Receiver operating characteristic curves are widely used as a measure of accuracy of diagnostic tests and can be summarised using the area under the receiver operating characteristic curve (AUC). Often, it is useful to construct a confidence interval for the AUC; however, because there are a number of different proposed methods to measure variance of the AUC, there are thus many different resulting methods for constructing these intervals. In this article, we compare different methods of constructing Wald-type confidence interval in the presence of missing data where the missingness mechanism is ignorable. We find that constructing confidence intervals using multiple imputation based on logistic regression gives the most robust coverage probability and the choice of confidence interval method is less important. However, when missingness rate is less severe (e.g. less than 70%), we recommend using Newcombe's Wald method for constructing confidence intervals along with multiple imputation using predictive mean matching.

中文翻译：

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存在可忽略的缺失数据时接收器操作特征曲线下区域的置信区间

接收器操作特征曲线被广泛用作诊断测试准确性的度量，并且可以使用接收器操作特征曲线下的面积 (AUC) 进行总结。通常，为 AUC 构建置信区间很有用；然而，因为有许多不同的提议方法来测量 AUC 的方差，因此有许多不同的方法来构建这些区间。在本文中，我们比较了在存在缺失数据且缺失机制可忽略的情况下构建 Wald 型置信区间的不同方法。我们发现使用基于逻辑回归的多重插补构建置信区间给出了最稳健的覆盖概率，而置信区间方法的选择不太重要。然而，