विधियों की तुलना करें

	अनुकूली कॉक्स आनुपातिक ख़तरे	कैप्लान-मेयर सर्वाइवल एस्टिमेटर
क्षेत्र	महामारी विज्ञान	उत्तरजीविता
परिवार	Process / pipeline	Survival analysis
उद्भव वर्ष	2007 (adaptive LASSO variant); base Cox model 1972	1958
प्रवर्तक	Hao Helen Zhang & Wenbin Lu (adaptive LASSO formulation); base Cox model by David R. Cox	Kaplan, E. L. & Meier, P.
प्रकार	Penalized semi-parametric survival regression	Non-parametric survival estimator
मौलिक स्रोत	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.
उपनाम	adaptive Cox model, adaptive LASSO Cox regression, penalized Cox proportional hazards, adaptive regularized survival regression	product-limit estimator, km curve, kaplan-meier sağkalım analizi
संबंधित	5	2
सारांश	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.