Comparar métodos

	Landmark Analysis	Modelo Conjunto para Datos Longitudinales y de Tiempo hasta un Evento	Estimador de Supervivencia de Kaplan-Meier	Estimador de Riesgo Acumulado de Nelson-Aalen
Campo	Supervivencia	Supervivencia	Supervivencia	Supervivencia
Familia	Survival analysis	Survival analysis	Survival analysis	Survival analysis
Año de origen	1983	2004	1958	1972
Autor original	Anderson, J. R., Cain, K. C. & Gelber, R. D.	Tsiatis, A.A. & Davidian, M.; Rizopoulos, D.	Kaplan, E. L. & Meier, P.	Wayne Nelson & Odd Aalen
Tipo	Conditional survival estimator	Semiparametric regression model	Non-parametric survival estimator	Non-parametric cumulative hazard estimator
Fuente seminal	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.	Nelson, W. (1972). Theory and applications of hazard plotting for censored failure data. Technometrics, 14(4), 945–966.
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	Nelson-Aalen cumulative hazard, Aalen estimator, empirical cumulative hazard, Nelson-Aalen kümülatif hazard tahmincisi
Relacionados	3	5	2	5
Resumen	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.	The Nelson-Aalen estimator is a non-parametric estimator of the cumulative hazard function from right-censored time-to-event data. Developed by Wayne Nelson for reliability hazard plotting in 1972 and placed on a rigorous counting-process foundation by Odd Aalen in 1978, it accumulates the ratio of observed events to the number at risk at each event time, providing the natural hazard-scale companion to the Kaplan-Meier survival curve.