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Latent Variable Models for Longitudinal Data with Multiple Continuous Outcomes
dc.contributor.authorRoy, Jasonen_US
dc.contributor.authorLin, Xihongen_US
dc.date.accessioned2010-04-01T14:55:01Z
dc.date.available2010-04-01T14:55:01Z
dc.date.issued2000-12en_US
dc.identifier.citationRoy, Jason; Lin, Xihong (2000). "Latent Variable Models for Longitudinal Data with Multiple Continuous Outcomes." Biometrics 56(4): 1047-1054. <http://hdl.handle.net/2027.42/65373>en_US
dc.identifier.issn0006-341Xen_US
dc.identifier.issn1541-0420en_US
dc.identifier.urihttps://hdl.handle.net/2027.42/65373
dc.identifier.urihttp://www.ncbi.nlm.nih.gov/sites/entrez?cmd=retrieve&db=pubmed&list_uids=11129460&dopt=citationen_US
dc.description.abstractMultiple outcomes are often used to properly characterize an effect of interest. This paper proposes a latent variable model for the situation where repeated measures over time are obtained on each outcome. These outcomes are assumed to measure an underlying quantity of main interest from different perspectives. We relate the observed outcomes using regression models to a latent variable, which is then modeled as a function of covariates by a separate regression model. Random effects are used to model the correlation due to repeated measures of the observed outcomes and the latent variable. An EM algorithm is developed to obtain maximum likelihood estimates of model parameters. Unit-specific predictions of the latent variables are also calculated. This method is illustrated using data from a national panel study on changes in methadone treatment practices.en_US
dc.format.extent836124 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.otherEM Algorithmen_US
dc.subject.otherFactor Analysisen_US
dc.subject.otherMissing Dataen_US
dc.subject.otherMultivariate Responseen_US
dc.subject.otherRandom Effectsen_US
dc.subject.otherRepeated Measuresen_US
dc.titleLatent Variable Models for Longitudinal Data with Multiple Continuous Outcomesen_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.identifier.pmid11129460en_US
dc.description.bitstreamurlhttp://deepblue.lib.umich.edu/bitstream/2027.42/65373/1/j.0006-341X.2000.01047.x.pdf
dc.identifier.doi10.1111/j.0006-341X.2000.01047.xen_US
dc.identifier.sourceBiometricsen_US
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dc.identifier.citedreferenceRoy, J. ( 2000 ). Latent variable models for longitudinal data with multiple outcomes, informative dropouts, and missing covariates. Ph.D. dissertation, University of Michigan, Ann Arbor.en_US
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dc.owningcollnameInterdisciplinary and Peer-Reviewed


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