Comparar métodos

	Adaptive Cox Proportional Hazards	Floresta Aleatória para Sobrevivência
Área	Epidemiologia	Análise de sobrevivência
Família	Process / pipeline	Survival analysis
Ano de origem	2007 (adaptive LASSO variant); base Cox model 1972	2008
Autor original	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.
Tipo	Penalized semi-parametric survival regression	Ensemble machine learning survival model
Fonte seminal	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.
Outros nomes	adaptive Cox model, adaptive LASSO Cox regression, penalized Cox proportional hazards, adaptive regularized survival regression	RSF, Rastgele Sağkalım Ormanı (RSF), survival random forest
Relacionados	5	2
Resumo	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.