Comparer des méthodes

	Modèle Cox à risques proportionnels adaptatif	Forêt aléatoire de survie
Domaine	Épidémiologie	Analyse de survie
Famille	Process / pipeline	Survival analysis
Année d'origine	2007 (adaptive LASSO variant); base Cox model 1972	2008
Auteur d'origine	Hao Helen Zhang & Wenbin Lu (adaptive LASSO formulation); base Cox model by David R. Cox	Ishwaran, H., Kogalur, U.B., Blackstone, E.H. & Lauer, M.S.
Type	Penalized semi-parametric survival regression	Ensemble machine learning survival model
Source fondatrice	Zhang, H. H., & Lu, W. (2007). Adaptive Lasso for Cox's proportional hazards model. Biometrika, 94(3), 691–703.	Ishwaran, H., Kogalur, U.B., Blackstone, E.H. & Lauer, M.S. (2008). Random Survival Forests. Annals of Applied Statistics, 2(3), 841–860.
Alias	adaptive Cox model, adaptive LASSO Cox regression, penalized Cox proportional hazards, adaptive regularized survival regression	RSF, Rastgele Sağkalım Ormanı (RSF), survival random forest
Apparentées	5	2
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.	Random Survival Forest (RSF), introduced by Ishwaran, Kogalur, Blackstone, and Lauer in 2008, is an ensemble machine learning method that adapts the Random Forest algorithm to time-to-event (survival) data. Trees are grown using log-rank splitting to handle censored observations naturally, and the ensemble aggregates cumulative hazard functions across hundreds of trees to produce predictions and variable importance rankings.