A consistent version of distance covariance for right-censored survival data and its application in hypothesis testing.
distance correlation
distance covariance
hypothesis testing
nonlinear
survival analysis
Journal
Biometrics
ISSN: 1541-0420
Titre abrégé: Biometrics
Pays: United States
ID NLM: 0370625
Informations de publication
Date de publication:
09 2022
09 2022
Historique:
revised:
24
12
2020
received:
11
03
2020
accepted:
31
03
2021
pubmed:
14
4
2021
medline:
5
10
2022
entrez:
13
4
2021
Statut:
ppublish
Résumé
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.
Types de publication
Journal Article
Research Support, Non-U.S. Gov't
Langues
eng
Sous-ensembles de citation
IM
Pagination
867-879Informations de copyright
© 2021 The Authors. Biometrics published by Wiley Periodicals LLC on behalf of International Biometric Society.
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