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Cox Proporcional de Riesgos Adaptativo×Estimador de Supervivencia de Kaplan-Meier×
CampoEpidemiologíaSupervivencia
FamiliaProcess / pipelineSurvival analysis
Año de origen2007 (adaptive LASSO variant); base Cox model 19721958
Autor originalHao Helen Zhang & Wenbin Lu (adaptive LASSO formulation); base Cox model by David R. CoxKaplan, E. L. & Meier, P.
TipoPenalized semi-parametric survival regressionNon-parametric survival estimator
Fuente seminalZhang, H. H., & Lu, W. (2007). Adaptive Lasso for Cox's proportional hazards model. Biometrika, 94(3), 691–703. DOI ↗Kaplan, E. L. & Meier, P. (1958). Nonparametric Estimation from Incomplete Observations. Journal of the American Statistical Association, 53(282), 457–481. DOI ↗
Aliasadaptive Cox model, adaptive LASSO Cox regression, penalized Cox proportional hazards, adaptive regularized survival regressionproduct-limit estimator, km curve, kaplan-meier sağkalım analizi
Relacionados52
ResumenThe 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 Kaplan-Meier estimator, introduced by Kaplan and Meier in 1958, is a non-parametric method that estimates the survival curve — the probability of remaining event-free over time — from right-censored time-to-event data. The log-rank test is the companion procedure used to compare survival curves between groups.
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ScholarGateComparar métodos: Adaptive Cox Proportional Hazards · Kaplan-Meier. Recuperado el 2026-06-19 de https://scholargate.app/es/compare