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Properties of the Projected Length of the Curve (PLC) and Area Swept out by the Curve (ASC) Indices for the Receiver Operating Characteristic (SROC) Curve

机译:接收器工作特性(SROC)曲线的曲线预计长度(PLC)和曲线所覆盖的面积(ASC)指标的特性

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Several measures have been proposed to summarize the Receiver Operating Characteristic (ROC) curve, including the Projected Length of the Curve (PLC) and the Area Swept out by the Curve (ASC). These indices were first proposed by Lee (Epidemiology 1996; 7:605-611) to avoid certain deficiencies of the traditional Area Under the Curve (AUC) summary measure. More recently meta-analysis methods for assessing diagnostic test accuracy have been developed and the Summary Receiver Operating Characteristic (SROC) curve has been recommended to represent the performance of a diagnostic test. Some properties of the SROC curve were discussed by Walter (Statist. Med. 2002; 21:1237-1256). Here we extend that work to focus on properties of PLC and ASC in the context of SROC curve. Mathematical expressions for these two indices and their variances are derived in terms of the overall diagnostic odds ratio and the magnitude of inter-study heterogeneity in the odds ratio. Expressions for PLC and ASC and their variances are easily computed in homogeneous studies, and their values provide good approximations to the corresponding values for heterogeneous studies in most practical situations. General variances of PLC and ASC are derived by using delta methods, and are found to be smaller if the odds ratio is large. The methods are illustrated using data from two studies, the first being a meta-analysis on the detection of metastases in cervical cancer patients, and the second being a single study of HPV infection and pre-invasive cervical lesions.
机译:已经提出了几种措施来总结接收器工作特性(ROC)曲线,包括曲线的预计长度(PLC)和曲线扫过的面积(ASC)。这些索引最初由Lee(Epidemiology 1996; 7:605-611)提出,以避免传统曲线下面积(AUC)汇总度量的某些不足。最近已经开发了用于评估诊断测试准确性的荟萃分析方法,并且建议使用汇总接收器工作特性(SROC)曲线来代表诊断测试的性能。 Walter(Statist。Med。2002; 21:1237-1256)讨论了SROC曲线的一些特性。在这里,我们将工作扩展为在SROC曲线的背景下关注PLC和ASC的属性。这两个指数及其方差的数学表达式是根据整体诊断比值比和研究间异质性比值比得出的。在同类研究中,PLC和ASC的表达式及其方差很容易计算,在大多数实际情况下,它们的值可以很好地近似于异构研究的相应值。 PLC和ASC的一般方差是使用delta方法得出的,如果优势比较大,则发现方差较小。使用两项研究的数据说明了这些方法,第一项是对子宫颈癌患者转移检测的荟萃分析,第二项是对HPV感染和宫颈浸润前病变的单项研究。

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