Sammenlign metoder

	Landemerkeanalyse for betinget overlevelse og dynamisk prediksjon	Fellesmodell for longitudinelle data og data om tid til hendelse
Fagfelt	Overlevelsesanalyse	Overlevelsesanalyse
Familie	Survival analysis	Survival analysis
Opprinnelsesår	1983	2004
Opphavsperson	Anderson, J. R., Cain, K. C. & Gelber, R. D.	Tsiatis, A.A. & Davidian, M.; Rizopoulos, D.
Type	Conditional survival estimator	Semiparametric regression model
Opprinnelig kilde	Anderson, J. R., Cain, K. C. & Gelber, R. D. (1983). Analysis of Survival by Tumor Response. Journal of Clinical Oncology, 1(11), 710–719.	Rizopoulos, D. (2012). Joint Models for Longitudinal and Time-to-Event Data. CRC Press.
Alias	landmark method, dynamic prediction, conditional survival estimation, Landmark Analizi (Dinamik Tahmin)	joint model, shared random effects model, longitudinal-survival joint model, Joint Model (Boylamsal + Sağkalım Birleşik Model)
Relaterte	3	5
Sammendrag	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.	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.