So sánh phương pháp

	Mô hình Frailty Chia sẻ cho Dữ liệu Sống còn theo Cụm	Mô hình kết hợp cho dữ liệu dọc và dữ liệu thời gian đến sự kiện
Lĩnh vực	Phân tích sống còn	Phân tích sống còn
Họ	Survival analysis	Survival analysis
Năm ra đời	1979	2004
Người khởi xướng	Vaupel, J.W., Manton, K.G. & Stallard, E.	Tsiatis, A.A. & Davidian, M.; Rizopoulos, D.
Loại	Random effects survival model	Semiparametric regression model
Công trình gốc	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.
Tên gọi khác	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)
Liên quan	3	5
Tóm tắt	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.