方法对比
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| 自适应Cox比例风险模型× | Lasso 回归× | |
|---|---|---|
| 领域≠ | 流行病学 | 机器学习 |
| 方法族≠ | Process / pipeline | Machine learning |
| 起源年份≠ | 2007 (adaptive LASSO variant); base Cox model 1972 | 1996 |
| 提出者≠ | Hao Helen Zhang & Wenbin Lu (adaptive LASSO formulation); base Cox model by David R. Cox | Tibshirani, R. |
| 类型≠ | Penalized semi-parametric survival regression | Regularized linear regression (L1 penalty) |
| 开创性文献≠ | Zhang, H. H., & Lu, W. (2007). Adaptive Lasso for Cox's proportional hazards model. Biometrika, 94(3), 691–703. DOI ↗ | Tibshirani, R. (1996). Regression Shrinkage and Selection via the Lasso. Journal of the Royal Statistical Society: Series B, 58(1), 267–288. DOI ↗ |
| 别名 | adaptive Cox model, adaptive LASSO Cox regression, penalized Cox proportional hazards, adaptive regularized survival regression | LASSO Regresyonu, lasso, L1-regularized regression, L1 regularization |
| 相关≠ | 5 | 4 |
| 摘要≠ | 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. | Lasso regression, introduced by Robert Tibshirani in 1996, is a linear regression method that adds an L1 penalty to the loss so that it shrinks coefficients and performs variable selection at the same time, producing a sparse model. By driving some coefficients exactly to zero it keeps only the predictors that matter. |
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