Department of Statistics, University of South Carolina, South Carolina.
Department of Bioinformatics and Biostatistics, University of Louisville, Kentucky.
Stat Med. 2019 Oct 15;38(23):4519-4533. doi: 10.1002/sim.8311. Epub 2019 Jul 11.
Group testing, as a cost-effective strategy, has been widely used to perform large-scale screening for rare infections. Recently, the use of multiplex assays has transformed the goal of group testing from detecting a single disease to diagnosing multiple infections simultaneously. Existing research on multiple-infection group testing data either exclude individual covariate information or ignore possible retests on suspicious individuals. To incorporate both, we propose a new regression model. This new model allows us to perform a regression analysis for each infection using multiple-infection group testing data. Furthermore, we introduce an efficient variable selection method to reveal truly relevant risk factors for each disease. Our methodology also allows for the estimation of the assay sensitivity and specificity when they are unknown. We examine the finite sample performance of our method through extensive simulation studies and apply it to a chlamydia and gonorrhea screening data set to illustrate its practical usefulness.
成组检测作为一种具有成本效益的策略，已被广泛用于大规模筛查罕见感染。最近，多重检测方法的使用将成组检测的目标从检测单一疾病转变为同时诊断多种感染。现有的多感染成组检测数据研究要么排除个体协变量信息，要么忽略对可疑个体的可能重复检测。为了同时包含这些信息，我们提出了一种新的回归模型。这个新模型允许我们使用多感染成组检测数据对每种感染进行回归分析。此外，我们引入了一种有效的变量选择方法来揭示每种疾病真正相关的风险因素。我们的方法还允许在未知检测灵敏度和特异性时进行估计。我们通过广泛的模拟研究来检验我们方法的有限样本性能，并将其应用于衣原体和淋病筛查数据集，以说明其实用性。
