Semi-competing risks data, including probably correlated non-terminal event and terminal event times, are frequently encountered in medical research. Work on semi-competing risks data has mainly focused on the situations without or only with low-dimensional covariates. However, high and ultra-high dimensional data have been very common in modern scientific research. In this article, we propose a model-free feature screening procedure for ultra-high dimensional semi-competing risks data to discover features contributing separately or jointly to non-terminal and terminal event times via Kolmogorov–Smirnov statistics. This new approach could be used for contexts with discrete, categorical and continuous covariates, which is achieved through the technique of slice-and-fuse. It enjoys several desirable advantages inherited in the Kolmogorov–Smirnov statistics. Under rather mild conditions, we show that the newly suggested method possesses sure screening property. Monte-Carlo simulation studies are conducted to investigate the finite sample properties of our proposed procedure and make comparisons with existing methods, while a real data example is also offered for illustration.

半竞争风险数据，包括可能相关的非终端事件和终端事件时间，在医学研究中经常遇到。半竞争风险数据的工作主要集中在没有或只有低维协变量的情况。然而，高维和超高维数据在现代科学研究中已经非常普遍。在本文中，我们提出了一种用于超高维半竞争风险数据的无模型特征筛选程序，以通过 Kolmogorov-Smirnov 统计发现单独或共同对非终端和终端事件时间做出贡献的特征。这种新方法可用于具有离散、分类和连续协变量的上下文，这是通过切片融合技术实现的。它享有 Kolmogorov-Smirnov 统计中继承的几个理想优势。在相当温和的条件下，我们表明新建议的方法具有确定的筛选特性。进行蒙特卡罗模拟研究以研究我们提出的程序的有限样本特性并与现有方法进行比较，同时还提供了一个真实的数据示例进行说明。