Vida S
Department of Psychiatry, McGill University, Montreal, Quebec, Canada.
Comput Methods Programs Biomed. 1993 Jun;40(2):95-101. doi: 10.1016/0169-2607(93)90004-5.
Sensitivity and specificity are key measures of the performance of a given test in detecting a given disorder. For tests yielding numerical scores, sensitivity and specificity usually vary inversely over the range of theoretically possible cutoff scores, complicating the task of quantifying and comparing the diagnostic accuracy of tests. Receiver Operating Characteristic analysis (ROC) approaches this problem by plotting the curve of sensitivity versus 1-specificity for all possible cutoff scores of the test. The area under the ROC curve (AUC) can be used to describe the diagnostic accuracy of the test. Parametric and non-parametric methods exist that allow the calculation of the AUC and the comparison of tests. A disadvantage of parametric formulations is the assumption of a normal or Gaussian distribution of test scores. The present article presents a computer program that utilizes non-parametric formulations that do not require the normal distribution of test scores. The program calculates the sensitivity and specificity of a test at all possible cutoff scores, plots the ROC curve, calculates the AUC, its standard error and 95% confidence limits, and allows the comparison of tests on independent and correlated samples.
敏感性和特异性是给定检测方法在检测特定疾病时性能的关键指标。对于产生数值分数的检测方法，敏感性和特异性通常在理论上可能的临界分数范围内呈反比变化，这使得量化和比较检测方法的诊断准确性变得复杂。受试者操作特征分析（ROC）通过绘制该检测方法所有可能临界分数下的敏感性与1-特异性曲线来解决这个问题。ROC曲线下的面积（AUC）可用于描述检测方法的诊断准确性。存在参数化和非参数化方法可用于计算AUC以及比较不同检测方法。参数化公式的一个缺点是假设检测分数呈正态或高斯分布。本文介绍了一个计算机程序，该程序使用不需要检测分数呈正态分布的非参数化公式。该程序计算检测方法在所有可能临界分数下的敏感性和特异性，绘制ROC曲线，计算AUC、其标准误差和95%置信区间，并允许对独立样本和相关样本的检测方法进行比较。
