Compara mètodes

	Modelatge Conjunt de Dades Longitudinals i de Temps fins a un Esdeveniment	Regressió de Cox amb covariables dependents del temps
Camp	Supervivència	Supervivència
Família	Survival analysis	Survival analysis
Any d'origen	2004	1972
Autor original	Tsiatis, A.A. & Davidian, M.; Rizopoulos, D.	Cox, D. R. (extended formulation by Therneau & Grambsch)
Tipus	Semiparametric regression model	Semi-parametric hazard regression model
Font seminal	Rizopoulos, D. (2012). Joint Models for Longitudinal and Time-to-Event Data. CRC Press.	Therneau, T. M. & Grambsch, P. M. (2000). Modeling Survival Data: Extending the Cox Model. Springer.
Àlies	joint model, shared random effects model, longitudinal-survival joint model, Joint Model (Boylamsal + Sağkalım Birleşik Model)	time-varying covariate Cox model, extended Cox model, Zamana Bağlı Kovaryatlı Cox Regresyonu
Relacionats	5	4
Resum	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.	Time-dependent Cox regression is an extension of the standard Cox proportional hazards model, introduced through the counting-process formulation developed by Therneau and Grambsch (2000), that allows one or more predictor variables to take different values at different points in a subject's follow-up period. It is the method of choice whenever a covariate — such as a laboratory measurement, a medication dose, or a disease severity score — changes over time rather than remaining fixed from study entry.