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dc.rights.licenseopenen_US
hal.structure.identifierBordeaux population health [BPH]
dc.contributor.authorPROUST-LIMA, Cecile
hal.structure.identifierBordeaux population health [BPH]
dc.contributor.authorPHILIPPS, Viviane
hal.structure.identifierBordeaux population health [BPH]
dc.contributor.authorDARTIGUES, Jean-Francois
ORCID: 0000-0001-9482-5529
IDREF: 058586105
dc.contributor.authorBENNETT, D. A.
dc.contributor.authorGLYMOUR, M. M.
hal.structure.identifierBordeaux population health [BPH]
dc.contributor.authorJACQMIN-GADDA, Helene
hal.structure.identifierBordeaux population health [BPH]
dc.contributor.authorSAMIERI, Cecilia
dc.date.accessioned2020-07-10T07:21:33Z
dc.date.available2020-07-10T07:21:33Z
dc.date.issued2019-07
dc.identifier.issn1477-0334 (Electronic) 0962-2802 (Linking)en_US
dc.identifier.urihttps://oskar-bordeaux.fr/handle/20.500.12278/10389
dc.description.abstractEnAs with many health constructs, cognition is difficult to measure accurately; it is assessed by multiple psychometric tests. Two approaches are commonly adopted to address this multivariate aspect in longitudinal analyses: the composite score approach summarizes the tests into a single outcome and subsequently analyzes its change; the multivariate approach relates the tests to the underlying cognitive level and simultaneously analyzes its change. We compared the quality of inference of these approaches in a simulation study based on three combinations of tests inspired by two population-based cohorts. In the absence of missing data and with relatively Gaussian psychometric tests, the composite score approach provided similar type-I error rates and statistical power as the multivariate latent process approach. In the more plausible scenario with departures from normality, transformations of each constituent test or of the composite score were required to avoid excess type-I error rates. When missing tests were more likely in cognitively impaired subjects, inference with the composite was not correct. In conclusion, composite scores can be used to assess risk factors for cognitive change provided they are correctly normalized, constituent tests are reliable and the amount of uninformative missing tests remains small. Otherwise, latent variable models are recommended.
dc.language.isoENen_US
dc.subject.enBiostatistics
dc.subject.enLEHA
dc.subject.enSEPIA
dc.subject.enFR
dc.title.enAre latent variable models preferable to composite score approaches when assessing risk factors of change? Evaluation of type-I error and statistical power in longitudinal cognitive studies
dc.title.alternativeStat Methods Med Resen_US
dc.typeArticle de revueen_US
dc.identifier.doi10.1177/0962280217739658en_US
dc.subject.halSciences du Vivant [q-bio]/Santé publique et épidémiologieen_US
dc.identifier.pubmed29165049en_US
bordeaux.journalStatistical Methods in Medical Researchen_US
bordeaux.page1942-1957en_US
bordeaux.volume28en_US
bordeaux.hal.laboratoriesBordeaux Population Health Research Center (BPH) - U1219en_US
bordeaux.issue7en_US
bordeaux.institutionUniversité de Bordeauxen_US
bordeaux.teamLEHA_BPH
bordeaux.teamBIOSTAT_BPH
bordeaux.peerReviewedouien_US
bordeaux.inpressnonen_US
hal.identifierhal-03162625
hal.version1
hal.date.transferred2021-03-08T14:44:08Z
hal.exporttrue
bordeaux.COinSctx_ver=Z39.88-2004&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft.jtitle=Statistical%20Methods%20in%20Medical%20Research&rft.date=2019-07&rft.volume=28&rft.issue=7&rft.spage=1942-1957&rft.epage=1942-1957&rft.eissn=1477-0334%20(Electronic)%200962-2802%20(Linking)&rft.issn=1477-0334%20(Electronic)%200962-2802%20(Linking)&rft.au=PROUST-LIMA,%20Cecile&PHILIPPS,%20Viviane&DARTIGUES,%20Jean-Francois&BENNETT,%20D.%20A.&GLYMOUR,%20M.%20M.&rft.genre=article


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