A very classical problem in statistics is to test the stochastic superiority of one distribution to another. However, many existing approaches are developed for independent samples and, moreover, do not take censored data into account. We develop a new estimand-driven method to compare the effectiveness of two treatments in the context of right-censored survival data with matched pairs. With the help of competing risks techniques, the so-called relative treatment effect is estimated. It quantifies the probability that the individual undergoing the first treatment survives the matched individual undergoing the second treatment. Hypothesis tests and confidence intervals are based on a studentized version of the estimator, where resampling-based inference is established by means of a randomization method. In a simulation study, we found that the developed test exhibits good power, when compared to competitors which are actually testing the simpler null hypothesis of the equality of both marginal survival functions. Finally, we apply the methodology to a well-known benchmark data set from a trial with patients suffering from with diabetic retinopathy.

翻译：统计学中的一个经典问题是检验一个分布在随机优势上是否优于另一个分布。然而，现有方法大多针对独立样本开发，且未考虑删失数据。我们提出一种新的基于估计目标的方法，用于在带匹配对的右删失生存数据情境下比较两种治疗的有效性。借助竞争风险技术，我们估计了所谓的相对治疗效果。该效应量化了接受第一种治疗的个体比匹配的接受第二种治疗的个体存活更久的概率。假设检验和置信区间基于该估计量的学生化版本，其中通过随机化方法建立重抽样推断。在模拟研究中，我们发现所开发的方法与实际上检验更简单零假设（即两个边缘生存函数相等）的竞争方法相比，具有较好的检验功效。最后，我们将该方法应用于一项糖尿病视网膜病变患者试验中的著名基准数据集。