gms | German Medical Science

Background: Distance covariance is a powerful new dependence measure that allows to detect arbitrary dependencies between random vectors. However, a consistent version of distance covariance for right-censored survival data is missing.

Methods: 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.

Results: 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.

Conclusion: Both simulation results and a real data example demonstrate that distance covariance appears to be a very promising approach for testing associations in time-to-event data, outperforming existing methods in numerous settings.

The authors declare that they have no competing interests.

The authors declare that an ethics committee vote is not required.