Linganisha mbinu

	Usajili wa Kuishi	Kikokotozi cha Kuishi cha Kaplan-Meier	Regressioni ya Kuishi ya Weibull ya Parametric
Nyanja	Takwimu	Uchanganuzi wa Uhai	Uchanganuzi wa Uhai
Familia	Regression model	Survival analysis	Survival analysis
Mwaka wa asili	1980s	1958	1951
Mwanzilishi	Kalbfleisch & Prentice; Cox & Oakes	Kaplan, E. L. & Meier, P.	Waloddi Weibull
Aina	Parametric survival model	Non-parametric survival estimator	Fully parametric survival regression model
Chanzo asilia	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.
Majina mbadala	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
Zinazohusiana	3	2	4
Muhtasari	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.