Incorporating the dilution effect in group testing regression
Incorporating the dilution effect in group testing regression
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DOI：
10.1002/sim.8916
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发表时间：
2021-02-17
影响因子：
2
通讯作者：
Bilder, Christopher R.
中科院分区：
文献类型：
--
作者：
Mokalled, Stefani C.;McMahan, Christopher S.;Bilder, Christopher R.
在筛查传染病时，群体检测已被证明是比个体检测更经济有效的替代方法。通过测试单个样本池（例如，血液、尿液、唾液等）而不是单独测试样本，可以实现成本节约。然而，在群体测试中出现的一个常见问题是所谓的“稀释效应”。如果来自阳性个体标本的信号在与多个阴性标本混合时被稀释超过检测阈值，则会发生这种情况。在本文中，我们提出了一种新的统计框架，用于合并估计和病例识别，这在文献中通常是分开处理的。我们的方法考虑从汇集的样本中分析连续的生物标志物水平（例如，抗体水平，抗原浓度等），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，我们展示了如何使用生物标志物分布的估计值来逐个池地选择诊断阈值。我们的建议通过数值研究进行评估，并使用在爱尔兰监狱人口中收集的乙型肝炎病毒数据进行说明。
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.