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A latent process model for joint modeling of events and marker.

Abstract : The paper formulates joint modeling of a counting process and a sequence of longitudinal measurements, governed by a common latent stochastic process. The latent process is modeled as a function of explanatory variables and a Brownian motion process. The conditional likelihood given values of the latent process at the measurement times, has been drawn using Brownian bridge properties; then integrating over all possible values of the latent process at the measurement times leads to the desired joint likelihood. An estimation procedure using joint likelihood and a numerical optimization is described. The method is applied to the study of cognitive decline and Alzheimer's disease.
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https://www.hal.inserm.fr/inserm-00262051
Contributor : Evelyne Mouillet <>
Submitted on : Monday, March 10, 2008 - 4:46:44 PM
Last modification on : Wednesday, November 29, 2017 - 2:54:13 PM
Long-term archiving on: : Thursday, May 20, 2010 - 9:48:32 PM

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  • HAL Id : inserm-00262051, version 1
  • PUBMED : 15000408

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Reza Hashemi, Hélène Jacqmin-Gadda, Daniel Commenges. A latent process model for joint modeling of events and marker.. Lifetime Data Analysis, Springer Verlag, 2003, 9 (4), pp.331-43. ⟨inserm-00262051⟩

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