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Dynamic modeling of multivariate dimensions and their temporal relationships using latent processes: Application to Alzheimer's disease

dc.rights.licenseopenen_US
hal.structure.identifierBordeaux population health [BPH]
dc.contributor.authorTADDE, Bachirou O.
hal.structure.identifierBordeaux population health [BPH]
dc.contributor.authorJACQMIN-GADDA, Helene
hal.structure.identifierBordeaux population health [BPH]
dc.contributor.authorDARTIGUES, Jean-Francois
ORCID: 0000-0001-9482-5529
IDREF: 058586105
hal.structure.identifierBordeaux population health [BPH]
dc.contributor.authorCOMMENGES, Daniel
hal.structure.identifierBordeaux population health [BPH]
dc.contributor.authorPROUST LIMA, Cecile
ORCID: 0000-0002-9884-955X
IDREF: 114375747
dc.date.accessioned2021-02-17T15:55:38Z
dc.date.available2021-02-17T15:55:38Z
dc.date.issued2020
dc.identifier.issn1541-0420 (Electronic) 0006-341X (Linking)en_US
dc.identifier.urihttps://oskar-bordeaux.fr/handle/20.500.12278/26284
dc.description.abstractEnAlzheimer's disease gradually affects several components including the cerebral dimension with brain atrophies, the cognitive dimension with a decline in various functions, and the functional dimension with impairment in the daily living activities. Understanding how such dimensions interconnect is crucial for Alzheimer's disease research. However, it requires to simultaneously capture the dynamic and multidimensional aspects and to explore temporal relationships between dimensions. We propose an original dynamic structural model that accounts for all these features. The model defines dimensions as latent processes and combines a multivariate linear mixed model and a system of difference equations to model trajectories and temporal relationships between latent processes in finely discrete time. Dimensions are simultaneously related to their observed (possibly multivariate) markers through nonlinear equations of observation. Parameters are estimated in the maximum likelihood framework enjoying a closed form for the likelihood. We demonstrate in a simulation study that this dynamic model in discrete time benefits the same causal interpretation of temporal relationships as models defined in continuous time as long as the discretization step remains small. The model is then applied to the data of the Alzheimer's Disease Neuroimaging Initiative. Three longitudinal dimensions (cerebral anatomy, cognitive ability, and functional autonomy) measured by six markers are analyzed, and their temporal structure is contrasted between different clinical stages of Alzheimer's disease.
dc.language.isoENen_US
dc.subjectSEPIA
dc.subjectBiostatistics
dc.title.enDynamic modeling of multivariate dimensions and their temporal relationships using latent processes: Application to Alzheimer's disease
dc.title.alternativeBiometricsen_US
dc.typeArticle de revueen_US
dc.identifier.doi10.1111/biom.13168en_US
dc.subject.halSciences du Vivant [q-bio]/Santé publique et épidémiologieen_US
dc.identifier.pubmed31647111en_US
bordeaux.journalBiometricsen_US
bordeaux.page886-899en_US
bordeaux.volume76en_US
bordeaux.hal.laboratoriesBordeaux Population Health Research Center (BPH) - U1219en_US
bordeaux.issue3en_US
bordeaux.institutionUniversité de Bordeauxen_US
bordeaux.teamSEPIAen_US
bordeaux.teamBIOSTAT_BPHen_US
bordeaux.peerReviewedouien_US
bordeaux.inpressnonen_US
hal.identifierhal-03144562
hal.version1
hal.date.transferred2021-02-17T15:55:41Z
hal.exporttrue
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