近期观察特别是癌症免疫治疗临床试验关于时间事件结果的表现表明，常用的比例风险假设不经常成立，这妨碍了通过风险比对这些数据进行适当分析。一个可行的吸引人的替代方案是由受限均值生存时间（RMST）提供的，它不依赖于任何模型假设并且始终可以直观地解释。由于基于渐近理论的RMST的方法在小样本量下会出现错误，最近提出了一种排列检验，可以在模拟中提供更令人信服的结果。然而，经典排列策略需要比较组之间的可交换数据设置，这在实践中可能有限。此外，无法倒转相关测试程序以获得有效的置信区间，这可以提供更深入的信息。本文通过提出学生化排列检验以及相应的基于排列的置信区间来解决这些限制。在广泛的模拟研究中，我们展示了我们的新方法的优点，特别是在相对较小的样本量和不平衡的组中。最后，我们通过重新分析最近的肺癌临床试验数据来说明所提出的方法的应用。©2023年作者。由John Wiley＆Sons Ltd.出版的《统计医学》发表。

Recent observations, especially in cancer immunotherapy clinical trials with time-to-event outcomes, show that the commonly used proportional hazard assumption is often not justifiable, hampering an appropriate analysis of the data by hazard ratios. An attractive alternative advocated is given by the restricted mean survival time (RMST), which does not rely on any model assumption and can always be interpreted intuitively. Since methods for the RMST based on asymptotic theory suffer from inflated type-I error under small sample sizes, a permutation test was proposed recently leading to more convincing results in simulations. However, classical permutation strategies require an exchangeable data setup between comparison groups which may be limiting in practice. Besides, it is not possible to invert related testing procedures to obtain valid confidence intervals, which can provide more in-depth information. In this paper, we address these limitations by proposing a studentized permutation test as well as respective permutation-based confidence intervals. In an extensive simulation study, we demonstrate the advantage of our new method, especially in situations with relatively small sample sizes and unbalanced groups. Finally, we illustrate the application of the proposed method by re-analyzing data from a recent lung cancer clinical trial.© 2023 The Authors. Statistics in Medicine published by John Wiley & Sons Ltd.