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dc.rights.licenseopenen_US
hal.structure.identifierStatistics In System biology and Translational Medicine [SISTM]
hal.structure.identifierBordeaux population health [BPH]
dc.contributor.authorSEGALAS, Corentin
hal.structure.identifierBordeaux population health [BPH]
dc.contributor.authorHELMER, Catherine
hal.structure.identifierStatistics In System biology and Translational Medicine [SISTM]
hal.structure.identifierBordeaux population health [BPH]
dc.contributor.authorGENUER, Robin
hal.structure.identifierBordeaux population health [BPH]
dc.contributor.authorPROUST LIMA, Cecile
ORCID: 0000-0002-9884-955X
IDREF: 114375747
dc.date.accessioned2024-09-23T09:51:34Z
dc.date.available2024-09-23T09:51:34Z
dc.date.issued2024-09-09
dc.identifier.issn1097-0258en_US
dc.identifier.urihttps://oskar-bordeaux.fr/handle/20.500.12278/201732
dc.description.abstractEnAnalyzing longitudinal data in health studies is challenging due to sparse and error-prone measurements, strong within-individual correlation, missing data and various trajectory shapes. While mixed-effect models (MM) effectively address these challenges, they remain parametric models and may incur computational costs. In contrast, functional principal component analysis (FPCA) is a non-parametric approach developed for regular and dense functional data that flexibly describes temporal trajectories at a potentially lower computational cost. This article presents an empirical simulation study evaluating the behavior of FPCA with sparse and error-prone repeated measures and its robustness under different missing data schemes in comparison with MM. The results show that FPCA is well-suited in the presence of missing at random data caused by dropout, except in scenarios involving most frequent and systematic dropout. Like MM, FPCA fails under missing not at random mechanism. The FPCA was applied to describe the trajectories of four cognitive functions before clinical dementia and contrast them with those of matched controls in a case-control study nested in a population-based aging cohort. The average cognitive declines of future dementia cases showed a sudden divergence from those of their matched controls with a sharp acceleration 5 to 2.5 years prior to diagnosis.
dc.language.isoENen_US
dc.rightsAttribution 3.0 United States*
dc.rights.urihttp://creativecommons.org/licenses/by/3.0/us/*
dc.subject.enFunctional Principal Component Analysis
dc.subject.enMissing Data
dc.subject.enMixed Models
dc.subject.enSparse Functional Data
dc.title.enFunctional Principal Component Analysis as an Alternative to Mixed-Effect Models for Describing Sparse Repeated Measures in Presence of Missing Data.
dc.title.alternativeStat Meden_US
dc.typeArticle de revueen_US
dc.identifier.doi10.1002/sim.10214en_US
dc.subject.halSciences du Vivant [q-bio]/Santé publique et épidémiologieen_US
dc.identifier.pubmed39248704en_US
bordeaux.journalStatistics in Medicineen_US
bordeaux.hal.laboratoriesBordeaux Population Health Research Center (BPH) - UMR 1219en_US
bordeaux.institutionUniversité de Bordeauxen_US
bordeaux.institutionINSERMen_US
bordeaux.institutionINRIAen_US
bordeaux.teamSISTM_BPHen_US
bordeaux.teamBIOSTAT_BPHen_US
bordeaux.peerReviewedouien_US
bordeaux.inpressnonen_US
bordeaux.import.sourcepubmed
hal.popularnonen_US
hal.audienceInternationaleen_US
hal.exportfalse
workflow.import.sourcepubmed
dc.rights.ccPas de Licence CCen_US
bordeaux.COinSctx_ver=Z39.88-2004&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft.jtitle=Statistics%20in%20Medicine&rft.date=2024-09-09&rft.eissn=1097-0258&rft.issn=1097-0258&rft.au=SEGALAS,%20Corentin&HELMER,%20Catherine&GENUER,%20Robin&PROUST%20LIMA,%20Cecile&rft.genre=article


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