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Modeling Risk-Factor Trajectories When Measurement Tools Change Sequentially During Follow-up in Cohort Studies: Application to Dietary Habits in Prodromal Dementia

dc.rights.licenseopenen_US
hal.structure.identifierBordeaux population health [BPH]
dc.contributor.authorWAGNER, Maud
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.authorSAMIERI, Cecilia
hal.structure.identifierBordeaux population health [BPH]
dc.contributor.authorPROUST LIMA, Cecile
ORCID: 0000-0002-9884-955X
IDREF: 114375747
dc.date.accessioned2021-01-06T13:10:44Z
dc.date.available2021-01-06T13:10:44Z
dc.date.issued2018-04-01
dc.identifier.issn1476-6256 (Electronic) 0002-9262 (Linking)en_US
dc.identifier.urihttps://oskar-bordeaux.fr/handle/20.500.12278/23712
dc.description.abstractEnModeling risk-factor trajectories is critical to understanding the natural history of diseases, yet the measurement tools used to assess risk factors often evolve during follow-up in cohorts, and such change prevents longitudinal analyses using standard models. We addressed this issue with a latent process model. Trajectories of average intakes of 5 food families (fish, meat, fruits, vegetables, and carbohydrate-rich foods) were described in prodromal dementia during the 10 years prior to diagnosis of cases and compared with those of controls, using a case-control sample nested within the Three-City Study, Bordeaux, France (1999-2012). Food intakes were measured by 2 or 3 different subquestionnaires across 5 repeated food frequency questionnaires. The sample comprised 205 incident cases and 410 controls matched for age, sex, education, and number of repeated food frequency questionnaires. Intakes of fish, fruits, and vegetables decreased at the approach of diagnosis among cases, suggesting reverse causation. This study demonstrated that the latent process model approach constitutes a powerful framework for modeling risk-factor trajectories, even when measurement tools change sequentially over time. Coupled with a case-control approach to contrast trajectories in prodromal disease versus healthy status, it can help us to understand the dynamic, causal relationships between risk factors and diseases.
dc.language.isoENen_US
dc.subject.enBiostatistics
dc.subject.enLEHA
dc.subject.enSEPIA
dc.title.enModeling Risk-Factor Trajectories When Measurement Tools Change Sequentially During Follow-up in Cohort Studies: Application to Dietary Habits in Prodromal Dementia
dc.title.alternativeAm J Epidemiolen_US
dc.typeArticle de revueen_US
dc.identifier.doi10.1093/aje/kwx293en_US
dc.subject.halSciences du Vivant [q-bio]/Santé publique et épidémiologieen_US
dc.identifier.pubmed29020158en_US
bordeaux.journalAmerican Journal of Epidemiologyen_US
bordeaux.page845-854en_US
bordeaux.volume187en_US
bordeaux.hal.laboratoriesBordeaux Population Health Research Center (BPH) - U1219en_US
bordeaux.issue4en_US
bordeaux.institutionUniversité de Bordeauxen_US
bordeaux.teamBIOSTAT_BPHen_US
bordeaux.teamLEHA_BPH
bordeaux.teamSEPIAen_US
bordeaux.peerReviewedouien_US
bordeaux.inpressnonen_US
hal.identifierhal-03194247
hal.version1
hal.date.transferred2021-04-09T12:05:46Z
hal.exporttrue
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