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.

总结

项目成果

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

使用社会接触数据来改善塞内加尔的聚类随机流感疫苗接种计划的总体效果估计。

• 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.

Identification of causal intervention effects under contagion.

• DOI: 10.1515/jci-2019-0033
• 发表时间: 2021-01
• 期刊: Journal of causal inference
• 影响因子: 1.4
• 作者: Cai X;Loh WW;Crawford FW
• 通讯作者: Crawford FW

A Recipe for inferference: Start with Causal Inference. Add Interference. Mix Well with R.

推理秘诀：从因果推理开始。

• DOI: 10.18637/jss.v082.i02
• 发表时间: 2017
• 期刊: Journal of statistical software
• 影响因子: 5.8
• 作者: Saul,BradleyC;Hudgens,MichaelG
• 通讯作者: Hudgens,MichaelG

Assessing effects of cholera vaccination in the presence of interference.

• DOI: 10.1111/biom.12184
• 发表时间: 2014-09
• 期刊: Biometrics
• 影响因子: 1.9
• 作者: Perez-Heydrich C;Hudgens MG;Halloran ME;Clemens JD;Ali M;Emch ME
• 通讯作者: Emch ME

Randomization-Based Inference within Principal Strata.

• DOI: 10.1198/jasa.2011.tm10356
• 发表时间: 2011-06
• 期刊: Journal of the American Statistical Association
• 影响因子: 3.7
• 作者: Nolen TL;Hudgens MG
• 通讯作者: Hudgens MG