In the analysis of current life science datasets, we often encounter scenarios in which the application of asymptotic theory to hypothesis testing can be problematic. Besides improved asymptotic results, permutation/simulation-based tests are a general approach to address this issue. However, these randomized tests can impose a massive computational burden, for example, in scenarios in which large numbers of statistical tests are computed, and the specified significance level is very small. Stopping rules aim to assess significance with the smallest possible number of draws while controlling the probabilities of errors due to statistical uncertainty. In this communication, we derive a general stopping rule, QUICK-STOP, based on the sequential testing theory that is easy to implement, controls the error probabilities rigorously, and is nearly optimal in terms of expected draws. In a simulation study, we show that our approach outperforms current stopping approaches for general randomized tests by factor 10 and does not impose an additional computational burden. We illustrate our approach by applying our stopping rule to a single-variant analysis of a whole-genome sequencing study for lung function.

中文翻译：

---

一种灵活且几乎最佳的顺序测试方法来进行随机测试：QUICK-STOP。

在对当前生命科学数据集进行分析时，我们经常会遇到将渐进理论应用于假设检验可能会出现问题的情况。除了改善渐近结果外，基于置换/模拟的测试是解决此问题的通用方法。但是，例如在计算大量统计检验且指定的显着性水平非常小的情况下，这些随机检验会带来巨大的计算负担。止损规则旨在以尽可能少的平局次数评估重要性，同时控制由于统计不确定性而导致的错误概率。在此通讯中，我们基于易于实施的顺序测试理论导出了一个通用的停止规则QUICK-STOP，该规则严格控制错误概率，并且在预期抽奖方面几乎是最佳的。在模拟研究中，我们表明我们的方法比常规随机测试的当前停止方法性能高10倍，并且没有施加额外的计算负担。我们通过将终止规则应用于肺功能全基因组测序研究的单变量分析来说明我们的方法。