Porovnat metody

	Model zrychleného přežití (AFT)	Odhad Kaplan-Meierovy křivky přežití	Weibullova parametrická regresní analýza přežití
Obor	Analýza přežití	Analýza přežití	Analýza přežití
Rodina	Survival analysis	Survival analysis	Survival analysis
Rok vzniku	1992	1958	1951
Tvůrce	Wei, L. J. (seminal review 1992); origins in parametric survival literature	Kaplan, E. L. & Meier, P.	Waloddi Weibull
Typ	Parametric survival regression model	Non-parametric survival estimator	Fully parametric survival regression model
Původní zdroj	Wei, L. J. (1992). The Accelerated Failure Time Model: A Useful Alternative to the Cox Regression Model in Survival Analysis. Statistics in Medicine, 11(14–15), 1871–1879.	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.
Další názvy	AFT model, parametric survival regression, Hızlandırılmış Başarısızlık Zamanı Modeli (AFT)	product-limit estimator, km curve, kaplan-meier sağkalım analizi	weibull aft model, weibull survival model, parametric survival regression, Weibull Regresyonu — Parametrik Hayatta Kalma
Příbuzné	3	2	4
Shrnutí	The Accelerated Failure Time model is a parametric regression approach to survival analysis — formally reviewed and advocated by L. J. Wei in 1992 — in which covariates act as multiplicative factors that directly stretch or compress the time-to-event scale. Unlike the Cox proportional-hazards model, which models how covariates shift the hazard rate, AFT models express the covariate effect as an acceleration or deceleration of the time axis itself.	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.