So sánh phương pháp

	Mô hình kết hợp cho dữ liệu dọc và dữ liệu thời gian đến sự kiện	Phân tích mốc thời gian cho sống còn có điều kiện và dự đoán động
Lĩnh vực	Phân tích sống còn	Phân tích sống còn
Họ	Survival analysis	Survival analysis
Năm ra đời	2004	1983
Người khởi xướng	Tsiatis, A.A. & Davidian, M.; Rizopoulos, D.	Anderson, J. R., Cain, K. C. & Gelber, R. D.
Loại	Semiparametric regression model	Conditional survival estimator
Công trình gốc	Rizopoulos, D. (2012). Joint Models for Longitudinal and Time-to-Event Data. CRC Press.	Anderson, J. R., Cain, K. C. & Gelber, R. D. (1983). Analysis of Survival by Tumor Response. Journal of Clinical Oncology, 1(11), 710–719.
Tên gọi khác	joint model, shared random effects model, longitudinal-survival joint model, Joint Model (Boylamsal + Sağkalım Birleşik Model)	landmark method, dynamic prediction, conditional survival estimation, Landmark Analizi (Dinamik Tahmin)
Liên quan	5	3
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.	Landmark analysis, introduced by Anderson, Cain, and Gelber in 1983, estimates conditional survival probabilities for subjects who are still at risk at a pre-specified point in time — the landmark — rather than at study entry. It was developed explicitly to avoid immortal time bias that arises when subjects are grouped by an event (such as a treatment change or biomarker result) that can only occur if they remain event-free long enough to experience it.