方法对比
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| 自适应Cox比例风险模型× | Cox比例风险模型× | |
|---|---|---|
| 领域 | 流行病学 | 流行病学 |
| 方法族 | Process / pipeline | Process / pipeline |
| 起源年份≠ | 2007 (adaptive LASSO variant); base Cox model 1972 | 1972 |
| 提出者≠ | Hao Helen Zhang & Wenbin Lu (adaptive LASSO formulation); base Cox model by David R. Cox | Sir David Roxbee Cox |
| 类型≠ | Penalized semi-parametric survival regression | Semi-parametric regression 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 ↗ | Cox, D. R. (1972). Regression models and life-tables. Journal of the Royal Statistical Society: Series B (Methodological), 34(2), 187–202. DOI ↗ |
| 别名 | adaptive Cox model, adaptive LASSO Cox regression, penalized Cox proportional hazards, adaptive regularized survival regression | Cox regression, Cox PH model, proportional hazards model, CPH |
| 相关 | 5 | 5 |
| 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. | The Cox proportional hazards model is a semi-parametric regression method that estimates the effect of one or more covariates on the hazard — the instantaneous rate of an event such as death, relapse, or failure — while making no assumption about the shape of the baseline hazard function. Introduced by David Cox in 1972, it is the dominant tool for multivariable survival analysis in clinical and epidemiological research. |
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