Department of Statistics, Ludwig-Maximilians-University Munich; Institute of Medical Biometry and Epidemiology, University Medical Center Hamburg-Eppendorf.
Dtsch Arztebl Int. 2021 Aug 23;118(33-34):555-560. doi: 10.3238/arztebl.m2021.0224.
The accurate diagnosis of a disease is a prerequisite for its appropriate treatment. How well a medical test is able to correctly identify or rule out a target disease can be assessed by diagnostic accuracy studies.
The main statistical parameters that are derived from diagnostic accuracy studies, and their proper interpretation, will be presented here in the light of publications retrieved by a selective literature search, supplemented by the authors' own experience. Aspects of study planning and the analysis of complex studies on diagnostic tests will also be discussed.
In the usual case, the findings of a diagnostic accuracy study are presented in a 2 × 2 contingency table containing the number of true-positive, true-negative, false-positive, and true-positive test results. This information allows the calculation of various statistical parameters, of which the most important are the two pairs sensitivity/ specificity and positive/negative predictive value. All of these parameters are quotients, with the number of true positive (resp. true negative) test results in the numerator; the denominator is, in the first pair, the total number of ill (resp. healthy) patients, and in the second pair, the total number of patients with a positive (resp. negative) test. The predictive values are the parameters of greatest interest to phy - sicians and patients, but their main disadvantage is that they can easily be misinterpreted. We will also present the receiver operating characteristic (ROC) curve and the area under the curve (AUC) as additional important measures for the assessment of diagnostic tests. Further topics are discussed in the supplementary materials.
The statistical parameters used to assess diagnostic tests are primarily based on 2 × 2 contingency tables. These parameters must be interpreted with care in order to draw correct conclusions for use in medical practice.
准确的疾病诊断是恰当治疗的前提。医学检验能够正确识别或排除目标疾病的能力，可以通过诊断准确性研究来评估。
本文将根据选择性文献检索中检索到的出版物，并结合作者自己的经验，介绍诊断准确性研究中得出的主要统计参数及其正确解释。还将讨论研究计划和复杂诊断测试分析方面的问题。
通常情况下，诊断准确性研究的结果以包含真阳性、真阴性、假阳性和真阴性检测结果数量的 2×2 列联表呈现。该信息允许计算各种统计参数，其中最重要的是两对敏感性/特异性和阳性/阴性预测值。所有这些参数都是商，分子为真阳性（或真阴性）检测结果的数量；在第一对中，分母是患病（或健康）患者的总数，在第二对中，分母是阳性（或阴性）检测患者的总数。预测值是医生和患者最感兴趣的参数，但它们的主要缺点是容易被误解。我们还将介绍接收者操作特征（ROC）曲线和曲线下面积（AUC），作为评估诊断测试的额外重要指标。其他主题将在补充材料中讨论。
用于评估诊断测试的统计参数主要基于 2×2 列联表。为了得出正确的结论并将其应用于医学实践，必须谨慎解释这些参数。
