School of Mathematical and Statistical Sciences, Clemson University, Clemson, South Carolina, USA.
Department of Statistics, University of South Carolina, Columbia, South Carolina, USA.
Stat Med. 2021 May 20;40(11):2540-2555. doi: 10.1002/sim.8916. Epub 2021 Feb 17.
When screening for infectious diseases, group testing has proven to be a cost efficient alternative to individual level testing. Cost savings are realized by testing pools of individual specimens (eg, blood, urine, saliva, and so on) rather than by testing the specimens separately. However, a common concern that arises in group testing is the so-called "dilution effect." This occurs if the signal from a positive individual's specimen is diluted past an assay's threshold of detection when it is pooled with multiple negative specimens. In this article, we propose a new statistical framework for group testing data that merges estimation and case identification, which are often treated separately in the literature. Our approach considers analyzing continuous biomarker levels (eg, antibody levels, antigen concentrations, and so on) from pooled samples to estimate both a binary regression model for the probability of disease and the biomarker distributions for cases and controls. To increase case identification accuracy, we then show how estimates of the biomarker distributions can be used to select diagnostic thresholds on a pool-by-pool basis. Our proposals are evaluated through numerical studies and are illustrated using hepatitis B virus data collected on a prison population in Ireland.
在传染病筛查中，相较于个体水平检测，群体检测已被证实是一种具有成本效益的替代方法。通过对个体样本（如血液、尿液、唾液等）进行混合检测而非单独检测，可实现成本节约。然而，在群体检测中会出现一个常见的问题，即所谓的“稀释效应”。如果阳性个体的样本信号在与多个阴性样本混合时低于检测阈值，就会发生这种情况。在本文中，我们提出了一种新的群体检测数据分析的统计框架，它将估计和病例识别合并在一起，而在文献中这两个通常是分开处理的。我们的方法考虑了对混合样本的连续生物标志物水平（如抗体水平、抗原浓度等）进行分析，以估计疾病概率的二元回归模型以及病例和对照的生物标志物分布。为了提高病例识别的准确性，我们展示了如何基于每个样本池估计生物标志物分布，以便逐个选择诊断阈值。我们的建议通过数值研究进行评估，并通过在爱尔兰监狱人群中收集的乙型肝炎病毒数据进行说明。
