Distance covariance is a powerful new dependence measure that was recently introduced by Székely et al. and Székely and Rizzo. In this work, the concept of distance covariance is extended to measuring dependence between a covariate vector and a right-censored survival endpoint by establishing an estimator based on an inverse-probability-of-censoring weighted U-statistic. The consistency of the novel estimator is derived. In a large simulation study, it is shown that induced distance covariance permutation tests show a good performance in detecting various complex associations. Applying the distance covariance permutation tests on a gene expression dataset from breast cancer patients outlines its potential for biostatistical practice.

中文翻译：

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右截尾生存数据的距离协方差的一致版本及其在假设检验中的应用

距离协方差是 Székely等人最近引入的一种强大的新依赖性度量。以及 Székely 和 Rizzo。在这项工作中，距离协方差的概念被扩展到测量协变量向量和右删失生存端点之间的依赖性，方法是建立一个基于审查逆概率加权 U 统计量的估计量。导出新估计量的一致性。在大型模拟研究中，结果表明诱导距离协方差置换检验在检测各种复杂关联方面表现出良好的性能。对来自乳腺癌患者的基因表达数据集应用距离协方差置换检验概述了其在生物统计学实践中的潜力。