قارن الطرق

	نموذج الهشاشة المشتركة للبيانات البقائية المجمعة	النموذج المشترك للبيانات الطولية والبيانات المتعلقة بالوقت حتى وقوع حدث
المجال	تحليل البقاء	تحليل البقاء
العائلة	Survival analysis	Survival analysis
سنة النشأة	1979	2004
صاحب الطريقة	Vaupel, J.W., Manton, K.G. & Stallard, E.	Tsiatis, A.A. & Davidian, M.; Rizopoulos, D.
النوع	Random effects survival model	Semiparametric regression model
المصدر التأسيسي	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.
الأسماء البديلة	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)
ذات صلة	3	5
الملخص	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.