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	Adaptive Cox Proportional Hazards	Modello del Tempo di Fallimento Accelerato (AFT)
Campo	Epidemiologia	Analisi di sopravvivenza
Famiglia	Process / pipeline	Survival analysis
Anno di origine	2007 (adaptive LASSO variant); base Cox model 1972	1992
Ideatore	Hao Helen Zhang & Wenbin Lu (adaptive LASSO formulation); base Cox model by David R. Cox	Wei, L. J. (seminal review 1992); origins in parametric survival literature
Tipo	Penalized semi-parametric survival regression	Parametric survival regression model
Fonte seminale	Zhang, H. H., & Lu, W. (2007). Adaptive Lasso for Cox's proportional hazards model. Biometrika, 94(3), 691–703.	Wei, L. J. (1992). The Accelerated Failure Time Model: A Useful Alternative to the Cox Regression Model in Survival Analysis. Statistics in Medicine, 11(14–15), 1871–1879.
Alias	adaptive Cox model, adaptive LASSO Cox regression, penalized Cox proportional hazards, adaptive regularized survival regression	AFT model, parametric survival regression, Hızlandırılmış Başarısızlık Zamanı Modeli (AFT)
Correlati	5	3
Sintesi	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 Accelerated Failure Time model is a parametric regression approach to survival analysis — formally reviewed and advocated by L. J. Wei in 1992 — in which covariates act as multiplicative factors that directly stretch or compress the time-to-event scale. Unlike the Cox proportional-hazards model, which models how covariates shift the hazard rate, AFT models express the covariate effect as an acceleration or deceleration of the time axis itself.