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Регресия на Кокс с времево-променливи ковариати×Оценител на преживяемостта на Каплан-Майер×
ОбластАнализ на преживяемосттаАнализ на преживяемостта
СемействоSurvival analysisSurvival analysis
Година на възникване19721958
СъздателCox, D. R. (extended formulation by Therneau & Grambsch)Kaplan, E. L. & Meier, P.
ТипSemi-parametric hazard regression modelNon-parametric survival estimator
Основополагащ източникTherneau, T. M. & Grambsch, P. M. (2000). Modeling Survival Data: Extending the Cox Model. Springer. DOI ↗Kaplan, E. L. & Meier, P. (1958). Nonparametric Estimation from Incomplete Observations. Journal of the American Statistical Association, 53(282), 457–481. DOI ↗
Други названияtime-varying covariate Cox model, extended Cox model, Zamana Bağlı Kovaryatlı Cox Regresyonuproduct-limit estimator, km curve, kaplan-meier sağkalım analizi
Свързани42
РезюмеTime-dependent Cox regression is an extension of the standard Cox proportional hazards model, introduced through the counting-process formulation developed by Therneau and Grambsch (2000), that allows one or more predictor variables to take different values at different points in a subject's follow-up period. It is the method of choice whenever a covariate — such as a laboratory measurement, a medication dose, or a disease severity score — changes over time rather than remaining fixed from study entry.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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  2. 1 Източници
  3. PUBLISHED
  1. v2
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ScholarGateСравнение на методи: Time-Dependent Cox Regression · Kaplan-Meier. Извлечено на 2026-06-17 от https://scholargate.app/bg/compare