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Assessing population-level effects of vaccines and other infectious diseaseprevention measures is important to the field of public health. In infectiousdisease studies, one person's treatment may affect another individual'soutcome, i.e., there may be interference between units. For example, use of bednets to prevent malaria by one individual may have an indirect or spillovereffect to other individuals living in close proximity. In some settings,individuals may form groups or clusters where interference only occurs withingroups, i.e., there is partial interference. Inverse probability weightedestimators have previously been developed for observational studies withpartial interference. Unfortunately, these estimators are not well suited forstudies with large clusters. Therefore, in this paper, the parametric g-formulais extended to allow for partial interference. G-formula estimators areproposed of overall effects, spillover effects when treated, and spillovereffects when untreated. The proposed estimators can accommodate large clustersand do not suffer from the g-null paradox that may occur in the absence ofinterference. The large sample properties of the proposed estimators arederived, and simulation studies are presented demonstrating the finite-sampleperformance of the proposed estimators. The Demographic and Health Survey fromthe Democratic Republic of the Congo is then analyzed using the proposedg-formula estimators to assess the overall and spillover effects of bed net useon malaria.

G-Formula for Observational Studies with Partial Interference, with Application to Bed Net Use on Malaria