विधियों की तुलना करें

	सर्वाइवल रिग्रेशन (Survival Regression)	कॉक्स प्रोपोर्शनल हैज़र्ड्स रिग्रेशन	कैप्लान-मेयर सर्वाइवल एस्टिमेटर	वेइबुल पैरामीट्रिक सर्वाइवल रिग्रेशन
क्षेत्र	सांख्यिकी	उत्तरजीविता	उत्तरजीविता	उत्तरजीविता
परिवार	Regression model	Survival analysis	Survival analysis	Survival analysis
उद्भव वर्ष	1980s	1972	1958	1951
प्रवर्तक	Kalbfleisch & Prentice; Cox & Oakes	Cox, D. R.	Kaplan, E. L. & Meier, P.	Waloddi Weibull
प्रकार	Parametric survival model	Semi-parametric hazard regression model	Non-parametric survival estimator	Fully parametric survival regression model
मौलिक स्रोत	Kalbfleisch, J. D., & Prentice, R. L. (2002). The Statistical Analysis of Failure Time Data (2nd ed.). Wiley. ISBN: 978-0471363576	Cox, D. R. (1972). Regression Models and Life-Tables. Journal of the Royal Statistical Society: Series B, 34(2), 187–202.	Kaplan, E. L. & Meier, P. (1958). Nonparametric Estimation from Incomplete Observations. Journal of the American Statistical Association, 53(282), 457–481.	Kalbfleisch, J. D. & Prentice, R. L. (2002). The Statistical Analysis of Failure Time Data (2nd ed.). Wiley.
उपनाम	accelerated failure time model, AFT model, parametric survival model, time-to-event regression	cox ph model, proportional hazards model, cox ph regression, Cox Orantılı Tehlikeler Regresyonu	product-limit estimator, km curve, kaplan-meier sağkalım analizi	weibull aft model, weibull survival model, parametric survival regression, Weibull Regresyonu — Parametrik Hayatta Kalma
संबंधित	3	3	2	4
सारांश	Survival regression models the time until an event occurs — such as death, failure, or relapse — as a function of covariates. Unlike ordinary regression, it properly accounts for censored observations (cases where the event had not yet occurred at the end of follow-up) by specifying a parametric distribution for the survival time and estimating covariate effects via maximum likelihood.	Cox proportional hazards regression, introduced by D. R. Cox in 1972, is a semi-parametric model that estimates how one or more covariates affect the hazard — the instantaneous rate of experiencing an event — while leaving the baseline hazard function unspecified. It is the standard multivariable method in survival analysis and produces hazard ratios that quantify the relative risk associated with each predictor.	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.	Weibull regression is a fully parametric survival model, formalised by Kalbfleisch and Prentice, that assumes survival times follow a Weibull distribution. A shape parameter controls whether the hazard increases, decreases, or remains constant over time, while covariates shift the scale of the distribution to express how predictors affect survival.