Joint modeling of quantitative longitudinal data and censored survival time
Jacqmin-Gadda, H.; Thiébaut, R.; Dartigues, J-F.
Revue d'Epidemiologie et de Sante Publique 52(6): 502-510
2004
ISSN/ISBN: 0398-7620 PMID: 15741913 Document Number: 570871
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. 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). 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.