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[Joint modeling of quantitative longitudinal data and censored survival time]

Abstract : BACKGROUND: In epidemiology, we are often interested in the association between the evolution of a quantitative variable and the onset of an event. The aim of this paper is to present a joint model for the analysis of Gaussian repeated data and survival time. Such models allow, for example, to perform survival analysis when a time-dependent explanatory variable is measured intermittently, or to study the evolution of a quantitative marker conditionally to an event. METHODS: They are constructed by combining a mixed model for repeated Gaussian variables and a survival model which can be parametric or semi-parametric (Cox model). RESULTS: We discuss the hypotheses underlying the different joint models proposed in the literature and the necessary assumptions for maximum likelihood estimation. The interest of these methods is illustrated with a study of the natural history of dementia in a cohort of elderly persons.
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https://www.hal.inserm.fr/inserm-00262018
Contributor : Evelyne Mouillet <>
Submitted on : Monday, March 10, 2008 - 4:13:06 PM
Last modification on : Tuesday, May 14, 2019 - 6:50:06 PM
Long-term archiving on: : Thursday, May 20, 2010 - 9:48:04 PM

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  • HAL Id : inserm-00262018, version 1
  • PUBMED : 15741913

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Hélène Jacqmin-Gadda, Rodolphe Thiébaut, Jean-François Dartigues. [Joint modeling of quantitative longitudinal data and censored survival time]. Epidemiology and Public Health / Revue d'Epidémiologie et de Santé Publique, Elsevier Masson, 2004, 52 (6), pp.502-10. ⟨inserm-00262018⟩

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