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存在相关性。在此情形下，重新随机化检验是一种直接且具有吸引力的方法，能够提供有效的统计推断。本文研究了重新随机化检验中的重复次数问题。这一研究受临床试验中的成组序贯设计启发，在此类设计中，中期分析时的名义显著性界值可能非常小。相应地，重新随机化检验所需的重复次数极大，可能在计算上难以实现。为减少重复次数，我们提出了一种自适应程序，并在预定义标准下将其与多种方法进行比较。通过蒙特卡洛模拟，我们展示了不同方法在有限样本量下的表现。我们还提出了减少总计算时间的策略，并为在中期分析数据揭盲前后进行准备、执行和报告提供了实践指导，从而确保计算能在合理时间范围内完成。