Linganisha mbinu

	Kikokotozi cha Kuishi cha Kaplan-Meier	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	1958	1966	1951
Mwanzilishi	Kaplan, E. L. & Meier, P.	Mantel, N.	Waloddi Weibull
Aina	Non-parametric survival estimator	Non-parametric hypothesis test	Fully parametric survival regression model
Chanzo asilia	Kaplan, E. L. & Meier, P. (1958). Nonparametric Estimation from Incomplete Observations. Journal of the American Statistical Association, 53(282), 457–481.	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	product-limit estimator, km curve, kaplan-meier sağkalım analizi	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	2	2	4
Muhtasari	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.	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.