قارن الطرق

	نموذج كوكس التكيفي للمخاطر النسبية	مقدّر البقاء لكابلان-ماير
المجال	علم الأوبئة	تحليل البقاء
العائلة	Process / pipeline	Survival analysis
سنة النشأة	2007 (adaptive LASSO variant); base Cox model 1972	1958
صاحب الطريقة	Hao Helen Zhang & Wenbin Lu (adaptive LASSO formulation); base Cox model by David R. Cox	Kaplan, E. L. & Meier, P.
النوع	Penalized semi-parametric survival regression	Non-parametric survival estimator
المصدر التأسيسي	Zhang, H. H., & Lu, W. (2007). Adaptive Lasso for Cox's proportional hazards model. Biometrika, 94(3), 691–703.	Kaplan, E. L. & Meier, P. (1958). Nonparametric Estimation from Incomplete Observations. Journal of the American Statistical Association, 53(282), 457–481.
الأسماء البديلة	adaptive Cox model, adaptive LASSO Cox regression, penalized Cox proportional hazards, adaptive regularized survival regression	product-limit estimator, km curve, kaplan-meier sağkalım analizi
ذات صلة	5	2
الملخص	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 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.