers a principled yet flexible method of addressing missing data, which is particularly useful for large data sets with complex data structures. However, FCS MI is still rarely used in epidemiology, and few practical resources exist to guide researchers in the implementation of this technique. We demonstrate the application of FCS MI in support of a large epidemiologic study evaluating national blood utilization patterns in a sub-Saharan African country. A number of practical tips and guidelines for implementing FCS MI based on this experience are described. 2015-08-19 2015 /pmc/articles/PMC4945131/ /pubmed/27429686 http://dx.doi.org/10.6000/1929-6029.2015.04.03.7 Text en http://creativecommons.org/licenses/by-nc/3.0/ This is an open access article licensed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/3.0/) which permits unrestricted, non-commercial use, distribution and reproduction in any medium, provided the work is properly cited.
repository_type	Open Access Journal
institution_category	Foreign Institution
institution	US National Center for Biotechnology Information
building	NCBI PubMed
collection	Online Access
language	English
format	Online
author	Liu, Yang De, Anindya
spellingShingle	Liu, Yang De, Anindya Multiple Imputation by Fully Conditional Specification for Dealing with Missing Data in a Large Epidemiologic Study
author_facet	Liu, Yang De, Anindya
author_sort	Liu, Yang
title	Multiple Imputation by Fully Conditional Specification for Dealing with Missing Data in a Large Epidemiologic Study
title_short	Multiple Imputation by Fully Conditional Specification for Dealing with Missing Data in a Large Epidemiologic Study
title_full	Multiple Imputation by Fully Conditional Specification for Dealing with Missing Data in a Large Epidemiologic Study
title_fullStr	Multiple Imputation by Fully Conditional Specification for Dealing with Missing Data in a Large Epidemiologic Study
title_full_unstemmed	Multiple Imputation by Fully Conditional Specification for Dealing with Missing Data in a Large Epidemiologic Study
title_sort	multiple imputation by fully conditional specification for dealing with missing data in a large epidemiologic study
description	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 (CCA), are generally inappropriate due to the loss of precision and risk of bias. Multiple imputation by fully conditional specification (FCS MI) is a powerful and statistically valid method for creating imputations in large data sets which include both categorical and continuous variables. It specifies the multivariate imputation model on a variable-by-variable basis and offers a principled yet flexible method of addressing missing data, which is particularly useful for large data sets with complex data structures. However, FCS MI is still rarely used in epidemiology, and few practical resources exist to guide researchers in the implementation of this technique. We demonstrate the application of FCS MI in support of a large epidemiologic study evaluating national blood utilization patterns in a sub-Saharan African country. A number of practical tips and guidelines for implementing FCS MI based on this experience are described.
publishDate	2015
url	https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4945131/
_version_	1613609484364546048

Multiple Imputation by Fully Conditional Specification for Dealing with Missing Data in a Large Epidemiologic Study

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