In covariate-adaptive or response-adaptive randomization, the treatment assignment and outcome can be correlated. Under this situation, re-randomization tests are a straightforward and attractive method to provide valid statistical inference. In this paper, we investigate the number of repetitions in the re-randomization tests. This is motivated by the group sequential design in clinical trials, where the nominal significance bound can be very small at an interim analysis. Accordingly, re-randomization tests lead to a very large number of required repetitions, which may be computationally intractable. To reduce the number of repetitions, we propose an adaptive procedure and compare it with multiple approaches under pre-defined criteria. Monte Carlo simulations are conducted to show the performance of different approaches in a limited sample size. We also suggest strategies to reduce total computation time and provide practical guidance in preparing, executing and reporting before and after data are unblinded at an interim analysis, so one can complete the computation within a reasonable time frame.

翻译：在协变量自适应或响应自适应随机化中，治疗分配与结果可能相关。在此情况下，重随机化检验是一种直接且具有吸引力的方法，可提供有效的统计推断。本文研究了重随机化检验中的重复次数问题。这一研究源于临床试验中的成组序贯设计，其名义显著性界值在中期分析中可能非常小。相应地，重随机化检验导致所需重复次数极大，可能带来计算上的困难。为减少重复次数，我们提出一种自适应程序，并在预设准则下将其与多种方法进行比较。通过蒙特卡洛模拟展示了有限样本量下不同方法的性能。我们还提出了缩短总计算时间的策略，并为数据揭盲前后在中期分析中的准备、执行及报告提供实用指导，以确保计算能在合理时间范围内完成。