Gallagher E J
Department of Emergency Medicine, Albert Einstein College of Medicine, Bronx, NY 10467, USA.
Ann Emerg Med. 1998 Mar;31(3):391-7. doi: 10.1016/s0196-0644(98)70352-x.
Test-performance characteristics can be derived from a simple 2x2 table displaying the dichotomous relationship between a positive or negative test result and the presence or absence of a target disorder. Sensitivity and specificity, including a summary display of their reciprocal relationship as a receiver operating characteristics curve, are relatively stable test characteristics. Unfortunately, they represent an inversion of customary clinical logic and fail to tell us precisely what we wish to know. Predictive values, on the other hand, provide us with the requisite information but-because they are vulnerable to variation in disease prevalence-are numerically unstable. Likelihood ratios (LRs), in contrast, combine the stability of sensitivity and specificity to provide an omnibus index of test performance far more useful than its constituent parts. Application of Bayes' theorem to LRs produces the following summary equation: Clinically estimated pretest odds of disease x LR=Posttest odds of disease. This simple equation illustrates a concordance between the mathematical properties of likelihood ratios and the central strategy underlying diagnostic testing: the revision of disease probability.
检验性能特征可从一个简单的2×2列联表得出，该表展示了阳性或阴性检验结果与目标疾病存在与否之间的二分关系。敏感性和特异性，包括将它们的倒数关系汇总显示为一条受试者工作特征曲线，是相对稳定的检验特征。不幸的是，它们代表了对惯常临床逻辑的一种倒置，并且未能确切地告诉我们我们想要知道的内容。另一方面，预测值为我们提供了所需信息，但由于它们易受疾病患病率变化的影响，在数值上不稳定。相比之下，似然比（LRs）结合了敏感性和特异性的稳定性，以提供一个比其组成部分有用得多的检验性能综合指标。将贝叶斯定理应用于似然比可得出以下总结方程：临床估计的疾病验前概率×似然比 = 疾病验后概率。这个简单的方程说明了似然比的数学特性与诊断检验的核心策略（即疾病概率的修正）之间的一致性。
