Salīdzināt metodes

	Regresija izdzīvošanai	Kaplana-Meiera izdzīvošanas novērtētājs	Veibula parametriskā izdzīvošanas regresija
Nozare	Statistika	Dzīvildze	Dzīvildze
Saime	Regression model	Survival analysis	Survival analysis
Izcelsmes gads	1980s	1958	1951
Autors	Kalbfleisch & Prentice; Cox & Oakes	Kaplan, E. L. & Meier, P.	Waloddi Weibull
Tips	Parametric survival model	Non-parametric survival estimator	Fully parametric survival regression model
Pirmavots	Kalbfleisch, J. D., & Prentice, R. L. (2002). The Statistical Analysis of Failure Time Data (2nd ed.). Wiley. ISBN: 978-0471363576	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.
Citi nosaukumi	accelerated failure time model, AFT model, parametric survival model, time-to-event regression	product-limit estimator, km curve, kaplan-meier sağkalım analizi	weibull aft model, weibull survival model, parametric survival regression, Weibull Regresyonu — Parametrik Hayatta Kalma
Saistītās	3	2	4
Kopsavilkums	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.	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.