We study rank-based approaches to estimate the correlation between two right-censored variables. With end-of-study censoring, it is often impossible to nonparametrically identify the complete bivariate survival distribution, and therefore it is impossible to nonparametrically compute Spearman's rank correlation. As a solution, we propose two measures that can be nonparametrically estimated. The first measure is Spearman's correlation in a restricted region. The second measure is Spearman's correlation for an altered but estimable joint distribution. We describe population parameters for these measures and illustrate how they are similar to and different from the overall Spearman's correlation. We propose consistent estimators of these measures and study their performance through simulations. We illustrate our methods with a study assessing the correlation between the time to viral failure and the time to regimen change among persons living with HIV in Latin America who start antiretroviral therapy.

中文翻译：

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Spearman 等级相关性与双变量生存数据的非参数估计


我们研究基于排名的方法来估计两个右删失变量之间的相关性。通过研究结束审查，通常不可能非参数地识别完整的二元生存分布，因此不可能非参数地计算 Spearman 的等级相关性。作为解决方案，我们提出了两种可以非参数估计的度量。第一个度量是限制区域内的 Spearman 相关性。第二个度量是 Spearman 相关性，用于改变但可估计的联合分布。我们描述了这些度量的总体参数，并说明了它们与总体斯皮尔曼相关性的相似和不同之处。我们提出了这些措施的一致估计器，并通过模拟研究它们的性能。我们通过一项研究来说明我们的方法，该研究评估了开始抗逆转录病毒治疗的拉丁美洲艾滋病毒感染者的病毒衰竭时间与治疗方案改变时间之间的相关性。