分层干扰下观察性研究的G-Formula，及其在疟疾床网使用中的应用
G-Formula for Observational Studies under Stratified Interference, with Application to Bed Net Use on Malaria
AI总结：
本文扩展了参数化g-formula以适应分层干扰下的大聚类观察性研究，提出总体效应、治疗效应和未治疗效应的估计量，避免了g-null悖论，并通过模拟和刚果疟疾床网数据验证了其有效性。
摘要：
评估疫苗和传染病预防措施的人群层面效应对于公共卫生领域非常重要。在传染病研究中，一个人的治疗可能会影响另一个人的结果，即单元之间可能存在干扰。例如，一个人使用床网预防疟疾可能对居住在附近的其他个体产生间接影响。在某些场景下，个体可能形成群组或聚类，其中干扰仅发生在群组内部，即存在部分干扰。逆概率加权估计量此前已被开发用于具有部分干扰的观察性研究。然而，这些估计量不适用于具有大聚类的研究。因此，本文扩展了参数化g-formula以允许部分干扰。针对总体效应、接受治疗时的效应以及未接受治疗时的效应，提出了g-formula估计量。所提出的估计量能够适应大聚类，并且不会出现在无干扰情况下可能出现的g-null悖论。在假设无未测量混杂因素且部分干扰采取特定形式（称为“弱分层干扰”）的条件下，推导了所提出估计量的大样本性质。模拟研究表明了所提出估计量的有限样本性能。使用所提出的g-formula估计量分析了刚果民主共和国的人口与健康调查，以评估床网使用对疟疾的效应
英文摘要：
Assessing population-level effects of vaccines and other infectious disease prevention measures is important to the field of public health. In infectious disease studies, one person's treatment may affect another individual's outcome, i.e., there may be interference between units. For example, the use of bed nets to prevent malaria by one individual may have an indirect effect on other individuals living in close proximity. In some settings, individuals may form groups or clusters where interference only occurs within groups, i.e., there is partial interference. Inverse probability weighted estimators have previously been developed for observational studies with partial interference. Unfortunately, these estimators are not well suited for studies with large clusters. Therefore, in this paper, the parametric g-formula is extended to allow for partial interference. G-formula estimators are proposed for overall effects, effects when treated, and effects when untreated. The proposed estimators can accommodate large clusters and do not suffer from the g-null paradox that may occur in the absence of interference. The large sample properties of the proposed estimators are derived assuming no unmeasured confounders and that the partial interference takes a particular form (referred to as `weak stratified interference'). Simulation studies are presented demonstrating the finite-sample performance of the proposed estimators. The Demographic and Health Survey from the Democratic Republic of the Congo is then analyzed using the proposed g-formula estimators to assess the effects of bed net use on malaria.
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