Gutenberg Open Science: Multiple imputation of missing data : a simulation study on a binary response

Please use this identifier to cite or link to this item: http://doi.org/10.25358/openscience-7857

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DC Field	Value	Language
dc.contributor.author	Hardt, Jochen	-
dc.contributor.author	Herke, Max	-
dc.contributor.author	Brian, Tamara	-
dc.contributor.author	Laubach, Wilfried	-
dc.date.accessioned	2022-10-05T10:05:21Z	-
dc.date.available	2022-10-05T10:05:21Z	-
dc.date.issued	2013
dc.identifier.uri	https://openscience.ub.uni-mainz.de/handle/20.500.12030/7872	-
dc.description.abstract	Currently, a growing number of programs become available in statistical software for multiple imputation of missing values. Among others, two algorithms are mainly implemented: Expectation Maximization (EM) and Multiple Imputation by Chained Equations (MICE). They have been shown to work well in large samples or when only small proportions of missing data are to be imputed. However, some researchers have begun to impute large proportions of missing data or to apply the method to small samples. A simulation was performed using MICE on datasets with 50, 100 or 200 cases and four or eleven variables. A varying proportion of data (3% - 63%) was set as missing completely at random and subsequently substituted using multiple imputation by chained equations. In a logistic regression model, four coefficients, i.e. non-zero and zero main effects as well as non-zero and zero interaction effects were examined. Estimations of all main and interaction effects were unbiased. There was a considerable variance in the estimates, increasing with the proportion of missing data and decreasing with sample size. The imputation of missing data by chained equations is a useful tool for imputing small to moderate proportions of missing data. The method has its limits, however. In small samples, there are considerable random errors for all effects.	en_GB
dc.description.sponsorship	DFG, Open Access-Publizieren Universität Mainz / Universitätsmedizin	de
dc.language.iso	eng	de
dc.rights	CC BY	*
dc.rights.uri	https://creativecommons.org/licenses/by/4.0/	*
dc.subject.ddc	610 Medizin	de_DE
dc.subject.ddc	610 Medical sciences	en_GB
dc.title	Multiple imputation of missing data : a simulation study on a binary response	en_GB
dc.type	Zeitschriftenaufsatz	de
dc.identifier.doi	http://doi.org/10.25358/openscience-7857	-
jgu.type.dinitype	article	en_GB
jgu.type.version	Published version	de
jgu.type.resource	Text	de
jgu.organisation.department	FB 04 Medizin	de
jgu.organisation.number	2700	-
jgu.organisation.name	Johannes Gutenberg-Universität Mainz	-
jgu.rights.accessrights	openAccess	-
jgu.journal.title	Open journal of statistics	de
jgu.journal.volume	3	de
jgu.journal.issue	5	de
jgu.pages.start	370	de
jgu.pages.end	378	de
jgu.publisher.year	2013	-
jgu.publisher.name	Scientific Research Publ.	de
jgu.publisher.place	Irvine, Calif.	de
jgu.publisher.uri	http://dx.doi.org/10.4236/ojs.2013.35043	de
jgu.publisher.issn	2161-718X	de
jgu.organisation.place	Mainz	-
jgu.subject.ddccode	610	de
opus.date.modified	2018-07-31T09:32:55Z
opus.subject.dfgcode	00-000
opus.organisation.string	FB 04: Medizin: Klinik und Poliklinik für Psychosomatische Medizin und Psychotherapie	de_DE
opus.identifier.opusid	24284
opus.institute.number	0434
opus.metadataonly	false
opus.type.contenttype	Keine	de_DE
opus.type.contenttype	None	en_EN
opus.affiliated	Hardt, Jochen
jgu.publisher.doi	10.4236/ojs.2013.35043	de
jgu.organisation.ror	https://ror.org/023b0x485	-
Appears in collections:	DFG-OA-Publizieren (2012 - 2017)

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