Bilder Christopher R, Tebbs Joshua M, McMahan Christopher S
Department of Statistics, University of Nebraska-Lincoln, Lincoln, Nebraska 68583.
Department of Statistics, University of South Carolina, Columbia, South Carolina 29208.
Biometrics. 2019 Mar;75(1):278-288. doi: 10.1111/biom.12988. Epub 2019 Mar 28.
Infectious disease testing frequently takes advantage of two tools-group testing and multiplex assays-to make testing timely and cost effective. Until the work of Tebbs et al. (2013) and Hou et al. (2017), there was no research available to understand how best to apply these tools simultaneously. This recent work focused on applications where each individual is considered to be identical in terms of the probability of disease. However, risk-factor information, such as past behavior and presence of symptoms, is very often available on each individual to allow one to estimate individual-specific probabilities. The purpose of our paper is to propose the first group testing algorithms for multiplex assays that take advantage of individual risk-factor information as expressed by these probabilities. We show that our methods significantly reduce the number of tests required while preserving accuracy. Throughout this paper, we focus on applying our methods with the Aptima Combo 2 Assay that is used worldwide for chlamydia and gonorrhea screening.
传染病检测经常利用两种工具——分组检测和多重检测——来使检测既及时又具成本效益。在特布斯等人（2013年）和侯等人（2017年）开展相关工作之前，尚无研究可用于了解如何最佳地同时应用这些工具。这项近期的工作聚焦于每个个体在患病概率方面被视为相同的应用场景。然而，诸如过去行为和症状表现等风险因素信息通常可在每个个体上获取，从而使人们能够估计个体特定的概率。我们论文的目的是提出首个用于多重检测的分组检测算法，该算法利用这些概率所表达的个体风险因素信息。我们表明，我们的方法在保持准确性的同时显著减少了所需的检测次数。在整篇论文中，我们专注于将我们的方法应用于全球范围内用于衣原体和淋病筛查的Aptima Combo 2检测。
