Comparer des méthodes

	Modèle Cox à risques proportionnels adaptatif	Cox proportional hazards
Domaine	Épidémiologie	Épidémiologie
Famille	Process / pipeline	Process / pipeline
Année d'origine	2007 (adaptive LASSO variant); base Cox model 1972	1972
Auteur d'origine	Hao Helen Zhang & Wenbin Lu (adaptive LASSO formulation); base Cox model by David R. Cox	Sir David Roxbee Cox
Type	Penalized semi-parametric survival regression	Semi-parametric regression model
Source fondatrice	Zhang, H. H., & Lu, W. (2007). Adaptive Lasso for Cox's proportional hazards model. Biometrika, 94(3), 691–703.	Cox, D. R. (1972). Regression models and life-tables. Journal of the Royal Statistical Society: Series B (Methodological), 34(2), 187–202.
Alias	adaptive Cox model, adaptive LASSO Cox regression, penalized Cox proportional hazards, adaptive regularized survival regression	Cox regression, Cox PH model, proportional hazards model, CPH
Apparentées	5	5
Résumé	The Adaptive Cox Proportional Hazards model extends the classic Cox regression for time-to-event outcomes by adding adaptive LASSO (or related) penalization. It simultaneously estimates hazard ratios and performs variable selection, shrinking irrelevant covariate coefficients exactly to zero. This makes it especially valuable in high-dimensional clinical or genomic datasets where the number of candidate predictors is large relative to the number of events.	The Cox proportional hazards model is a semi-parametric regression method that estimates the effect of one or more covariates on the hazard — the instantaneous rate of an event such as death, relapse, or failure — while making no assumption about the shape of the baseline hazard function. Introduced by David Cox in 1972, it is the dominant tool for multivariable survival analysis in clinical and epidemiological research.