A consistent version of distance covariance for right‐censored survival data and its application in hypothesis testing. Issue 3 (6th May 2021)

• Record Type: Journal Article
• Authors: Edelmann, Dominic; Welchowski, Thomas; Benner, Axel
• Is Part Of: Biometrics. Volume 78:Issue 3(2022)
• Page Start: 867
• Page End: 879
• Publication Date: 2021-05-06
• Subjects: distance correlation -- distance covariance -- hypothesis testing -- nonlinear -- survival analysis; Biometry -- Periodicals; 570.15195
• DOI: 10.1111/biom.13470
• Languages: English
• ISSNs: 0006-341X

Abstract

Abstract: 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.