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Modélisation Dynamique de processus latents multivariés et leur relations temporelles: Application à la Maladie d'Alzheimer

dc.rights.licenseopenen_US
hal.structure.identifierBordeaux population health [BPH]
dc.contributor.authorTADDE, Bachirou
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.accessioned2021-04-23T09:01:54Z
dc.date.available2021-04-23T09:01:54Z
dc.identifier.urihttps://oskar-bordeaux.fr/handle/20.500.12278/27066
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 AD research. However it requires to simultaneously capture the dynamic and multidimensional aspects, and to explore temporal relationships between dimensions. We propose an original dynamic 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. 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 mechanistic models defined in continuous time. 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) are analyzed and their temporal structure is contrasted between different clinical stages of Alzheimer's disease.
dc.language.isoENen_US
dc.subject.enlongitudinal data
dc.subject.encausality
dc.subject.endifference equations
dc.subject.enmixed models
dc.subject.enlatent process
dc.titleModélisation Dynamique de processus latents multivariés et leur relations temporelles: Application à la Maladie d'Alzheimer
dc.title.enDynamic modeling of multivariate latent processes and their temporal relationships: Application to Alzheimer's disease
dc.typeDocument de travail - Pré-publicationen_US
dc.subject.halStatistiques [stat]/Applications [stat.AP]en_US
dc.subject.halSciences du Vivant [q-bio]/Santé publique et épidémiologieen_US
dc.identifier.arxiv1806.03659en_US
bordeaux.hal.laboratoriesBordeaux Population Health Research Center (BPH) - UMR 1219en_US
bordeaux.institutionUniversité de Bordeauxen_US
bordeaux.institutionINSERMen_US
bordeaux.teamSISTM_BPH
bordeaux.teamBIOSTAT_BPHen_US
bordeaux.teamSEPIAen_US
bordeaux.import.sourcehal
hal.identifierhal-01811796
hal.version1
hal.exportfalse
workflow.import.sourcehal
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