Division of Biostatistics, German Cancer Research Center (DKFZ), Heidelberg, Germany.
Biom J. 2020 May;62(3):610-626. doi: 10.1002/bimj.201800269. Epub 2019 Aug 26.
When performing survival analysis in very high dimensions, it is often required to reduce the number of covariates using preliminary screening. During the last years, a large number of variable screening methods for the survival context have been developed. However, guidance is missing for choosing an appropriate method in practice. The aim of this work is to provide an overview of marginal variable screening methods for survival and develop recommendations for their use. For this purpose, a literature review is given, offering a comprehensive and structured introduction to the topic. In addition, a novel screening procedure based on distance correlation and martingale residuals is proposed, which is particularly useful in detecting nonmonotone associations. For evaluating the performance of the discussed approaches, a simulation study is conducted, comparing the true positive rates of competing variable screening methods in different settings. A real data example on mantle cell lymphoma is provided.
当在非常高的维度中进行生存分析时，通常需要使用初步筛选来减少协变量的数量。在过去的几年中，已经开发出了大量用于生存分析的变量筛选方法。然而，在实践中选择适当方法的指导却缺失了。这项工作的目的是提供生存分析中边缘变量筛选方法的概述，并为其使用提供建议。为此，进行了文献综述，为该主题提供了全面而结构化的介绍。此外，还提出了一种基于距离相关系数和鞅残差的新筛选程序，该程序特别有助于检测非单调关联。为了评估所讨论方法的性能，进行了模拟研究，比较了不同设置下竞争变量筛选方法的真实阳性率。提供了一个关于套细胞淋巴瘤的真实数据示例。
