Causal Inference in Infectious Disease Prevention Studies

 描述（由申请人提供）：本研究的总体目标是开发量化干预措施预防传染病效果的统计方法。这项研究的主要动机是疫苗的研究，虽然开发的方法将是通用的，并在其他环境中立即应用。疫苗研究中一个特别重要和具有挑战性的问题是评估疫苗接种的间接影响。对于昂贵的疫苗或当个体接种疫苗时不能提供完全的疾病保护的疫苗，评估间接效应（或群体免疫力）对于疫苗引进和利用的政策考虑是重要的。不考虑群体免疫可能导致关于疫苗的公共卫生益处的错误结论。关于群体免疫的推论是不标准的，因为间接效应衡量的是一个人接种疫苗对另一个人健康结果的影响。在因果推理的术语中，这被称为“干扰”。“也就是说，干扰被认为存在于治疗中（例如，一个人的免疫力会影响另一个人的免疫力。在这项赠款中，将开发创新的统计方法，用于在个体之间可能存在干扰时推断治疗或暴露的影响。在目标1中，基于随机化（即，将制定统计方法。在目标2中，将为观察性研究开发逆概率加权、双重稳健和分层倾向评分治疗效应估计量。目标3将侧重于推断治疗对右删失至事件时间结局的影响。在目标4中，将在不能完全识别因果效应的各种假设下开发治疗效应界限和敏感性分析方法。就目标1 - 4而言，将假设个人可被划分为群体，从而不同组中的个人之间不存在干扰; 如果这些组在空间上、时间上和/或社会上充分分离，则该假设是合理的。在目标5中，将针对任意形式的干扰开发方法， 假设可以将群体划分为单独的干扰组。对于所有提出的研究，所提出的方法的理论属性将严格建立。将进行广泛的模拟研究，以评估所提出的方法在现实环境中的性能。开发的方法将用于分析几项大型传染病预防研究的数据，为不同的传染病预防提供新的见解。 霍乱、流感、肺炎球菌、轮状病毒和伤寒疫苗以及疟疾蚊帐的效果。由此产生的推论将有直接的解释方面的预期数量的感染或疾病的情况下避免由于干预。所开发的统计方法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。

 DESCRIPTION (provided by applicant): The overall objective of this research is to develop statistical methods for quantifying the effects of interventions to prevent infectious diseases. Th 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 effects of vaccination. For vaccines that are costly or do not afford complete protection from disease when an individual is vaccinated, evaluating the 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 i 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. In Aim 1 randomization-based (i.e., exact) statistical methods will be developed. In Aim 2 inverse probability weighted, doubly robust, and stratified propensity score treatment effect estimators will be developed for observational studies. Aim 3 will focus on inference about treatment effects on time-to-event outcomes subject to right censoring. In Aim 4 treatment effect bounds and sensitivity analysis methods will be developed under various sets of assumptions which do not fully identify the causal effects. For Aims 1 - 4 it will be assumed that individuals can be partitioned into groups such that there is no interference between individuals in different groups; this assumption will be reasonable if the groups are sufficiently separated spatially, temporally, and/or socially. In Aim 5 methods will be developed for arbitrary forms of interference that do not assume the population can be partitioned into separate interference groups. For all of the proposed research, the theoretical properties of the proposed methods will be rigorously established. Extensive simulation studies will be conducted to evaluate the performance of the proposed methods in 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 cholera, influenza, pneumococcal, rotavirus, and typhoid vaccines, 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. The statistical methods developed will be applicable to many other settings where interference may be present, including econometrics, education, network analysis, political science, and spatial analyses.