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000168370 1001_ $$0P:(DE-He78)92820b4867c955a04f642707ecf35b40$$aEdelmann, Dominic$$b0$$eFirst author$$udkfz
000168370 245__ $$aA consistent version of distance covariance for right-censored survival data and its application in hypothesis testing.
000168370 260__ $$aMalden, Mass. [u.a.]$$bWiley-Blackwell$$c2022
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000168370 500__ $$a#EA:C060#LA:C060# / 2022 Sep;78(3):867-879 
000168370 520__ $$aDistance covariance is a powerful new dependence measure that was recently introduced by Székely et al. (2007) and Székely and Rizzo (2009). In this work, the concept of distance covariance is extended to measuring dependence between a covariate vector and a right-censored survival endpoint by establishing an estimator based on an inverse-probability-of-censoring weighted U-statistic. The consistency of the novel estimator is derived. In a large simulation study, it is shown that induced distance covariance permutation tests show a good performance in detecting various complex associations. Applying the distance covariance permutation tests on a gene expression dataset from breast cancer patients outlines its potential for biostatistical practice. This article is protected by copyright. All rights reserved.
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000168370 650_7 $$2Other$$adistance correlation
000168370 650_7 $$2Other$$adistance covariance
000168370 650_7 $$2Other$$ahypothesis testing
000168370 650_7 $$2Other$$anonlinear
000168370 650_7 $$2Other$$asurvival analysis
000168370 7001_ $$aWelchowski, Thomas$$b1
000168370 7001_ $$0P:(DE-He78)e15dfa1260625c69d6690a197392a994$$aBenner, Axel$$b2$$eLast author$$udkfz
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