Comparar métodos

	Modelo de Frailidad Compartida para Datos de Supervivencia Agrupados	Modelo de Supervivencia para Eventos Recurrentes
Campo	Supervivencia	Supervivencia
Familia	Survival analysis	Survival analysis
Año de origen	1979	1981
Autor original	Vaupel, J.W., Manton, K.G. & Stallard, E.	Andersen & Gill (AG, 1982); Prentice, Williams & Peterson (PWP, 1981); Wei, Lin & Weissfeld (WLW, 1989)
Tipo	Random effects survival model	Semi-parametric hazard model for repeated events
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.	Cook, R.J. & Lawless, J.F. (2007). The Statistical Analysis of Recurrent Events. Springer.
Alias	shared frailty model, random effects survival model, Frailty Modeli (Paylaşılan Kırılganlık)	Tekrarlayan Olay Modeli (Recurrent Events), Andersen-Gill model, AG model, Wei-Lin-Weissfeld model
Relacionados	3	4
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.	A recurrent event model is a survival analysis extension, formalised through the landmark contributions of Prentice, Williams and Peterson (1981), Andersen and Gill (1982), and Wei, Lin and Weissfeld (1989), that models time-to-event data when the same event — such as a hospital readmission, disease relapse, or equipment failure — can occur multiple times in the same individual. The three principal frameworks are the Andersen-Gill (AG) model, the Prentice-Williams-Peterson (PWP) stratified model, and the Wei-Lin-Weissfeld (WLW) marginal model, each making different assumptions about within-subject dependence.