A consistent version of distance covariance for right‐censored survival data and its application in hypothesis testing

Dominic Edelmann, Thomas Welchowski and Axel Benner

Biometrics, 2022, vol. 78, issue 3, 867-879

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.

Date: 2022
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