Group testing, a method that screens subjects in pooled samples rather than individually, has been employed as a cost-effective strategy for chlamydia screening among Iowa residents. In efforts to deepen our understanding of chlamydia epidemiology in Iowa, several group testing regression models have been proposed. Different than previous approaches, we expand upon the varying coefficient model to capture potential age-varying associations with chlamydia infection risk. In general, our model operates within a Bayesian framework, allowing regression associations to vary with a covariate of key interest. We employ a stochastic search variable selection process for regularization in estimation. Additionally, our model can integrate random effects to consider potential geographical factors and estimate unknown assay accuracy probabilities. The performance of our model is assessed through comprehensive simulation studies. Upon application to the Iowa group testing dataset, we reveal a significant age-varying racial disparity in chlamydia infections. We believe this discovery has the potential to inform the enhancement of interventions and prevention strategies, leading to more effective chlamydia control and management, thereby promoting health equity across all populations.

翻译：分组检测是一种通过混合样本而非个体筛查受试者的方法，已被用作爱荷华州居民衣原体筛查的经济有效策略。为深化对爱荷华州衣原体流行病学的理解，研究者已提出多种分组检测回归模型。与以往方法不同，我们扩展了变系数模型以捕捉衣原体感染风险中潜在的年龄变化关联。总体而言，我们的模型在贝叶斯框架下运行，允许回归关联随关键协变量变化。我们采用随机搜索变量选择过程进行估计正则化。此外，该模型可整合随机效应以考虑潜在地理因素并估计未知检测准确率概率。通过综合模拟研究评估了模型性能。在应用于爱荷华州分组检测数据集后，我们揭示了衣原体感染中存在显著的年龄变化种族差异。我们相信这一发现有望为改进干预措施和预防策略提供依据，从而实现更有效的衣原体控制与管理，进而促进所有人群的健康公平。