Módszerek összehasonlítása

	Joint Model for Longitudinal and Survival Data	Közös frailitási modell klaszterezett túlélési adatokhoz
Tudományterület	Túléléselemzés	Túléléselemzés
Módszercsalád	Survival analysis	Survival analysis
Keletkezés éve	2004	1979
Megalkotó	Tsiatis, A.A. & Davidian, M.; Rizopoulos, D.	Vaupel, J.W., Manton, K.G. & Stallard, E.
Típus	Semiparametric regression model	Random effects survival model
Alapmű	Rizopoulos, D. (2012). Joint Models for Longitudinal and Time-to-Event Data. CRC Press.	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.
Alternatív nevek	joint model, shared random effects model, longitudinal-survival joint model, Joint Model (Boylamsal + Sağkalım Birleşik Model)	shared frailty model, random effects survival model, Frailty Modeli (Paylaşılan Kırılganlık)
Kapcsolódó	5	3
Összefoglaló	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.	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.