A frequently addressed issue in clinical trials is the comparison of censored paired survival outcomes, for example, when individuals were matched based on their characteristics prior to the analysis. In this regard, a proper incorporation of the dependence structure of the paired censored outcomes is required and, up to now, appropriate methods are only rarely available in the literature. Moreover, existing methods are not motivated by the strive for insights by means of an easy‐to‐interpret parameter. Hence, we seek to 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. This estimand describes the probability that individuals under Treatment 1 have a longer lifetime than comparable individuals under Treatment 2. We derive hypothesis tests and confidence intervals 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 demonstrate for numerous sample sizes and different amounts of censoring that the developed test exhibits a good power. Finally, we apply the methodology to a well‐known benchmark data set from a trial with patients suffering from diabetic retinopathy.

中文翻译：

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用于直接比较审查配对生存结果的非参数相对治疗效果

临床试验中经常讨论的一个问题是审查配对生存结果的比较，例如，在分析之前根据个人特征进行匹配。在这方面，需要适当纳入配对审查结果的依赖结构，并且到目前为止，文献中很少有适当的方法。此外，现有的方法并不是通过易于解释的参数来寻求见解。因此，我们寻求开发一种新的估计驱动方法，以在具有匹配对的右删失生存数据的背景下比较两种治疗方法的有效性。借助竞争风险技术，可以估计所谓的相对治疗效果。该估计值描述了治疗 1 下的个体比治疗 2 下的同类个体寿命更长的概率。我们基于学生化版本的估计量得出假设检验和置信区间，其中通过随机化方法建立基于重采样的推断。在模拟研究中，我们证明了对于大量样本大小和不同数量的审查，所开发的测试表现出良好的功效。最后，我们将该方法应用于来自糖尿病视网膜病变患者试验的众所周知的基准数据集。