Comparer des méthodes

	Modèle conjoint pour données longitudinales et données de temps d'événement	Modèle à effets mixtes
Domaine	Analyse de survie	Statistique
Famille	Survival analysis	Regression model
Année d'origine	2004	1982
Auteur d'origine	Tsiatis, A.A. & Davidian, M.; Rizopoulos, D.	Laird & Ware
Type	Semiparametric regression model	Mixed effects regression
Source fondatrice	Rizopoulos, D. (2012). Joint Models for Longitudinal and Time-to-Event Data. CRC Press.	Laird, N. M., & Ware, J. H. (1982). Random-effects models for longitudinal data. Biometrics, 38(4), 963–974.
Alias	joint model, shared random effects model, longitudinal-survival joint model, Joint Model (Boylamsal + Sağkalım Birleşik Model)	LME, LMM, mixed model, random effects model
Apparentées	5	4
Résumé	The joint model for longitudinal and time-to-event data, formalised by Tsiatis and Davidian in 2004 and extended comprehensively by Rizopoulos in 2012, simultaneously estimates a mixed-effects model for repeatedly measured biomarkers and a survival model for the time to an event, linking the two processes through shared random effects. It resolves two major problems that simpler approaches cannot handle: informative dropout from longitudinal studies and the endogeneity of time-varying biomarkers used as covariates in a Cox model.	A mixed effects model (or linear mixed model) extends ordinary regression by including both fixed effects — population-level parameters shared by all observations — and random effects that capture subject-, group-, or cluster-level variability. It is the standard tool for repeated-measures, longitudinal, and multilevel data where observations within the same unit are correlated.