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Modèles conjoints pour données longitudinales et données de survie incomplètes appliqués à l'étude du vieillissement cognitif

Abstract : In cognitive ageing study, older people are highly selected bya risk of death associated with poor cognitive performances. Modeling the natural history of cognitive decline is difficult in presence of incomplete longitudinal and survival data. Moreover, the non observed cognitive decline acceleration begining before the dementia diagnosis is difficult to evaluate. Cognitive decline is higly heterogeneous, e.g. there are various patterns associated with different risks of survival event. The objective is to study joint models for incomplete longitudinal and survival data to describe the cognitive evolution in older people. Latent variable approaches were used to take into account the non-observed mecanismes, e.g. heterogeneity and decline acceleration. First, we compared two approaches to consider missing data in longitudinal data analysis. Second, we propose a joint model with a latent state to model cognitive evolution and its pre-dementia acceleration, dementia risk and death risk.
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http://hal.univ-nantes.fr/tel-03248321
Contributor : Etienne Dantan <>
Submitted on : Friday, June 4, 2021 - 10:04:38 PM
Last modification on : Wednesday, June 9, 2021 - 3:02:51 AM
Long-term archiving on: : Sunday, September 5, 2021 - 9:08:52 PM

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  • HAL Id : tel-03248321, version 1

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Etienne Dantan. Modèles conjoints pour données longitudinales et données de survie incomplètes appliqués à l'étude du vieillissement cognitif. Santé publique et épidémiologie. 2009-12-08, 2009. Français. ⟨tel-03248321⟩

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