Methoden vergelijken

	Robuuste Cox-regressie	Regressieanalyse van overlevingstijd
Vakgebied	Statistiek	Statistiek
Familie	Regression model	Regression model
Jaar van ontstaan	1989	1980s
Grondlegger	Lin & Wei	Kalbfleisch & Prentice; Cox & Oakes
Type	Semi-parametric survival regression with robust variance	Parametric survival model
Oorspronkelijke bron	Lin, D. Y., & Wei, L. J. (1989). The robust inference for the Cox proportional hazards model. Journal of the American Statistical Association, 84(408), 1074–1078.	Kalbfleisch, J. D., & Prentice, R. L. (2002). The Statistical Analysis of Failure Time Data (2nd ed.). Wiley. ISBN: 978-0471363576
Aliassen	Cox model with robust standard errors, sandwich-variance Cox regression, Lin-Wei robust Cox model, robust partial likelihood regression	accelerated failure time model, AFT model, parametric survival model, time-to-event regression
Verwant	3	3
Samenvatting	Robust Cox regression fits the standard Cox proportional hazards model but replaces the model-based variance estimate with a sandwich (Huber-White) estimator. This yields valid standard errors and confidence intervals even when observations are clustered, the independence assumption is mildly violated, or the working model is slightly misspecified, without discarding the familiar hazard-ratio interpretation.	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.