论文标题
SROC曲线AUC的置信区间和一些相关方法，使用引导程序进行诊断精度研究的荟萃分析
Confidence interval for the AUC of SROC curve and some related methods using bootstrap for meta-analysis of diagnostic accuracy studies
论文作者
论文摘要
摘要接收器工作特性（SROC）曲线的曲线下的面积（AUC）是诊断测试准确性研究（DTA）荟萃分析的主要统计结果。但是，在大多数DTA荟萃分析中尚未报告其置信区间，因为没有提供某些方法和统计包。在本文中，我们提供了用于计算AUC置信区间的自举算法。此外，使用Bootstrap框架，我们可以进行自举测试，以评估AUC在多个诊断测试中的差异的重要性。此外，我们通过一对一的研究分析提供了一种基于AUC的影响诊断方法。我们介绍了使用两个DTA Met-Analyses进行宫颈癌和哮喘的诊断测试的说明性示例。我们还为这些计算开发了一个易于处理的R软件包dmetatools。这些方法提供的各种定量证据无疑支持DTA荟萃分析的统计证据的解释和精确评估。
The area under the curve (AUC) of summary receiver operating characteristic (SROC) curve is a primary statistical outcome for meta-analysis of diagnostic test accuracy studies (DTA). However, its confidence interval has not been reported in most of DTA meta-analyses, because no certain methods and statistical packages have been provided. In this article, we provide a bootstrap algorithm for computing the confidence interval of the AUC. Also, using the bootstrap framework, we can conduct a bootstrap test for assessing significance of the difference of AUCs for multiple diagnostic tests. In addition, we provide an influence diagnostic method based on the AUC by leave-one-study-out analyses. We present illustrative examples using two DTA met-analyses for diagnostic tests of cervical cancer and asthma. We also developed an easy-to-handle R package dmetatools for these computations. The various quantitative evidence provided by these methods certainly supports the interpretations and precise evaluations of statistical evidence of DTA meta-analyses.
