Recent observations, especially in cancer immunotherapy clinical trials with time-to-event outcomes, show that the commonly used proportial hazard assumption is often not justifiable, hampering an appropriate analyse 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. As pointed out recently by Horiguchi and Uno (2020), methods for the RMST based on asymptotic theory suffer from inflated type-I error under small sample sizes. To overcome this problem, they suggested a permutation strategy leading to more convincing results in simulations. However, their proposal requires an exchangeable data set-up between comparison groups which may be limiting in practice. In addition, it is not possible to invert their testing procedure 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 the corresponding permutation-based confidence intervals. In our extensive simulation study, we demonstrate the advantage of our new method, especially in situations with relative small sample sizes and unbalanced groups. Finally we illustrate the application of the proposed method by re-analysing data from a recent lung cancer clinical trial.

翻译：最近的一些观察,特别是在具有时间到活动结果的癌症免疫疗法临床试验中,表明通常使用的预测危险假设往往不合理,妨碍了按危险比率对数据进行适当分析。一种有吸引力的替代办法是由有限的平均存活时间(RMST)提出的,这种平均存活时间并不依赖任何模型假设,而且总是可以直觉地解释。正如Horiguchi和Uno(202020年)最近指出的,基于无症状理论的RMST方法在抽样大小小的情况下,因过度的一型错误而受到影响。为了克服这一问题,他们建议了一种通勤战略,导致在模拟中得出更令人信服的结果。然而,它们的建议要求在实际中可能限制的比较小组之间建立一个可交换的数据组。此外,不可能改变其测试程序,以获得有效的信任间隔,这可以提供更深入的信息。在本文件中,我们提出一个学生化的消化测试以及相应的间歇性信任间隔,以解决这些限制。我们的广泛模拟研究显示我们新方法的优势,特别是在相对小的临床试验中,我们最后通过比较的临床试验方法展示了最近的一种癌症的试验组。