Methoden vergelijken

	Accelerated Failure Time (AFT) Model	Cox Proportionele Risico's Regressie	Weibull parametrische overlevingsregressie
Vakgebied	Overlevingsanalyse	Overlevingsanalyse	Overlevingsanalyse
Familie	Survival analysis	Survival analysis	Survival analysis
Jaar van ontstaan	1992	1972	1951
Grondlegger	Wei, L. J. (seminal review 1992); origins in parametric survival literature	Cox, D. R.	Waloddi Weibull
Type	Parametric survival regression model	Semi-parametric hazard regression model	Fully parametric survival regression model
Oorspronkelijke bron	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.	Cox, D. R. (1972). Regression Models and Life-Tables. Journal of the Royal Statistical Society: Series B, 34(2), 187–202.	Kalbfleisch, J. D. & Prentice, R. L. (2002). The Statistical Analysis of Failure Time Data (2nd ed.). Wiley.
Aliassen	AFT model, parametric survival regression, Hızlandırılmış Başarısızlık Zamanı Modeli (AFT)	cox ph model, proportional hazards model, cox ph regression, Cox Orantılı Tehlikeler Regresyonu	weibull aft model, weibull survival model, parametric survival regression, Weibull Regresyonu — Parametrik Hayatta Kalma
Verwant	3	3	4
Samenvatting	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.	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.	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.