Building Intuition Regarding the Statistical Behavior of Mass Medical Testing Programs
Building Intuition Regarding the Statistical Behavior of Mass Medical Testing Programs
复制标题
建立对大众医学检测项目统计行为的直觉
DOI：
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发表时间：
2021
期刊：
影响因子：
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通讯作者：
Taal Levi
中科院分区：
文献类型：
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作者：
L. Waller;Taal Levi
医疗决策和公共卫生规划的质量直接取决于对医学检测准确性的理解，特别是在大流行期间。但是，用于评估测试准确性的统计概念和措施可能会令人困惑。为什么没有一个单一的测试准确性的明确措施？如果检测结果为阴性，个人应该有多担心COVID-19的传播？敏感性、特异性、假阳性结果、假阴性结果和阳性预测值之间的相对关系是什么？在本教程中，我们通过直观的方式通过可视化插图澄清这些术语的含义，并解释这些术语如何通过贝叶斯定理相互连接。我们展示了如何使用该定理中的关系来评估大量人接受测试时的个人风险。我们还举例说明了大量测试的准确性在多大程度上取决于受试者中患有这种疾病的比例。总的来说，我们的目标是提高关于大规模医学检测活动的性能的一般直觉。在这里，为了达到这个目的，我们回顾了不同的方法来衡量诊断测试的准确性，参考特定的流行病的例子。
The quality of medical decision-making and public health planning alike depends directly upon understanding the accuracy of medical tests, especially during a pandemic. But the statistical concepts and measures used to assess test accuracy can be confusing. Why is there not one single definitive measure of test accuracy? How much should individuals worry about spreading COVID-19 if their test results are negative? What do sensitivity, specificity, false positive results, false negative results, and positive predictive value mean relative to each other? In this tutorial, we clarify the meaning of these terms in intuitive ways via visual illustrations, and explain how these terms are all connected to one another through Bayes’ theorem. We show how to use the relationships in that theorem to assess personal risk when large numbers of people are being tested. We illustrate as well the extent to which the accuracy of large numbers of tests depends on the proportion of those tested who have the disease. Overall, we aim to heighten a general intuition regarding the performance of mass medical testing campaigns. Here, toward that end, we review different ways to measure the accuracy of diagnostic tests with reference to pandemic-specific examples.