Causal Inference in Infectious Disease Prevention Studies

总结 本研究的总体目标是开发量化干预效果的统计方法 来预防传染病。这项研究的主要动机是疫苗研究，尽管 所开发的方法将是通用的，并可立即应用于其他环境。一个特别重要的 疫苗研究中的一个具有挑战性的问题是评估疫苗接种的间接（溢出）效应。疫苗 成本高或在接种疫苗时不能提供完全的疾病保护， 在疫苗引进和利用的政策考虑中，群体免疫效应是重要的。未能 对群体免疫的解释可能导致关于疫苗的公共卫生益处的错误结论。绘图 关于群体免疫的推论是不标准的，因为间接效应是衡量接种疫苗的效果的 另一个人的健康结果。在因果推理的命名法中，这被称为 “干扰”也就是说，如果治疗（例如，疫苗接种）影响一个人 另一个人的结果。在这项资助中，创新的统计方法将被开发出来， 当个体之间可能存在干扰时，关于治疗或暴露的影响。中的每 该项目的目标，所提出的统计方法的理论属性将被建立。仿真 将进行研究，以评估所提出的方法在广泛的现实范围内的性能。 设置.所开发的方法将用于分析几个大型传染病预防的数据 研究，为霍乱、流感和其他病原体疫苗的不同效果提供了新的见解， 防疟疾蚊帐由此产生的推论将有直接的解释方面的预期数字 由于干预措施而避免的感染或疾病病例。用户友好的软件， 将制定方法并免费提供。开发的统计方法和软件将 适用于许多其他可能存在干扰的环境，包括计量经济学、教育、网络 分析，政治学和空间分析。

Summary The overall objective of this research is to develop statistical methods for quantifying the effects of interventions to prevent infectious diseases. The primary motivating examples for this research are studies of vaccines, although the developed methods will be general and have immediate application in other settings. One particularly significant and challenging problem in vaccine studies entails assessing indirect (spillover) effects of vaccination. For vaccines that are costly or do not afford complete protection from disease when an individual is vaccinated, evaluating indirect effects (or herd immunity) is important in policy considerations about vaccine introduction and utilization. Failure to account for herd immunity can lead to incorrect conclusions regarding the public health benefit of a vaccine. Drawing inference about herd immunity is non-standard because indirect effects measure the effect of vaccinating one individual on another individual's health outcome. In the nomenclature of causal inference, this is known as “interference.” That is, interference is said to be present if the treatment (e.g., vaccination) of one individual affects the outcome of another individual. In this grant innovative statistical methods will be developed for drawing inference about the effects of a treatment or exposure when there is possibly interference between individuals. For each of the project's aims, the theoretical properties of the proposed statistical methods will be established. Simulation studies will be conducted to evaluate the performance of the proposed methods over a wide range of realistic settings. The developed methods will be used to analyze data from several large infectious disease prevention studies, providing new insights into the different effects of vaccines for cholera, influenza, and other pathogens, and malaria bed nets. The resulting inferences will have straightforward interpretations in terms of the expected number of infections or cases of disease averted due to the intervention. User-friendly software implementing the proposed methods will be developed and made freely available. The statistical methods and software developed will be applicable to many other settings where interference may be present, including econometrics, education, network analysis, political science, and spatial analyses.