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A joint model for multiple dynamic processes and clinical endpoints: Application to Alzheimer's disease

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.date.accessioned2020-07-10T06:27:11Z
dc.date.available2020-07-10T06:27:11Z
dc.date.issued2019-10-15
dc.identifier.issn1097-0258 (Electronic) 0277-6715 (Linking)en_US
dc.identifier.urihttps://oskar-bordeaux.fr/handle/20.500.12278/10388
dc.description.abstractEnAs other neurodegenerative diseases, Alzheimer's disease, the most frequent dementia in the elderly, is characterized by multiple progressive impairments in the brain structure and in clinical functions such as cognitive functioning and functional disability. Until recently, these components were mostly studied independently because no joint model for multivariate longitudinal data and time to event was available in the statistical community. Yet, these components are fundamentally interrelated in the degradation process toward dementia and should be analyzed together. We thus propose a joint model to simultaneously describe the dynamics of multiple correlated components. Each component, defined as a latent process, is measured by one or several continuous markers (not necessarily Gaussian). Rather than considering the associated time to diagnosis as in standard joint models, we assume diagnosis corresponds to the passing above a covariate-specific threshold (to be estimated) of a pathological process that is modeled as a combination of the component-specific latent processes. This definition captures the clinical complexity of diagnoses such as dementia diagnosis but also benefits from simplifications for the computation of maximum likelihood estimates. We show that the model and estimation procedure can also handle competing clinical endpoints. The estimation procedure, implemented in an R package, is validated by simulations and the method is illustrated on a large French population-based cohort of cerebral aging in which we focused on the dynamics of three clinical manifestations and the associated risk of dementia and death before dementia.
dc.language.isoENen_US
dc.subject.enBiostatistics
dc.subject.enSEPIA
dc.title.enA joint model for multiple dynamic processes and clinical endpoints: Application to Alzheimer's disease
dc.title.alternativeStat Meden_US
dc.typeArticle de revueen_US
dc.identifier.doi10.1002/sim.8328en_US
dc.subject.halSciences du Vivant [q-bio]/Santé publique et épidémiologieen_US
dc.identifier.pubmed31386222en_US
bordeaux.journalStatistics in Medicineen_US
bordeaux.page4702-4717en_US
bordeaux.volume38en_US
bordeaux.hal.laboratoriesBordeaux Population Health Research Center (BPH) - U1219en_US
bordeaux.issue23en_US
bordeaux.institutionUniversité de Bordeauxen_US
bordeaux.teamBIOSTAT_BPH
bordeaux.peerReviewedouien_US
bordeaux.inpressnonen_US
hal.identifierhal-03162631
hal.version1
hal.date.transferred2021-03-08T14:46:10Z
hal.exporttrue
bordeaux.COinSctx_ver=Z39.88-2004&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft.jtitle=Statistics%20in%20Medicine&rft.date=2019-10-15&rft.volume=38&rft.issue=23&rft.spage=4702-4717&rft.epage=4702-4717&rft.eissn=1097-0258%20(Electronic)%200277-6715%20(Linking)&rft.issn=1097-0258%20(Electronic)%200277-6715%20(Linking)&rft.au=PROUST-LIMA,%20Cecile&PHILIPPS,%20Viviane&DARTIGUES,%20Jean-Francois&rft.genre=article


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