方法对比
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| 自适应Cox比例风险模型× | 随机生存森林× | |
|---|---|---|
| 领域≠ | 流行病学 | 生存分析 |
| 方法族≠ | Process / pipeline | Survival analysis |
| 起源年份≠ | 2007 (adaptive LASSO variant); base Cox model 1972 | 2008 |
| 提出者≠ | Hao Helen Zhang & Wenbin Lu (adaptive LASSO formulation); base Cox model by David R. Cox | Ishwaran, H., Kogalur, U.B., Blackstone, E.H. & Lauer, M.S. |
| 类型≠ | Penalized semi-parametric survival regression | Ensemble machine learning survival model |
| 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 ↗ | Ishwaran, H., Kogalur, U.B., Blackstone, E.H. & Lauer, M.S. (2008). Random Survival Forests. Annals of Applied Statistics, 2(3), 841–860. DOI ↗ |
| 别名≠ | adaptive Cox model, adaptive LASSO Cox regression, penalized Cox proportional hazards, adaptive regularized survival regression | RSF, Rastgele Sağkalım Ormanı (RSF), survival random forest |
| 相关≠ | 5 | 2 |
| 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. | Random Survival Forest (RSF), introduced by Ishwaran, Kogalur, Blackstone, and Lauer in 2008, is an ensemble machine learning method that adapts the Random Forest algorithm to time-to-event (survival) data. Trees are grown using log-rank splitting to handle censored observations naturally, and the ensemble aggregates cumulative hazard functions across hundreds of trees to produce predictions and variable importance rankings. |
| ScholarGate数据集 ↗ |
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