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Multiple Imputation and Posterior Simulation for Multivariate Missing Data in Longitudinal Studies

dc.contributor.authorLiu, Minzhien_US
dc.contributor.authorTaylor, Jeremy M. G.en_US
dc.contributor.authorBelin, Thomas R.en_US
dc.date.accessioned2010-04-01T14:52:31Z
dc.date.available2010-04-01T14:52:31Z
dc.date.issued2000-12en_US
dc.identifier.citationLiu, Minzhi; Taylor, Jeremy M. G.; Belin, Thomas R. (2000). "Multiple Imputation and Posterior Simulation for Multivariate Missing Data in Longitudinal Studies." Biometrics 56(4): 1157-1163. <http://hdl.handle.net/2027.42/65329>en_US
dc.identifier.issn0006-341Xen_US
dc.identifier.issn1541-0420en_US
dc.identifier.urihttps://hdl.handle.net/2027.42/65329
dc.identifier.urihttp://www.ncbi.nlm.nih.gov/sites/entrez?cmd=retrieve&db=pubmed&list_uids=11213759&dopt=citationen_US
dc.description.abstractThis paper outlines a multiple imputation method for handling missing data in designed longitudinal studies. A random coefficients model is developed to accommodate incomplete multivariate continuous longitudinal data. Multivariate repeated measures are jointly modeled; specifically, an i.i.d. normal model is assumed for time-independent variables and a hierarchical random coefficients model is assumed for time-dependent variables in a regression model conditional on the time-independent variables and time, with heterogeneous error variances across variables and time points. Gibbs sampling is used to draw model parameters and for imputations of missing observations. An application to data from a study of startle reactions illustrates the model. A simulation study compares the multiple imputation procedure to the weighting approach of Robins, Rotnitzky, and Zhao (1995, Journal of the American Statistical Association 90 , 106–121) that can be used to address similar data structures.en_US
dc.format.extent749973 bytes
dc.format.extent3110 bytes
dc.format.mimetypeapplication/pdf
dc.format.mimetypetext/plain
dc.publisherBlackwell Publishing Ltden_US
dc.rightsThe International Biometric Society, 2000en_US
dc.subject.otherGibbs Samplingen_US
dc.subject.otherMissing Dataen_US
dc.subject.otherMultiple Imputationen_US
dc.subject.otherMultivariate Longitudinal Dataen_US
dc.titleMultiple Imputation and Posterior Simulation for Multivariate Missing Data in Longitudinal Studiesen_US
dc.typeArticleen_US
dc.rights.robotsIndexNoFollowen_US
dc.subject.hlbsecondlevelMathematicsen_US
dc.subject.hlbtoplevelScienceen_US
dc.description.peerreviewedPeer Revieweden_US
dc.contributor.affiliationumDepartment of Biostatistics, University of Michigan, Ann Arbor, Michigan 48109, U.S.A.en_US
dc.contributor.affiliationotherClinical Biostatistics, Merck and Co., Inc., Rahway, New Jersey 07065, U.S.A.en_US
dc.contributor.affiliationotherDepartment of Biostatistics, UCLA School of Public Health, Los Angeles, California 90095, U.S.A.en_US
dc.identifier.pmid11213759en_US
dc.description.bitstreamurlhttp://deepblue.lib.umich.edu/bitstream/2027.42/65329/1/j.0006-341X.2000.01157.x.pdf
dc.identifier.doi10.1111/j.0006-341X.2000.01157.xen_US
dc.identifier.sourceBiometricsen_US
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dc.owningcollnameInterdisciplinary and Peer-Reviewed


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