Group (pooled) testing is becoming a popular strategy for screening large populations for infectious diseases. This popularity is owed to the cost savings that can be realized through implementing group testing methods. These methods involve physically combining biomaterial (eg, saliva, blood, urine) collected on individuals into pooled specimens which are tested for an infection of interest. Through testing these pooled specimens, group testing methods reduce the cost of diagnosing all individuals under study by reducing the number of tests performed. Even though group testing offers substantial cost reductions, some practitioners are hesitant to adopt group testing methods due to the so-called dilution effect. The dilution effect describes the phenomenon in which biomaterial from negative individuals dilute the contributions from positive individuals to such a degree that a pool is incorrectly classified. Ignoring the dilution effect can reduce classification accuracy and lead to bias in parameter estimates and inaccurate inference. To circumvent these issues, we propose a Bayesian regression methodology which directly acknowledges the dilution effect while accommodating data that arises from any group testing protocol. As a part of our estimation strategy, we are able to identify pool specific optimal classification thresholds which are aimed at maximizing the classification accuracy of the group testing protocol being implemented. These two features working in concert effectively alleviate the primary concerns raised by practitioners regarding group testing. The performance of our methodology is illustrated via an extensive simulation study and by being applied to Hepatitis B data collected on Irish prisoners.

中文翻译：

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在组测试回归中捕获池稀释效应：贝叶斯方法

群体（合并）检测正在成为筛查大量人群传染病的流行策略。这种流行归功于通过实施小组测试方法可以实现的成本节约。这些方法涉及将在个体身上收集的生物材料（例如，唾液、血液、尿液）物理组合成混合样本，以测试感兴趣的感染。通过测试这些合并的标本，组测试方法通过减少执行的测试次数来降低诊断所有研究对象的成本。尽管团体测试可以显着降低成本，但由于所谓的稀释效应，一些从业者对采用团体测试方法犹豫不决. 稀释效应描述了这样一种现象，在这种现象中，来自负面个体的生物材料将正面个体的贡献稀释到一定程度，以至于池被错误分类。忽略稀释效应会降低分类准确性并导致参数估计偏差和不准确的推断。为了规避这些问题，我们提出了一种贝叶斯回归方法，该方法直接承认稀释效应，同时容纳来自任何组测试协议的数据。作为我们估计策略的一部分，我们能够确定池特定的最佳分类阈值，这些阈值旨在最大限度地提高正在实施的组测试协议的分类准确性。这两个功能的协同工作有效地缓解了从业者对组测试提出的主要担忧。我们的方法的性能通过广泛的模拟研究和应用于从爱尔兰囚犯收集的乙型肝炎数据来说明。