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Examine os métodos selecionados lado a lado; as linhas que diferem ficam destacadas.

Modelo Conjunto para Dados Longitudinais e de Tempo até o Evento×Estimador de Sobrevivência de Kaplan-Meier×
ÁreaAnálise de sobrevivênciaAnálise de sobrevivência
FamíliaSurvival analysisSurvival analysis
Ano de origem20041958
Autor originalTsiatis, A.A. & Davidian, M.; Rizopoulos, D.Kaplan, E. L. & Meier, P.
TipoSemiparametric regression modelNon-parametric survival estimator
Fonte seminalRizopoulos, D. (2012). Joint Models for Longitudinal and Time-to-Event Data. CRC Press. DOI ↗Kaplan, E. L. & Meier, P. (1958). Nonparametric Estimation from Incomplete Observations. Journal of the American Statistical Association, 53(282), 457–481. DOI ↗
Outros nomesjoint model, shared random effects model, longitudinal-survival joint model, Joint Model (Boylamsal + Sağkalım Birleşik Model)product-limit estimator, km curve, kaplan-meier sağkalım analizi
Relacionados52
ResumoThe 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.
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ScholarGateComparar métodos: Joint Model for Longitudinal and Survival Data · Kaplan-Meier. Recuperado em 2026-06-18 de https://scholargate.app/pt/compare