Competing regression models for longitudinal data

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dc.contributor.authorAlencar, Airlane P.
dc.contributor.authorSinger, Julio M.
dc.contributor.authorRocha, Francisco Marcelo M. [UNIFESP]
dc.contributor.institutionUniversidade de São Paulo (USP)
dc.contributor.institutionUniversidade Federal de São Paulo (UNIFESP)
dc.date.accessioned2016-01-24T14:26:52Z
dc.date.available2016-01-24T14:26:52Z
dc.date.issued2012-03-01
dc.description.abstractThe choice of an appropriate family of linear models for the analysis of longitudinal data is often a matter of concern for practitioners. To attenuate such difficulties, we discuss some issues that emerge when analyzing this type of data via a practical example involving pretestposttest longitudinal data. in particular, we consider log-normal linear mixed models (LNLMM), generalized linear mixed models (GLMM), and models based on generalized estimating equations (GEE). We show how some special features of the data, like a nonconstant coefficient of variation, may be handled in the three approaches and evaluate their performance with respect to the magnitude of standard errors of interpretable and comparable parameters. We also show how different diagnostic tools may be employed to identify outliers and comment on available software. We conclude by noting that the results are similar, but that GEE-based models may be preferable when the goal is to compare the marginal expected responses.en
dc.description.affiliationUniv São Paulo, Dept Estat, Inst Matemat & Estat, BR-05314970 São Paulo, Brazil
dc.description.affiliationUniversidade Federal de São Paulo, Dept Ciencia & Tecnol, Sao Jose Dos Campos, SP, Brazil
dc.description.affiliationUnifespUniversidade Federal de São Paulo, Dept Ciencia & Tecnol, Sao Jose Dos Campos, SP, Brazil
dc.description.sourceWeb of Science
dc.description.sponsorshipConselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)
dc.description.sponsorshipFundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)
dc.format.extent214-229
dc.identifierhttp://dx.doi.org/10.1002/bimj.201100056
dc.identifier.citationBiometrical Journal. Hoboken: Wiley-Blackwell, v. 54, n. 2, p. 214-229, 2012.
dc.identifier.doi10.1002/bimj.201100056
dc.identifier.issn0323-3847
dc.identifier.urihttp://repositorio.unifesp.br/handle/11600/34654
dc.identifier.wosWOS:000303045200004
dc.language.isoeng
dc.publisherWiley-Blackwell
dc.relation.ispartofBiometrical Journal
dc.rightsinfo:eu-repo/semantics/restrictedAccess
dc.rights.licensehttp://olabout.wiley.com/WileyCDA/Section/id-406071.html
dc.subjectEstimating equations methoden
dc.subjectGeneralized linear modelsen
dc.subjectLongitudinal dataen
dc.subjectMixed modelsen
dc.subjectPretesten
dc.subjectposttest measuresen
dc.titleCompeting regression models for longitudinal dataen
dc.typeinfo:eu-repo/semantics/article
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