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AuthorMayer, Benjamindc.contributor.author
Date of accession2016-03-15T06:25:02Zdc.date.accessioned
Available in OPARU since2016-03-15T06:25:02Zdc.date.available
Year of creation2010dc.date.created
AbstractMissing values are ubiquitous in clinical research. Especially in case of a longitudinal study, the complete acquisition of all study relevant data is complex and can therefore hardly be realized. In the course of an analysis, the parameters that should be estimated then may be biased. This leads to a restriction and falsification of study results. There are different approaches to figure the problems of missing data. In the present manuscript, imputation methods for the handling of missing data in longitudinal studies are investigated. It is discussed which preconditions has to be fulfilled for their appropriate usage and on which statistical properties they are based on. Their performance is mutually compared by the conduction of a simulation study. A completely observed longitudinal data set is used to simulate different missing data szenarios artificially. The results suggest the application of the so called Markov Chain Monte Carlo approach as imputation method of choice to treat missing data. Only this method showed a good imputation quality in a sense of acceptable validity and precision even under large amounts of missing data. However, the topic of missing data in clinical trials should still be a part of research to improve the existing imputation strategies and methods.dc.description.abstract
Languagededc.language.iso
PublisherUniversität Ulmdc.publisher
LicenseStandard (Fassung vom 01.10.2008)dc.rights
Link to license texthttps://oparu.uni-ulm.de/xmlui/license_v2dc.rights.uri
KeywordMissing Valuesdc.subject
Dewey Decimal GroupDDC 610 / Medicine & healthdc.subject.ddc
MeSHBiomedical researchdc.subject.mesh
TitleFehlende Werte in klinischen Verlaufsstudien - der Umgang mit Studienabbrecherndc.title
Resource typeDissertationdc.type
DOIhttp://dx.doi.org/10.18725/OPARU-2197dc.identifier.doi
PPN663081033dc.identifier.ppn
URNhttp://nbn-resolving.de/urn:nbn:de:bsz:289-vts-76335dc.identifier.urn
GNDFehlende Datendc.subject.gnd
GNDLängsschnittuntersuchungdc.subject.gnd
FacultyMedizinische Fakultätuulm.affiliationGeneral
Date of activation2011-06-08T08:54:12Zuulm.freischaltungVTS
Peer reviewneinuulm.peerReview
Shelfmark print versionZ: J-H 14.088; W: W-H 12.552uulm.shelfmark
DCMI TypeTextuulm.typeDCMI
VTS ID7633uulm.vtsID
CategoryPublikationenuulm.category
Bibliographyuulmuulm.bibliographie


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