Bandingkan kaedah

	Analisis Tanda Aras untuk Kelangsungan Bersyarat dan Ramalan Dinamik	Model Gabungan untuk Data Longitudinal dan Masa-ke-Peristiwa	Penganggar Kemandirian Kaplan-Meier
Bidang	Analisis Survival	Analisis Survival	Analisis Survival
Keluarga	Survival analysis	Survival analysis	Survival analysis
Tahun asal	1983	2004	1958
Pengasas	Anderson, J. R., Cain, K. C. & Gelber, R. D.	Tsiatis, A.A. & Davidian, M.; Rizopoulos, D.	Kaplan, E. L. & Meier, P.
Jenis	Conditional survival estimator	Semiparametric regression model	Non-parametric survival estimator
Sumber perintis	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.	Kaplan, E. L. & Meier, P. (1958). Nonparametric Estimation from Incomplete Observations. Journal of the American Statistical Association, 53(282), 457–481.
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)	product-limit estimator, km curve, kaplan-meier sağkalım analizi
Berkaitan	3	5	2
Ringkasan	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.	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.