Causal Inference in Infectious Disease Prevention Studies

传染病预防研究中的因果推断

• 批准号: 9195685
• 负责人: Michael G Hudgens
• 金额: $ 37.84万
• 依托单位: UNIV OF NORTH CAROLINA CHAPEL HILL
• 依托单位国家: 美国
• 财政年份: 2009
• 资助国家: 美国
• 起止时间: 2009-12-01 至 2019-12-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/9195685
• 关键词: Affect; Area; Beds; Cholera Vaccine; Communicable Diseases; Communities; Confidence Intervals; Cox Models; Data; Development; Disease; Education; Event; Failure; Grant; Health; Health Benefit; Health Policy; Herd Immunity; Individual; Infection; Intervention; Lead; Malaria; Measures; Methods; Modeling; Modernization; Nomenclature; Observational Study; Outcome; Pathway Analysis; Performance; Pneumococcal vaccine; Policies; Politics; Population; Probability; Property; Public Health; Randomized; Research; Rotavirus Vaccines; Sampling; Science; Statistical Methods; Structure; Testing; Time; Typhoid Vaccine; Vaccinated; Vaccination; Vaccines; base; cost; disorder control; disorder prevention; improved; influenza virus vaccine; innovation; insight; interest; intervention effect; prevent; public health relevance; research study; simulation; social; theories; treatment effect; user friendly software; vaccine trial

 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.

 描述（由应用程序提供）：这项研究的总体目的是开发统计方法，以量化干预措施以防止传染病的影响。这项研究的主要激励例子是对疫苗的研究，尽管开发的方法将是一般的，并且在其他环境中立即应用。疫苗研究期间的一个特别重要的挑战问题，疫苗的间接作用。对于当个人接种疫苗时，昂贵或无法完全保护疾病的疫苗，评估间接作用（或HERD免疫组织化学）在有关疫苗引入和利用方面的政策考虑方面很重要。如果不考虑牛群免疫组织化学可能会导致关于疫苗公共卫生益处的不正确结论。借鉴群群免疫组织化学的推断是非标准化的，因为间接效应衡量了疫苗接种一个人对另一个人的健康结果的影响。在因果推论的命名法中，这被称为“干扰”。也就是说，据说干涉是一个人的治疗（例如，疫苗接种）会影响另一个人的结果。在这种赠款中，将开发创新的统计方法，以推断个人之间可能干扰治疗或暴露的影响。将开发基于AIM 1的随机化（即精确）统计方法。在AIM 2中，将开发出对观察性研究的加权，双重稳健和分层的诺言分数治疗效果估计量。 AIM 3将重点关注有关治疗对事件时间结果的效果的推论，但要受到正确的审查。在AIM 4治疗效应界限和灵敏度分析方法中，将在各种无法完全识别因果效应的假设下开发。对于目标1-4，将假定可以将个人分割为组，以使不同群体中的个体之间没有干扰。 如果各组在分别，临时和/或社会上进行了足够的分离，则此假设将是合理的。在AIM中，将开发5种方法，以进行任意干扰形式 假设种群可以分为单独的干扰组。对于所有提出的研究，将严格确定所提出方法的理论特性。将进行广泛的仿真研究，以评估在现实环境中提出的方法的性能。已开发的方法将用于分析来自几种大型传染病预防研究的数据，从而提供有关不同的见解 霍乱，影响，肺炎球菌，轮状病毒和伤寒疫苗和疟疾床网的影响。根据干预措施，避免感染或疾病病例的预期数量或疾病病例将对结果进行直接解释。开发的统计方法将适用于可能存在干扰的许多其他环境，包括经济学，教育，网络分析，政治学和空间分析。