مقایسهٔ روش‌ها

	رگرسیون بقا با جریمه و انتخاب خودکار متغیر در مدل کوکس تطبیقی	تخمینگر بقای کاپلان-مایر
حوزه	اپیدمیولوژی	تحلیل بقا
خانواده	Process / pipeline	Survival analysis
سال پیدایش	2007 (adaptive LASSO variant); base Cox model 1972	1958
پدیدآور	Hao Helen Zhang & Wenbin Lu (adaptive LASSO formulation); base Cox model by David R. Cox	Kaplan, E. L. & Meier, P.
نوع	Penalized semi-parametric survival regression	Non-parametric survival estimator
منبع بنیادین	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.
نام‌های دیگر	adaptive Cox model, adaptive LASSO Cox regression, penalized Cox proportional hazards, adaptive regularized survival regression	product-limit estimator, km curve, kaplan-meier sağkalım analizi
مرتبط	5	2
خلاصه	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.