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, use of bed nets to prevent malaria by one individual may have an indirect or spillover effect to 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 of overall effects, spillover effects when treated, and spillover 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, and 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 overall and spillover effects of bed net use on malaria.
翻译：在传染病研究中,一个人的治疗可能影响到另一个人的结果,也就是说,单位之间可能发生干扰。例如,一个人使用蚊帐来预防疟疾可能会对居住在附近的其他个人产生间接或外溢效应。在有些环境中,个人可能形成群体或群体,只有群体内才发生干扰,即有部分干扰。以前曾为观察研究开发过反概率加权估计值,但部分干扰。不幸的是,这些估计值不适于进行大型群集的研究。因此,在本文件中,参数表表表表扩展以允许部分干扰。G表估量器提出了总体影响、治疗后的外溢效应和未治疗时的外溢效应。提议的估量器可以容纳大型群集,而不会因无干扰情况下可能出现的颗粒悖论而受到影响。拟议的估量器的大型样本特性是用来推断出大规模干扰的。拟议的测测量器的大型样本特性并不适合于大型群群的研究。因此,因此,在本文件中,参数表单表表表表将允许部分干扰。G-surullas 提议对刚果民主共和国进行模拟性评估,然后使用定数调查结果。
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