So sánh phương pháp

	Mô hình kết hợp cho dữ liệu dọc và dữ liệu thời gian đến sự kiện	Hồi quy Cox với các hiệp biến thay đổi theo thời gian
Lĩnh vực	Phân tích sống còn	Phân tích sống còn
Họ	Survival analysis	Survival analysis
Năm ra đời	2004	1972
Người khởi xướng	Tsiatis, A.A. & Davidian, M.; Rizopoulos, D.	Cox, D. R. (extended formulation by Therneau & Grambsch)
Loại	Semiparametric regression model	Semi-parametric hazard regression model
Công trình gốc	Rizopoulos, D. (2012). Joint Models for Longitudinal and Time-to-Event Data. CRC Press.	Therneau, T. M. & Grambsch, P. M. (2000). Modeling Survival Data: Extending the Cox Model. Springer.
Tên gọi khác	joint model, shared random effects model, longitudinal-survival joint model, Joint Model (Boylamsal + Sağkalım Birleşik Model)	time-varying covariate Cox model, extended Cox model, Zamana Bağlı Kovaryatlı Cox Regresyonu
Liên quan	5	4
Tóm tắt	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.	Time-dependent Cox regression is an extension of the standard Cox proportional hazards model, introduced through the counting-process formulation developed by Therneau and Grambsch (2000), that allows one or more predictor variables to take different values at different points in a subject's follow-up period. It is the method of choice whenever a covariate — such as a laboratory measurement, a medication dose, or a disease severity score — changes over time rather than remaining fixed from study entry.