Salīdzināt metodes

	Kopīgais modelis longitudināliem datiem un notikuma iestāšanās laikam	Kopīgā trausluma modelis (Shared Frailty Model) grupētiem izdzīvošanas datiem
Nozare	Dzīvildze	Dzīvildze
Saime	Survival analysis	Survival analysis
Izcelsmes gads	2004	1979
Autors	Tsiatis, A.A. & Davidian, M.; Rizopoulos, D.	Vaupel, J.W., Manton, K.G. & Stallard, E.
Tips	Semiparametric regression model	Random effects survival model
Pirmavots	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.
Citi nosaukumi	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)
Saistītās	5	3
Kopsavilkums	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.