Causal Inference in Infectious Disease Prevention Studies

摘要 这项研究的总体目标是开发用于量化干预效果的统计方法 预防传染病。这项研究的主要激励例子是疫苗研究，尽管 所开发的方法将是通用的，并立即在其他环境中应用。一个特别重要的问题 疫苗研究中的挑战性问题需要评估疫苗接种的间接(溢出)效应。对于疫苗 当个人接种疫苗时，费用昂贵或负担不起完全预防疾病的费用，间接评估 在疫苗引进和使用的政策考虑中，影响(或群体免疫)是重要的。未能做到 对群体免疫的解释可能会导致关于疫苗对公共卫生益处的错误结论。绘图 关于群体免疫的推断是不标准的，因为间接影响衡量的是接种疫苗的效果 个人对另一个人的健康结果的看法。在因果推理的术语中，这被称为 “干扰。”也就是说，如果一个人的治疗(例如，接种疫苗)影响了 另一个人的结果。在这笔赠款中，将开发创新的统计方法来进行绘图推理 当个体之间可能存在干扰时，关于治疗或暴露的影响。对于每一个 该项目的目标，所提议的统计方法的理论性质将被确立。模拟法 将进行研究，以评估所建议的方法在广泛的现实情况下的性能 设置。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影响提供了新的见解，以及 疟疾蚊帐。由此得出的推论将对预期数量有直接的解释 由于干预措施而避免的感染或疾病病例。用户界面友好的软件，实施拟议的 将开发方法并免费提供。开发的统计方法和软件将是 适用于可能存在干扰的许多其他环境，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.