Screening procedures for infectious diseases, such as HIV, often involve pooling individual specimens together and testing the pools. For diseases with low prevalence, group testing (or pooled testing) can be used to classify individuals as diseased or not while providing considerable cost savings when compared to testing specimens individually. The pooling literature is replete with group testing case identification algorithms including Dorfman testing, higher-stage hierarchical procedures, and array testing. Although these algorithms are usually evaluated on the basis of the expected number of tests and classification accuracy, most evaluations in the literature do not account for the continuous nature of the testing responses and thus invoke potentially restrictive assumptions to characterize an algorithm's performance. Commonly used case identification algorithms in group testing are considered and are evaluated by taking a different approach. Instead of treating testing responses as binary random variables (i.e., diseased/not), evaluations are made by exploiting an assay's underlying continuous biomarker distributions for positive and negative individuals. In doing so, a general framework to describe the operating characteristics of group testing case identification algorithms is provided when these distributions are known. The methodology is illustrated using two HIV testing examples taken from the pooling literature.

中文翻译：

---

使用生物标志物信息进行分组测试病例识别

传染病（例如 HIV）的筛查程序通常涉及将单个样本汇集在一起​​并进行测试。对于患病率较低的疾病，可以使用群体测试（或合并测试）将个体分类为患病与否，同时与单独测试样本相比可节省大量成本。汇集文献充满了组测试案例识别算法，包括 Dorfman 测试、更高阶段的分层程序和阵列测试。尽管这些算法通常根据预期的测试次数和分类准确度进行评估，但文献中的大多数评估并未考虑测试响应的连续性，因此调用了潜在的限制性假设来表征算法的性能。组测试中常用的案例识别算法被考虑并通过采用不同的方法进行评估。不是将测试响应视为二元随机变量（即，患病/未患病），而是通过利用检测的潜在连续生物标志物分布对阳性和阴性个体进行评估。在这样做时，当这些分布已知时，提供描述组测试用例识别算法的操作特性的通用框架。该方法使用来自汇集文献的两个 HIV 检测示例进行说明。s 阳性和阴性个体的潜在连续生物标志物分布。在这样做时，当这些分布已知时，提供描述组测试用例识别算法的操作特性的通用框架。该方法使用来自汇集文献的两个 HIV 检测示例进行说明。s 阳性和阴性个体的潜在连续生物标志物分布。在这样做时，当这些分布已知时，提供描述组测试用例识别算法的操作特性的通用框架。该方法使用来自汇集文献的两个 HIV 检测示例进行说明。