在疾病类别树形排序下的后测诊断准确性度量。
Post-test diagnostic accuracy measures under tree ordering of disease classes.
发表日期：2023 Sep 18
作者：
Hani Samawi, Marwan Alsharman, Mario Keko, Jing Kersey
来源：
STATISTICS IN MEDICINE
摘要：
医学领域通常使用后验检测措施，如预测值和似然比，来评估诊断准确性。预测值包括阳性和阴性预测值（PPV 和 NPV），表示个体根据检测结果患有目标健康状况的概率。而似然比包括阳性和阴性比率（分别为 LR+ 和 LR-），比较了疾病组和非疾病组之间特定检测结果的概率。虽然预测值在评估不同疾病患病率的人群中的诊断测试准确性方面很有用，似然比则在特定患者中提供了先验概率和后验概率之间的直接联系。在本研究中，我们介绍并分析了一种新的方法，即基于疾病分类的广义预测值和似然比，并通过模拟研究评估了这些方法的有效性，并结合肺癌的实际数据进行了说明。 ©2023 John Wiley & Sons Ltd.
The medical field commonly employs post-test measures such as predictive values and likelihood ratios to assess diagnostic accuracy. Predictive values, including positive and negative values (PPV and NPV), indicate the probability that individuals have a target health condition based on test results. On the other hand, likelihood ratios, including positive and negative ratios (LR+ and LR- respectively), compare the probability of a particular test result between the diseased and non-diseased groups. While predictive values are useful in evaluating diagnostic test accuracy in populations with varying disease prevalence, likelihood ratios provide a direct link between pre-test and post-test probabilities in specific patients. In this study, we introduce and analyze a new approach called generalized predictive values and likelihood ratios, using a tree ordering of disease classes. We evaluate the effectiveness of these methods through simulation studies and illustrate their use with real data on lung cancer.© 2023 John Wiley & Sons Ltd.