The probability of agreement has been used as an effective strategy for quantifying the similarity between the reliability of two populations. By contrast to hypothesis testing approaches based on P‐values, the probability of agreement provides a more realistic assessment of similarity by emphasizing practically important differences. In this article, we propose the use of the probability of agreement to evaluate the similarity of two Kaplan‐Meier curves, which estimate the survival functions in two populations. This article extends the probability of agreement paradigm to right censored data and explores three different methods of quantifying uncertainty in the probability of agreement estimate. The first approach provides a convenient assessment based on large‐sample normal‐theory (LSNT), while the other two approaches are nonparametric alternatives based on ordinary and fractional random‐weight bootstrap (FRWB) techniques. All methods are illustrated with examples for which comparing the survival curves of related populations is of interest and the efficacy of the methods are also evaluated through simulation studies. Based on these simulations we recommend point estimation using the proposed LSNT calculation and confidence interval estimation via the FRWB approach. We also provide a Shiny app that facilitates an automated implementation of the methodology.

中文翻译：

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将Kaplan-Meier曲线与一致概率进行比较。

一致性的概率已被用作量化两个总体可靠性之间相似性的有效策略。与基于P的假设检验方法相反-值，通过强调实际重要的差异，一致性的可能性提供了更现实的相似性评估。在本文中，我们建议使用一致性概率来评估两条Kaplan-Meier曲线的相似度，以估计两个群体的生存函数。本文将协议范式的可能性扩展到正确的检查数据，并探讨了量化协议估计概率不确定性的三种不同方法。第一种方法基于大样本正态理论（LSNT）提供了方便的评估，而其他两种方法是基于普通和分数随机权重自举（FRWB）技术的非参数替代方案。举例说明了所有方法，比较了相关种群的生存曲线，并通过模拟研究评估了方法的有效性。基于这些模拟，我们建议使用建议的LSNT计算进行点估计，并通过FRWB方法推荐置信区间估计。我们还提供了一个Shiny应用程序，可促进该方法的自动化实施。