Σύγκριση μεθόδων

	Μοντέλο Συνδυασμού για Διαχρονικά Δεδομένα και Δεδομένα Χρόνου-έως-Συμβάντος	Εκτιμητής Επιβίωσης Kaplan-Meier
Πεδίο	Ανάλυση Επιβίωσης	Ανάλυση Επιβίωσης
Οικογένεια	Survival analysis	Survival analysis
Έτος προέλευσης	2004	1958
Δημιουργός	Tsiatis, A.A. & Davidian, M.; Rizopoulos, D.	Kaplan, E. L. & Meier, P.
Τύπος	Semiparametric regression model	Non-parametric survival estimator
Θεμελιώδης πηγή	Rizopoulos, D. (2012). Joint Models for Longitudinal and Time-to-Event Data. CRC Press.	Kaplan, E. L. & Meier, P. (1958). Nonparametric Estimation from Incomplete Observations. Journal of the American Statistical Association, 53(282), 457–481.
Εναλλακτικές ονομασίες	joint model, shared random effects model, longitudinal-survival joint model, Joint Model (Boylamsal + Sağkalım Birleşik Model)	product-limit estimator, km curve, kaplan-meier sağkalım analizi
Συναφείς	5	2
Σύνοψη	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 Kaplan-Meier estimator, introduced by Kaplan and Meier in 1958, is a non-parametric method that estimates the survival curve — the probability of remaining event-free over time — from right-censored time-to-event data. The log-rank test is the companion procedure used to compare survival curves between groups.