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
翻译:大规模疾病筛查是一个复杂的过程,其成本高,必须与紧迫的公共卫生需求相平衡。当目标是对传染病进行筛查时,一种方法是群体测试,首先在集合体中对样本进行集体测试,只有在初始集合检测结果为阳性时才对单个样本进行重新测试。自然,如果感染流行率小,这可能导致所需的检测总数大幅减少。尽管如此,在医学研究中使用群体测试是有限的,这主要是因为对集合检测对特定检测结果的准确性的影响持怀疑态度。尽管在群体测试研究中存在大量研究,处理测试错误的问题,但通常的做法是假设错误分类参数是外部人口所知道的,而且/或者数值不会随着群体规模的变化而改变。这些假设对许多医学从业人员在研究设计中考虑群体测试的总数非常有疑问。尽管如此,我们在医学研究中使用这些假设的失败会如何影响群体测试设计的有效性,因此,群体测试目前是否可行。具体地说,如果在设计阶段的敏感度功能方面有多么错误的假设,那么在设计阶段,这些数值不会随群体规模的变化而改变。这两种假设对于总体测试的预测是,那么,在设计阶段里,我们所使用的最高级的检验是用来进行最差的检验。