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क्षेत्रउत्तरजीविताउत्तरजीविताउत्तरजीविता
परिवारSurvival analysisSurvival analysisSurvival analysis
उद्भव वर्ष197219791958
प्रवर्तकWayne Nelson & Odd AalenVaupel, J.W., Manton, K.G. & Stallard, E.Kaplan, E. L. & Meier, P.
प्रकारNon-parametric cumulative hazard estimatorRandom effects survival modelNon-parametric survival estimator
मौलिक स्रोतNelson, W. (1972). Theory and applications of hazard plotting for censored failure data. Technometrics, 14(4), 945–966. DOI ↗Vaupel, J.W., Manton, K.G. & Stallard, E. (1979). The Impact of Heterogeneity in Individual Frailty on the Dynamics of Mortality. Demography, 16(3), 439–454. DOI ↗Kaplan, E. L. & Meier, P. (1958). Nonparametric Estimation from Incomplete Observations. Journal of the American Statistical Association, 53(282), 457–481. DOI ↗
उपनामNelson-Aalen cumulative hazard, Aalen estimator, empirical cumulative hazard, Nelson-Aalen kümülatif hazard tahmincisishared frailty model, random effects survival model, Frailty Modeli (Paylaşılan Kırılganlık)product-limit estimator, km curve, kaplan-meier sağkalım analizi
संबंधित532
सारांशThe 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.The shared frailty model, introduced by Vaupel, Manton, and Stallard in 1979, extends standard survival regression by incorporating a random effect — the 'frailty' — that captures unobserved heterogeneity among subjects or clusters. When survival outcomes are measured on individuals who share a common environment (patients in the same hospital, members of the same family, animals in the same litter), a frailty term accounts for the within-cluster dependence that ordinary Cox regression ignores.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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