Linganisha mbinu

	Mfumo wa Kuongeza kasi ya Kushindwa kwa Wakati (AFT)	Kipimo cha Log-Rank cha Kulinganisha Milia ya Uhai	Regressioni ya Kuishi ya Weibull ya Parametric
Nyanja	Uchanganuzi wa Uhai	Uchanganuzi wa Uhai	Uchanganuzi wa Uhai
Familia	Survival analysis	Survival analysis	Survival analysis
Mwaka wa asili	1992	1966	1951
Mwanzilishi	Wei, L. J. (seminal review 1992); origins in parametric survival literature	Mantel, N.	Waloddi Weibull
Aina	Parametric survival regression model	Non-parametric hypothesis test	Fully parametric survival regression model
Chanzo asilia	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.	Mantel, N. (1966). Evaluation of Survival Data and Two New Rank Order Statistics Arising in Its Consideration. Cancer Chemotherapy Reports, 50(3), 163–170.	Kalbfleisch, J. D. & Prentice, R. L. (2002). The Statistical Analysis of Failure Time Data (2nd ed.). Wiley.
Majina mbadala	AFT model, parametric survival regression, Hızlandırılmış Başarısızlık Zamanı Modeli (AFT)	Mantel log-rank test, Mantel-Cox test, log-rank sağkalım testi, Log-Rank Testi	weibull aft model, weibull survival model, parametric survival regression, Weibull Regresyonu — Parametrik Hayatta Kalma
Zinazohusiana	3	2	4
Muhtasari	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 log-rank test, developed by Nathan Mantel in 1966, is a non-parametric hypothesis test that compares the overall survival experience of two or more groups throughout the entire follow-up period. It is the standard companion to Kaplan-Meier curves and determines whether observed differences between curves are statistically meaningful.	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.