পদ্ধতির তুলনা করুন

	সার্ভাইভাল রিগ্রেশন	Cox Proportional Hazards Regression	Weibull Parametric Survival Regression
ক্ষেত্র	পরিসংখ্যান	উত্তরজীবিতা	উত্তরজীবিতা
পরিবার	Regression model	Survival analysis	Survival analysis
উদ্ভবের বছর	1980s	1972	1951
প্রবর্তক	Kalbfleisch & Prentice; Cox & Oakes	Cox, D. R.	Waloddi Weibull
ধরন	Parametric survival model	Semi-parametric hazard regression model	Fully parametric survival regression model
মৌলিক উৎস	Kalbfleisch, J. D., & Prentice, R. L. (2002). The Statistical Analysis of Failure Time Data (2nd ed.). Wiley. ISBN: 978-0471363576	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.
অপর নাম	accelerated failure time model, AFT model, parametric survival model, time-to-event regression	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
সম্পর্কিত	3	3	4
সারসংক্ষেপ	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.	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.