Methoden vergelijken

	Gezamenlijk model voor longitudinale en tijd-tot-gebeurtenisgegevens	Landmarkanalyse voor Conditionele Overleving en Dynamische Predictie
Vakgebied	Overlevingsanalyse	Overlevingsanalyse
Familie	Survival analysis	Survival analysis
Jaar van ontstaan	2004	1983
Grondlegger	Tsiatis, A.A. & Davidian, M.; Rizopoulos, D.	Anderson, J. R., Cain, K. C. & Gelber, R. D.
Type	Semiparametric regression model	Conditional survival estimator
Oorspronkelijke bron	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.
Aliassen	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)
Verwant	5	3
Samenvatting	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.