Comparar métodos

	Modelo de Frailidad Compartida para Datos de Supervivencia Agrupados	Modelo Conjunto para Datos Longitudinales y de Tiempo hasta un Evento
Campo	Supervivencia	Supervivencia
Familia	Survival analysis	Survival analysis
Año de origen	1979	2004
Autor original	Vaupel, J.W., Manton, K.G. & Stallard, E.	Tsiatis, A.A. & Davidian, M.; Rizopoulos, D.
Tipo	Random effects survival model	Semiparametric regression model
Fuente seminal	Vaupel, J.W., Manton, K.G. & Stallard, E. (1979). The Impact of Heterogeneity in Individual Frailty on the Dynamics of Mortality. Demography, 16(3), 439–454.	Rizopoulos, D. (2012). Joint Models for Longitudinal and Time-to-Event Data. CRC Press.
Alias	shared frailty model, random effects survival model, Frailty Modeli (Paylaşılan Kırılganlık)	joint model, shared random effects model, longitudinal-survival joint model, Joint Model (Boylamsal + Sağkalım Birleşik Model)
Relacionados	3	5
Resumen	The shared frailty model, introduced by Vaupel, Manton, and Stallard in 1979, extends standard survival regression by incorporating a random effect — the 'frailty' — that captures unobserved heterogeneity among subjects or clusters. When survival outcomes are measured on individuals who share a common environment (patients in the same hospital, members of the same family, animals in the same litter), a frailty term accounts for the within-cluster dependence that ordinary Cox regression ignores.	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.