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Advances in statistical methodology for the evaluation of diagnostic and laboratory tests

机译：诊断和实验室测试评估的统计方法的进展

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AbstractThe ROC plot is a useful tool in the evaluation of the performance of medical tests for separating two populations. For a two‐state decision rule based on such a test, the ROC plot is the graph of all observed (1‐specificity, sensitivity) pairs. Each point on this empirical plot can be represented by a 2 × 2 contingency table. The non‐parametric statistics of Mann‐Whitney and Kolmogorov‐Smirnov can be immediately identified on this plot. Local non‐parametric confidence interval procedures related to the theoretical ROC curve are briefly reviewed. For continuous data, two new simultaneous confidence regions associated with the ROC curve are presented, one based on Kolmogorov‐Smirnov confidence bands for distribution functions and the other based on bootstrapping.Two different tests on the same patients can be compared on the ROC scale. For continuous data, one important problem concerns the comparison of two ROC plots (as would arise from two correlated diagnostic tests on each patient) using a sup norm (this metric can detect differences that the ROC area cannot). The distribution of a statistic based on this norm is studied, using the bootstrap. A biomedical example illustrates the
机译：摘要ROC图是评估医学检测分离两个人群性能的有用工具。对于基于此类检验的双态决策规则，ROC 图是所有观察到的（1 特异性、灵敏度）对的图形。此经验图上的每个点都可以用 2 × 2 列联表表示。Mann-Whitney 和 Kolmogorov-Smirnov 的非参数统计量可以在该图上立即识别。本文简要综述了与理论ROC曲线相关的局部非参数置信区间程序。对于连续数据，给出了两个与ROC曲线相关的新的同时置信区域，一个基于分布函数的Kolmogorov-Smirnov置信带，另一个基于自举。可以在 ROC 量表上比较对同一患者的两种不同测试。对于连续数据，一个重要问题涉及使用 sup 范数比较两个 ROC 图（如对每个患者进行两次相关诊断测试所产生的那样）（该指标可以检测到 ROC 区域无法检测到的差异）。使用引导程序研究了基于此范数的统计量分布。一个生物医学的例子说明了

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