Causal Inference in Infectious Disease Prevention Studies

传染病预防研究中的因果推断

• 批准号: 10624327
• 负责人: Michael G Hudgens
• 金额: --
• 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 2025-05-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10624327
• 关键词: Acceleration; Affect; Beds; Cholera Vaccine; Communicable Diseases; Communities; Cox Models; Data; Disease; Education; Event; Failure; Grant; Health; Health Benefit; Health Policy; Herd Immunity; Individual; Infection; Influenza; Intervention; Knowledge; Malaria; Measures; Methods; Modeling; Nomenclature; Observational Study; Outcome; Pathway Analysis; Performance; Pneumococcal vaccine; Policies; Politics; Probability; Property; Public Health; Randomized; Relaxation; Research; Rotavirus Vaccines; Sampling; Science; Statistical Methods; Structure; Time; Vaccinated; Vaccination; Vaccines; cost; disorder control; disorder prevention; econometrics; experimental study; improved; influenza virus vaccine; innovation; insight; interest; intervention effect; pathogen; prevent; research study; simulation; social; software development; treatment effect; user friendly software; vaccine trial

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.

概括 这项研究的总体目的是开发统计方法来量化干预措施的影响 预防传染病。这项研究的主要激励例子是疫苗的研究，但是 开发的方法将是一般的，并在其他设置中立即应用。一个特别重要 疫苗研究中的挑战问题需要评估疫苗的间接作用（溢出）影响。用于疫苗 当一个人接种疫苗时，昂贵或无法完全保护疾病，评估间接 影响（或牛群免疫）在有关疫苗引入和利用方面的政策考虑方面很重要。无法 关于群疫苗的帐户可能会导致关于疫苗公共卫生益处的不正确结论。绘画 关于牛群免疫的推断是非标准的，因为间接效应测量了接种疫苗的效果 在因果推论的命名法中，这被称为 “干涉。”也就是说，如果一个人的治疗（例如，疫苗接种）会影响干扰 另一个人的结果。在这种赠款中，将开发用于绘制推理的创新统计方法 关于当个人之间可能干扰治疗或暴露的影响。每个 该项目的目的是，将建立所提出的统计方法的理论特性。模拟 将进行研究以评估在广泛现实的范围内提出的方法的性能 设置。开发的方法将用于分析来自几种大型传染病预防的数据 研究，提供有关疫苗对霍乱，影响力和其他病原体的不同影响的新见解，以及 疟疾床网。最终的推论将对预期数字有直接的解释 由于干预而避免了感染或疾病病例。用户友好的软件实施了建议的 方法将开发并免费提供。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，包括经济学，教育，网络 分析，政治学和空间分析。

项目成果

Large sample randomization inference of causal effects in the presence of interference.

• DOI: 10.1080/01621459.2013.844698
• 发表时间: 2014-01-01
• 期刊: Journal of the American Statistical Association
• 影响因子: 3.7
• 作者: Liu L;Hudgens MG
• 通讯作者: Hudgens MG

Challenges of evaluating and modelling vaccination in emerging infectious diseases.

• DOI: 10.1016/j.epidem.2021.100506
• 发表时间: 2021-12
• 期刊: Epidemics
• 影响因子: 3.8
• 作者: Madewell ZJ;Dean NE;Berlin JA;Coplan PM;Davis KJ;Struchiner CJ;Halloran ME
• 通讯作者: Halloran ME

A Bayesian approach to estimating causal vaccine effects on binary post-infection outcomes.

用于估计疫苗对二元感染后结果的因果效应的贝叶斯方法。

• DOI: 10.1002/sim.6573
• 发表时间: 2016
• 期刊: Statistics in medicine
• 影响因子: 2
• 作者: Zhou,Jincheng;Chu,Haitao;Hudgens,MichaelG;Halloran,MElizabeth
• 通讯作者: Halloran,MElizabeth

Comparing bounds for vaccine effects on infectiousness.

比较疫苗对传染性影响的界限。

• DOI: 10.1097/ede.0b013e31826d0741
• 发表时间: 2012
• 期刊: Epidemiology (Cambridge, Mass.)
• 作者: Halloran,MElizabeth;Hudgens,MichaelG
• 通讯作者: Hudgens,MichaelG

Using social contact data to improve the overall effect estimate of a cluster-randomized influenza vaccination program in Senegal.

• DOI: 10.1111/rssc.12522
• 发表时间: 2022-01
• 期刊: JOURNAL OF THE ROYAL STATISTICAL SOCIETY SERIES C-APPLIED STATISTICS
• 影响因子: 1.6
• 作者: Potter, Gail E.;Carnegie, Nicole Bohme;Sugimoto, Jonathan D.;Diallo, Aldiouma;Victor, John C.;Neuzil, Kathleen M.;Elizabeth Halloran, M.
• 通讯作者: Elizabeth Halloran, M.