In screening applications involving low-prevalence diseases, pooling specimens (e.g., urine, blood, swabs, etc.) through group testing can be far more cost effective than testing specimens individually. Estimation is a common goal in such applications and typically involves modeling the probability of disease as a function of available covariates. In recent years, several authors have developed regression methods to accommodate the complex structure of group testing data but often under the assumption that covariate effects are linear. Although linearity is a reasonable assumption in some applications, it can lead to model misspecification and biased inference in others. To offer a more flexible framework, we propose a Bayesian generalized additive regression approach to model the individual-level probability of disease with potentially misclassified group testing data. Our approach can be used to analyze data arising from any group testing protocol with the goal of estimating multiple unknown smooth functions of covariates, standard linear effects for other covariates, and assay classification accuracy probabilities. We illustrate the methods in this article using group testing data on chlamydia infection in Iowa.

中文翻译：

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组测试数据的广义加性回归。

在涉及低患病率疾病的筛查应用中，通过群体测试汇集样本（例如，尿液、血液、拭子等）可能比单独测试样本更具成本效益。估计是此类应用中的一个共同目标，通常涉及将疾病概率建模为可用协变量的函数。近年来，一些作者开发了回归方法来适应组测试数据的复杂结构，但通常假设协变量效应是线性的。尽管线性在某些应用中是一个合理的假设，但在其他应用中它可能会导致模型指定错误和有偏差的推理。为了提供更灵活的框架，我们提出了一种贝叶斯广义加性回归方法，用可能错误分类的组测试数据对个体水平的疾病概率进行建模。我们的方法可用于分析来自任何组测试协议的数据，目的是估计协变量的多个未知平滑函数、其他协变量的标准线性效应以及测定分类准确率概率。我们使用爱荷华州衣原体感染的组测试数据来说明本文中的方法。