Spearman-like correlation measure adjusting for covariates in bivariate survival data.
Spearman-like correlation measure adjusting for covariates in bivariate survival data.
复制标题
类斯皮尔曼相关性测量调整双变量生存数据中的协变量。
DOI：
10.1002/bimj.202200137
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发表时间：
2023
期刊：
影响因子：
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通讯作者：
Shepherd,BryanE
中科院分区：
文献类型：
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作者：
Eden,SvetlanaK;Li,Chun;Shepherd,BryanE
我们提出了对经审查的连续和离散数据的 Spearman 相关性的扩展，以允许协变量调整。先前提出的非参数和半参数 Spearman 相关估计量需要二元生存曲面的非参数估计或有关依赖结构的参数假设。实际上，双变量生存表面的非参数估计很困难，并且可能无法满足有关相关结构的参数假设。因此，我们提出了一种不需要任何东西并且仅使用边际生存分布的方法。我们的方法估计概率尺度残差的相关性，已被证明在没有审查时等于斯皮尔曼相关性。由于该方法仅依赖于边际分布，因此它的变量往往比之前建议的非参数估计量要小，并且置信区间很容易构建。尽管在审查下，它对斯皮尔曼相关性有偏差，正如我们的模拟所示，但它在适度审查下表现良好，均方误差比非参数方法更小。我们还将其扩展到部分（调整后）、条件和部分条件相关性，这使得它与实际应用特别相关。我们应用我们的方法来估计拉丁美洲多地点艾滋病毒感染者队列中病毒衰竭时间与治疗方案改变时间之间的相关性。
We propose an extension of Spearman's correlation for censored continuous and discrete data that permits covariate adjustment. Previously proposed nonparametric and semiparametric Spearman's correlation estimators require either nonparametric estimation of the bivariate survival surface or parametric assumptions about the dependence structure. In practice, nonparametric estimation of the bivariate survival surface is difficult, and parametric assumptions about the correlation structure may not be satisfied. Therefore, we propose a method that requires neither and uses only the marginal survival distributions. Our method estimates the correlation of probability‐scale residuals, which has been shown to equal Spearman's correlation when there is no censoring. Because this method relies only on marginal distributions, it tends to be less variable than the previously suggested nonparametric estimators, and the confidence intervals are easily constructed. Although under censoring, it is biased for Spearman's correlation as our simulations show, it performs well under moderate censoring, with a smaller mean squared error than nonparametric approaches. We also extend it to partial (adjusted), conditional, and partial‐conditional correlation, which makes it particularly relevant for practical applications. We apply our method to estimate the correlation between time to viral failure and time to regimen change in a multisite cohort of persons living with HIV in Latin America.