In the presence of prognostic covariates, inference about the treatment effect with time-to-event endpoints is mostly conducted via the stratified log-rank test or the score test based on the Cox proportional hazards model. In their ground-breaking work Ye and Shao (2020) have demonstrated theoretically that when the model is misspecified, the robust score test (Wei and Lin, 1989) as well as the unstratified log-rank test are conservative in trials with stratified randomization. This fact, however, was not established for the Pocock and Simon covariate-adaptive allocation other than through simulations. In this paper, we expand the results of Ye and Shao to a more general class of randomization procedures and show, in part theoretically, in part through simulations, that the Pocock and Simon covariate-adaptive allocation belongs to this class. We also advance the search for the correlation structure of the normalized within-stratum imbalances with minimization by describing the asymptotic correlation matrix for the case of equal prevalence of all strata. We expand the robust tests proposed by Ye and Shao for stratified randomization to minimization and examine their performance trough simulations.
翻译:在预测性共变中,对时间到活动端点的处理效果的推断大多通过分级日志测试或基于考克斯比例危害模型的分数测试进行。Ye和Shao(202020年)在其开创性工作中从理论上表明,当模型被错误地指定时,稳健的分数测试(Wei和Lin,1989年)以及未批准的日数测试在分级随机化试验中是保守的。然而,这个事实不是为波科克和Simon Covoliate-适应性分配而确定的,而是通过模拟。在本文件中,我们将叶和Shao的结果扩大到更普遍的随机化程序类别,并在理论上部分通过模拟表明,波科和Simon的正变式适应性分配属于这一类。我们还通过描述所有层均匀化的零散化案例的零度对应关系矩阵来推进对中区内均分失衡相关结构的搜索。我们扩大了Ye和Sharo提出的稳健的测试范围,以便随机化。