Causal inference in infectious disease prevention studies

传染病预防研究中的因果推断

• 批准号: 8385550
• 负责人: Michael G Hudgens
• 金额: $ 29.76万
• 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 2014-12-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/8385550
• 关键词: Affect; Area; Bangladesh; Cessation of life; Child; Cholera Vaccine; Communicable Diseases; Confidence Intervals; Data; Data Analyses; Data Set; Development; Event; HIV; Health; Health Policy; Individual; Infection; Intervention; Lead; Malawi; Measures; Methodology; Methods; Mothers; Observational Study; Outcome; Pertussis; Public Health; Research; Research Personnel; Risk; Sampling; Senegal; Statistical Methods; Stratification; Testing; Time; Vaccinated; Vaccination; Vaccine Clinical Trial; Vaccines; Vertical Disease Transmission; Weaning; antiretroviral therapy; conditioning; disorder control; disorder prevention; improved; interest; intervention effect; postnatal; prevent; public health relevance; transmission process; vaccine effectiveness

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. The main motivating examples are studies of vaccine effectiveness. Two particularly challenging problems in vaccine studies entail assessing (i) indirect effects of vaccination and (ii) vaccine effects on post-infection endpoints. Evaluating (i) is a non-standard problem because indirect effects measure the effect of vaccinating one individual on another individual's health outcome. Assessing (ii) is challenging because infected vaccinees may not be comparable to infected controls. This specific proposal is to adapt and develop modern causal inference methodology for use in evaluating (i) and (ii). Similar research will be conducted motivated by studies to prevent transmission of HIV from mother to child where issues similar to (ii) arise. Specific Aim 1 is to develop statistical methods in causal inference with interference for application in evaluating direct, indirect, total, and overall vaccine effects. Areas of particular emphasis will be development of nonparametric tests and confidence intervals, incorporating baseline covariates, and analysis of data from observational studies. A motivating data set is from a trial of cholera vaccines in Bangladesh. Specific Aim 2 is to develop exact statistical methods in causal inference with principal stratification for application in evaluating vaccine effects on post-infection endpoints. This research will focus on applying the ideas of principal stratification in the small sample setting under minimal assumptions. Comparisons will be conducted between the proposed methods and existing large-sample methods as well as traditional intent-to-treat approaches. The research for this aim is motivated by proof-of-concept clinical trials of vaccines where few events are expected. Specific Aim 3 is to develop causal inference methodology to assess vaccine effects on infectiousness. This research will combine aspects of Aims 1 and 2 since studies to assess vaccine effects on infectiousness typically entail conditioning on infection of primary cases (Aim 2) and the vaccination status of the primary case can affect the infection outcome in exposed close contacts (Aim 1). Methods developed in this aims will be used to estimate the causal effect of pertussis vaccination on infectiousness using data from a study in Senegal. Specific Aim 4 is to develop statistical methods for causal inference with principal stratification and competing risks. This research is motivated by studies to prevent postnatal mother-to-child transmission (MTCT) of HIV where HIV-free death and weaning are competing risks. In such studies, investigators are often interested in comparing intervention strategies conditional on survival to a certain time point such that this aim will utilize the principal stratification framework. The methods developed under this aim will be used to estimate the causal effect of antiretroviral therapy on MTCT using data from a recent study in Malawi. 1

描述（由申请人提供）：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。主要的激励例子是疫苗有效性的研究。疫苗研究中的两个特别具有挑战性的问题需要评估（i）疫苗接种和（ii）疫苗对感染后终点的影响。评估（i）是一个非标准的问题，因为间接效应衡量了接种一个人对另一个人的健康结果的影响。评估（II）是具有挑战性的，因为受感染的疫苗可能与感染对照不可媲美。该具体建议是适应和开发现代因果推理方法，用于评估（i）和（ii）。类似的研究将受到研究的动机，以防止艾滋病毒从母亲传播到孩子，那里出现了（ii）。具体目的1是开发因因果推断而在评估直接，间接，总和总疫苗效应时的统计方法。特别重点的领域将开发非参数测试和置信区间，结合基线协变量以及观察性研究的数据分析。激励的数据集来自孟加拉国霍乱疫苗的试验。具体目的2是开发有关因果推断的精确统计方法，并在评估感染后终点的疫苗影响时应用主要分层。这项研究将着重于在最小的假设下的小样本设置中应用主要分层的思想。将在提出的方法和现有的大样本方法以及传统意图对治疗方法之间进行比较。该目标的研究是由概念证明的临床试验进行的，这些试验的疫苗很少发生。具体目的3是开发因果推理方法，以评估疫苗对传染性的影响。这项研究将结合目标1和2的各个方面，因为研究评估疫苗对感染性的影响通常需要对原发性病例感染进行调节（AIM 2），而主要病例的疫苗接种状态可能会影响暴露的紧密接触中的感染结果（AIM 1）。在此目的中开发的方法将用于估计百日咳疫苗接种对传染性的因果关系，并使用塞内加尔的一项研究数据。特定目的4是开发统计方法，用于通过主要分层和竞争风险进行因果推断。这项研究是由研究旨在防止艾滋病毒后产后母亲到孩子传播（MTCT）的研究的动机，在这种研究中，无艾滋病毒的死亡和断奶是竞争的风险。在此类研究中，研究人员通常有兴趣将有条件的生存的干预策略与特定时间点进行比较，以便该目标将利用主要分层框架。该目标下开发的方法将用于估计抗逆转录病毒疗法对MTCT的因果关系，并使用马拉维最近一项研究的数据。 1