Linganisha mbinu
Pitia mbinu ulizochagua bega kwa bega; safu zinazotofautiana zinaangaziwa.
| Muda wa Cox wa Hatari za Uwiano× | Kikokotozi cha Kuishi cha Kaplan-Meier× | |
|---|---|---|
| Nyanja≠ | Epidemiolojia | Uchanganuzi wa Uhai |
| Familia≠ | Process / pipeline | Survival analysis |
| Mwaka wa asili≠ | 2007 (adaptive LASSO variant); base Cox model 1972 | 1958 |
| Mwanzilishi≠ | Hao Helen Zhang & Wenbin Lu (adaptive LASSO formulation); base Cox model by David R. Cox | Kaplan, E. L. & Meier, P. |
| Aina≠ | Penalized semi-parametric survival regression | Non-parametric survival estimator |
| Chanzo asilia≠ | Zhang, 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 ↗ |
| Majina mbadala≠ | adaptive Cox model, adaptive LASSO Cox regression, penalized Cox proportional hazards, adaptive regularized survival regression | product-limit estimator, km curve, kaplan-meier sağkalım analizi |
| Zinazohusiana≠ | 5 | 2 |
| Muhtasari≠ | 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. |
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