Comparar métodos

	Cox Proporcional de Riesgos Adaptativo	Modelo de Riesgos Proporcionales de Cox
Campo	Epidemiología	Epidemiología
Familia	Process / pipeline	Process / pipeline
Año de origen	2007 (adaptive LASSO variant); base Cox model 1972	1972
Autor original	Hao Helen Zhang & Wenbin Lu (adaptive LASSO formulation); base Cox model by David R. Cox	Sir David Roxbee Cox
Tipo	Penalized semi-parametric survival regression	Semi-parametric regression model
Fuente seminal	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
Relacionados	5	5
Resumen	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.