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Updated: Aug 19, 2026

Establishing a Competing Risk Regression Nomogram Model for Survival Data
Published on: October 23, 2020
[Joint modeling of quantitative longitudinal data and censored survival time]
H Jacqmin-Gadda1, R Thiébaut, J-F Dartigues
1INSERM E 0338, Université Victor-Segalen-Bordeaux II, case 11, 146, rue Léo-Saignat, 33076 Bordeaux Cedex, France. helene.jacqmin-gadda@bordeux.inserm.fr
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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