群体检测准备好进入疾病识别的黄金时期了吗？
Is Group Testing Ready for Prime-time in Disease Identification?
摘要：
大规模疾病筛查是一个复杂的过程，必须在高成本与紧迫的公共卫生需求之间取得平衡。当目标是筛查传染病时，一种方法是分组检测，其中样本最初在池中进行检测，只有当最初的池检测结果为阳性时，才重新检测单个样本。直觉上，如果感染的流行率很小，这可能会导致所需检测总数的大幅减少。尽管如此，在医学研究中使用组测试一直是有限的，主要是由于怀疑的影响，合并的准确性，一个给定的测定。虽然有大量的研究解决了组检验研究中的检验错误问题，但通常假设误分类参数是从外部人群中已知的和/或值不随组大小而变化。这两个假设是非常值得怀疑的许多医生考虑在他们的研究设计组测试。在这篇文章中，我们将探讨这些假设的失败可能会影响组测试设计的有效性，因此，组测试目前是否可行的医疗筛查。具体来说，我们看看如何在设计阶段的灵敏度函数的不正确的假设可能会导致一个程序的整体灵敏度和预期的测试次数估计不佳。此外，如果使用验证研究来估计给定检测的合并误分类参数，我们表明所需的样本量如此之大，以至于除了最大的筛选程序之外，在所有筛选程序中都令人望而却步
英文摘要：
Large scale disease screening is a complicated process in which high costs must be balanced against pressing public health needs. When the goal is screening for infectious disease, one approach is group testing in which samples are initially tested in pools and individual samples are retested only if the initial pooled test was positive. Intuitively, if the prevalence of infection is small, this could result in a large reduction of the total number of tests required. Despite this, the use of group testing in medical studies has been limited, largely due to skepticism about the impact of pooling on the accuracy of a given assay. While there is a large body of research addressing the issue of testing errors in group testing studies, it is customary to assume that the misclassification parameters are known from an external population and/or that the values do not change with the group size. Both of these assumptions are highly questionable for many medical practitioners considering group testing in their study design. In this article, we explore how the failure of these assumptions might impact the efficacy of a group testing design and, consequently, whether group testing is currently feasible for medical screening. Specifically, we look at how incorrect assumptions about the sensitivity function at the design stage can lead to poor estimation of a procedure's overall sensitivity and expected number of tests. Furthermore, if a validation study is used to estimate the pooled misclassification parameters of a given assay, we show that the sample sizes required are so large as to be prohibitive in all but the largest screening programs
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
