Some interesting recent studies have shown that safe feature elimination screening algorithms are useful alternatives in solving large scale and/or ultra-high-dimensional Lasso-type problems. However, to the best of our knowledge, the plausibility of adapting the safe feature elimination screening algorithm to survival models is rarely explored. In this study, we first derive the safe feature elimination screening rule for adaptive Lasso Cox model. Then, using both simulated and real-world datasets, we demonstrate that the resulting algorithm can outperform Lasso Cox and adaptive Lasso Cox prediction methods in terms of its predictive performance. In addition to its good predictive performance, we illustrate that the proposed algorithm has a key computational advantage over the above competing methods in terms of computation efficiency.

最近一些有趣的研究表明，安全特征消除筛选算法是解决大规模和/或超高维套索类型问题的有用替代方法。然而，据我们所知，将安全特征消除筛选算法应用于生存模型的可能性很少被探索。在本研究中，我们首先推导出自适应 Lasso Cox 模型的安全特征消除筛选规则。然后，使用模拟和真实世界的数据集，我们证明所得算法在其预测性能方面可以优于 Lasso Cox 和自适应 Lasso Cox 预测方法。除了其良好的预测性能外，我们还说明了所提出的算法在计算效率方面比上述竞争方法具有关键的计算优势。