Multivariate two-sample permutation tests for trials with multiple time-to-event outcomes

Clinical trials involving multiple time-to-event outcomes are increasingly common. In this paper, permutation tests for testing for group differences in multivariate time-to-event data are proposed. Unlike other two-sample tests for multivariate survival data, the proposed tests attain the nominal type I error rate. A simulation study shows that the proposed tests outperform their competitors when the degree of censored observations is sufficiently high. When the degree of censoring is low, it is seen that naive tests such as Hotelling's T-2 outperform tests tailored to survival data. Computational and practical aspects of the proposed tests are discussed, and their use is illustrated by analyses of three publicly available datasets. Implementations of the proposed tests are available in an accompanying R package.