Compară metode

	Regresia Cox cu riscuri proporționale	Regresie Robustă	Regresia supraviețuirii
Domeniu	Supraviețuire	Statistică	Statistică
Familie	Survival analysis	Regression model	Regression model
Anul apariției	1972	1964	1980s
Autorul original	Cox, D. R.	Peter J. Huber (M-estimation, 1964); Frank Hampel (influence function, 1974)	Kalbfleisch & Prentice; Cox & Oakes
Tip	Semi-parametric hazard regression model	Regression with outlier resistance	Parametric survival model
Sursa seminală	Cox, D. R. (1972). Regression Models and Life-Tables. Journal of the Royal Statistical Society: Series B, 34(2), 187–202.	Huber, P. J. (1964). Robust estimation of a location parameter. The Annals of Mathematical Statistics, 35(1), 73–101.	Kalbfleisch, J. D., & Prentice, R. L. (2002). The Statistical Analysis of Failure Time Data (2nd ed.). Wiley. ISBN: 978-0471363576
Denumiri alternative	cox ph model, proportional hazards model, cox ph regression, Cox Orantılı Tehlikeler Regresyonu	M-estimation regression, robust linear regression, outlier-resistant regression, MM-estimation	accelerated failure time model, AFT model, parametric survival model, time-to-event regression
Înrudite	3	6	3
Rezumat	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.	Robust regression estimates the linear relationship between a continuous outcome and predictors while sharply reducing the influence of outliers and leverage points. Unlike OLS, which is highly sensitive to extreme observations, robust methods assign down-weighted influence to atypical data points, producing coefficient estimates that remain stable even when a fraction of the data is contaminated or non-normally distributed.	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.