विधियों की तुलना करें

	कैप्लान-मीयर अनुमानक	कॉक्स प्रोपोर्शनल हैज़र्ड्स
क्षेत्र	सांख्यिकी	महामारी विज्ञान
परिवार	Survival analysis	Process / pipeline
उद्भव वर्ष	1958	1972
प्रवर्तक	Edward L. Kaplan and Paul Meier	Sir David Roxbee Cox
प्रकार	Nonparametric estimator	Semi-parametric regression model
मौलिक स्रोत	Kaplan, E. L., & Meier, P. (1958). Nonparametric estimation from incomplete observations. Journal of the American Statistical Association, 53(282), 457–481.	Cox, D. R. (1972). Regression models and life-tables. Journal of the Royal Statistical Society: Series B (Methodological), 34(2), 187–202.
उपनाम	KM estimator, product-limit estimator, Kaplan-Meier curve, survival curve estimator	Cox regression, Cox PH model, proportional hazards model, CPH
संबंधित	2	5
सारांश	The Kaplan-Meier estimator is a nonparametric method for estimating the survival function S(t) — the probability that an individual survives beyond time t — from data that include censored observations. Introduced by Edward L. Kaplan and Paul Meier in their landmark 1958 JASA paper, it is the standard first step in any survival analysis and is among the most-cited statistical methods in biomedical research.	The Cox proportional hazards model is a semi-parametric regression method that estimates the effect of one or more covariates on the hazard — the instantaneous rate of an event such as death, relapse, or failure — while making no assumption about the shape of the baseline hazard function. Introduced by David Cox in 1972, it is the dominant tool for multivariable survival analysis in clinical and epidemiological research.