Department of Biostatistics and Medical Informatics, University College of Medical Sciences, Delhi, India.
Indian Pediatr. 2011 Apr;48(4):277-87. doi: 10.1007/s13312-011-0055-4.
Sensitivity and specificity are two components that measure the inherent validity of a diagnostic test for dichotomous outcomes against a gold standard. Receiver operating characteristic (ROC) curve is the plot that depicts the trade-off between the sensitivity and (1-specificity) across a series of cut-off points when the diagnostic test is continuous or on ordinal scale (minimum 5 categories). This is an effective method for assessing the performance of a diagnostic test. The aim of this article is to provide basic conceptual framework and interpretation of ROC analysis to help medical researchers to use it effectively. ROC curve and its important components like area under the curve, sensitivity at specified specificity and vice versa, and partial area under the curve are discussed. Various other issues such as choice between parametric and non-parametric methods, biases that affect the performance of a diagnostic test, sample size for estimating the sensitivity, specificity, and area under ROC curve, and details of commonly used softwares in ROC analysis are also presented.
敏感度和特异性是衡量二项分类结局诊断试验相对于金标准固有有效性的两个组成部分。受试者工作特征（ROC）曲线是描绘连续或有序尺度（至少 5 个类别）诊断试验在一系列截止值时，敏感度与（1-特异性）之间权衡的图。这是评估诊断试验性能的有效方法。本文的目的是提供 ROC 分析的基本概念框架和解释，以帮助医学研究人员有效地使用它。讨论了 ROC 曲线及其重要组成部分，如曲线下面积、在特定特异性下的敏感度以及反之亦然，以及部分曲线下面积。还介绍了其他各种问题，例如参数和非参数方法之间的选择、影响诊断试验性能的偏差、用于估计 ROC 曲线下敏感度、特异性和面积的样本量，以及 ROC 分析中常用软件的详细信息。
