Chen Xiaolin, Li Chenguang, Zhang Tao, Gao Zhenlong
School of Statistics, Qufu Normal University, Qufu, People's Republic of China.
School of Science, Guangxi University of Science and Technology, Liuzhou, People's Republic of China.
J Appl Stat. 2021 Feb 9;49(7):1848-1864. doi: 10.1080/02664763.2021.1884209. eCollection 2022.
In recent years, numerous feature screening schemes have been developed for ultra-high dimensional standard survival data with only one failure event. Nevertheless, existing literature pays little attention to related investigations for competing risks data, in which subjects suffer from multiple mutually exclusive failures. In this article, we develop a new marginal feature screening for ultra-high dimensional time-to-event data to allow for competing risks. The proposed procedure is model-free, and robust against heavy-tailed distributions and potential outliers for time to the type of failure of interest. Apart from this, it is invariant to any monotone transformation of event time of interest. Under rather mild assumptions, it is shown that the newly suggested approach possesses the ranking consistency and sure independence screening properties. Some numerical studies are conducted to evaluate the finite-sample performance of our method and make a comparison with its competitor, while an application to a real data set is provided to serve as an illustration.
近年来，针对仅有一个失败事件的超高维标准生存数据，已经开发出了众多特征筛选方案。然而，现有文献很少关注竞争风险数据的相关研究，在竞争风险数据中，个体可能遭遇多种相互排斥的失败情况。在本文中，我们针对超高维生存时间数据开发了一种新的边际特征筛选方法，以处理竞争风险。所提出的方法无需模型，对于感兴趣的失败类型的时间分布具有重尾性和潜在异常值具有鲁棒性。除此之外，它对于感兴趣的事件时间的任何单调变换都是不变的。在相当温和的假设下，结果表明新提出的方法具有排序一致性和确定独立性筛选属性。进行了一些数值研究来评估我们方法的有限样本性能，并与竞争对手进行比较，同时提供了一个实际数据集的应用示例。
