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A consistent version of distance covariance for right‐censored survival data and its application in hypothesis testing
Author(s) -
Edelmann Dominic,
Welchowski Thomas,
Benner Axel
Publication year - 2022
Publication title -
biometrics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 2.298
H-Index - 130
eISSN - 1541-0420
pISSN - 0006-341X
DOI - 10.1111/biom.13470
Subject(s) - covariance , statistics , estimator , censoring (clinical trials) , mathematics , statistic , covariate , consistency (knowledge bases) , analysis of covariance , estimation of covariance matrices , econometrics , discrete mathematics
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.