AI 中文总结
本研究提出适配群组测试设计的因果推断框架，将逆概率加权整合入合并伪似然以消除选择偏差，可准确估计平均处理效应，其效用经模拟与CDC真实数据验证。
AI 中文摘要
群组测试是一种成熟且极具成本效益的人群层面疾病监测策略，通过合并生物标本识别阳性个体。该方法最初于二战期间被提出用于大规模筛查，在现代高通量公共卫生基础设施中得到广泛应用，但传统群组测试方法仅限于纯关联分析，因此当个体层面数据存在基线混杂时，它们缺乏推断干预直接因果效应的能力。在本研究中，我们通过引入专门针对群组测试设计的因果推断框架，弥合了这一根本差距。我们将逆概率加权（IPW）原则直接整合到合并伪似然公式中，以构建无偏伪得分函数。在标准正则条件下，我们证明了所提出的插件估计量的一致性和渐近正态性。大量数值模拟表明，我们的框架成功消除了严重的选择偏差，准确恢复了真实平均处理效应，而传统未加权合并模型则无法做到这一点。最后，我们利用美国疾病控制与预防中心（CDC）的美国流感疫苗有效性网络的真实世界观察性监测数据，说明了我们方法作为估计工具和诊断工具的实际效用。
英文摘要
Group testing is an established, highly cost-effective strategy for population-level disease surveillance that identifies positive individuals by pooling biological specimens. Originally introduced during World War II for large-scale screening and heavily utilized in modern high-throughput public health infrastructure, traditional group testing methods are restricted to purely associational analyses. Consequently, they lack the capacity to infer the direct causal effect of an intervention when individual-level data are subject to baseline confounding. In this work, we bridge this fundamental gap by introducing a causal inference framework tailored specifically for group testing designs. We integrate the principles of inverse probability weighting (IPW) directly into a pooled pseudo-likelihood formulation to construct an unbiased pseudo-score function. Under standard regularity conditions, we prove the consistency and asymptotic normality of our proposed plug-in estimator. Extensive numerical simulations demonstrate that our framework successfully purges severe selection bias, accurately recovering the true average treatment effect where traditional unweighted pooling models fail. Finally, we illustrate the practical utility of our method as both an estimation and a diagnostic tool using real-world observational surveillance data from the CDC's U.S. Influenza Vaccine Effectiveness Network.
Comments21 pages, 3 figures, 3 tables. Includes supplementary appendix. To be submitted to Biometrics
