Nonparametric Two‐Sample Tests of the Marginal Mark Distribution with Censored Marks
针对临床研究中因删失导致标记数据不完整的问题，提出了两类两样本非参数检验方法，用于比较标记分布是否相同，模拟显示新方法在重尾分布下功效显著提升。
Occasionally, investigators collect auxiliary marks at the time of failure in a clinical study. Because the failure event may be censored at the end of the follow-up period, these marked endpoints are subject to induced censoring. We propose two new families of two-sample tests for the null hypothesis of no difference in mark-scale distribution that allows for arbitrary associations between mark and time. One family of proposed tests is a nonparametric extension of an existing semi-parametric linear test of the same null hypothesis while a second family of tests is based on novel marked rank processes. Simulation studies indicate that the proposed tests have the desired size and possess adequate statistical power to reject the null hypothesis under a simple change of location in the marginal mark distribution. When the marginal mark distribution has heavy tails, the proposed rank-based tests can be nearly twice as powerful as linear tests.