Zhang Boan, Bilder Christopher R, Tebbs Joshua M
Department of Statistics, University of Nebraska-Lincoln, Lincoln, NE 68583, USA.
Biom J. 2013 Mar;55(2):173-89. doi: 10.1002/bimj.201200168. Epub 2013 Feb 8.
Group testing is frequently used to reduce the costs of screening a large number of individuals for infectious diseases or other binary characteristics in small prevalence situations. In many applications, the goals include both identifying individuals as positive or negative and estimating the probability of positivity. The identification aspect leads to additional tests being performed, known as "retests", beyond those performed for initial groups of individuals. In this paper, we investigate how regression models can be fit to estimate the probability of positivity while also incorporating the extra information from these retests. We present simulation evidence showing that significant gains in efficiency occur by incorporating retesting information, and we further examine which testing protocols are the most efficient to use. Our investigations also demonstrate that some group testing protocols can actually lead to more efficient estimates than individual testing when diagnostic tests are imperfect. The proposed methods are applied retrospectively to chlamydia screening data from the Infertility Prevention Project. We demonstrate that significant cost savings could occur through the use of particular group testing protocols.
分组检测常用于在低流行情况下降低对大量个体进行传染病或其他二元特征筛查的成本。在许多应用中，目标既包括将个体识别为阳性或阴性，也包括估计阳性概率。识别方面导致除了对初始个体组进行的检测之外，还需要进行额外的检测，即“复检”。在本文中，我们研究如何拟合回归模型来估计阳性概率，同时纳入这些复检的额外信息。我们给出的模拟证据表明，纳入复检信息可显著提高效率，并且我们进一步研究了哪种检测方案使用起来最有效。我们的研究还表明，当诊断测试不完善时，一些分组检测方案实际上可能比个体检测产生更有效的估计。所提出的方法被追溯应用于不育预防项目的衣原体筛查数据。我们证明，通过使用特定的分组检测方案可以显著节省成本。
