基于群组检测的爱荷华州居民衣原体感染年龄变化因素探索
A group testing based exploration of age-varying factors in chlamydia infections among Iowa residents
AI总结：
本文提出一种贝叶斯变系数群组检测回归模型，结合随机搜索变量选择和随机效应，揭示爱荷华州居民衣原体感染风险中随年龄变化的种族差异，为精准干预提供依据。
摘要：
群组检测是一种以合并样本而非个体方式筛查受试者的方法，已被用作爱荷华州居民衣原体筛查的一种成本效益较高的策略。为加深对爱荷华州衣原体流行病学的理解，已有若干群组检测回归模型被提出。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随年龄变化的关联。总体而言，我们的模型在贝叶斯框架内运行，允许回归关联随一个关键协变量而变化。我们采用随机搜索变量选择过程在估计中进行正则化。此外，我们的模型能够整合随机效应，以考虑潜在的地理因素并估计未知的检测准确率概率。通过全面的模拟研究评估了模型性能。在应用于爱荷华州群组检测数据集后，我们揭示了衣原体感染中显著的随年龄变化的种族差异。我们相信这一发现有望为干预措施和预防策略的改进提供依据，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。
英文摘要：
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.
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