Literature DB >> 15741913

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

H Jacqmin-Gadda1, R Thiébaut, J-F Dartigues.   

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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Mesh:

Year:  2004        PMID: 15741913     DOI: 10.1016/s0398-7620(04)99090-6

Source DB:  PubMed          Journal:  Rev Epidemiol Sante Publique        ISSN: 0398-7620            Impact factor:   1.019


  2 in total

1.  Joint modeling of longitudinal health-related quality of life data and survival.

Authors:  Divine E Ediebah; Francisca Galindo-Garre; Bernard M J Uitdehaag; Jolie Ringash; Jaap C Reijneveld; Linda Dirven; Efstathios Zikos; Corneel Coens; Martin J van den Bent; Andrew Bottomley; Martin J B Taphoorn
Journal:  Qual Life Res       Date:  2014-10-14       Impact factor: 4.147

2.  Joint modeling of multivariate longitudinal data and the dropout process in a competing risk setting: application to ICU data.

Authors:  Emmanuelle Deslandes; Sylvie Chevret
Journal:  BMC Med Res Methodol       Date:  2010-07-29       Impact factor: 4.615

  2 in total

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