Causal inference in infectious disease prevention studies

描述（由申请人提供）：本研究的总体目标是发展统计方法，量化预防传染病的干预措施的效果。主要的激励例子是对疫苗有效性的研究。疫苗研究中的两个特别具有挑战性的问题涉及评估(i)疫苗接种的间接影响和（ii）疫苗对感染后终点的影响。评估(i)是一个非标准问题，因为间接效应衡量的是接种疫苗对另一个人健康结果的影响。评估（ii）具有挑战性，因为受感染的疫苗接种者可能无法与受感染的对照者进行比较。这一具体建议是适应和发展现代因果推理方法，用于评估(i)和（ii）。将进行类似的研究，其动机是防止艾滋病毒从母亲传染给出现类似（二）问题的儿童。具体目标1是发展具有干扰的因果推断的统计方法，用于评估疫苗的直接、间接、总和总体效果。特别强调的领域将是非参数检验和置信区间的发展，包括基线协变量，以及对观察性研究数据的分析。一组鼓舞人心的数据来自孟加拉国的霍乱疫苗试验。具体目标2是在主要分层的因果推理中发展精确的统计方法，用于评估疫苗对感染后终点的影响。本研究将侧重于在最小假设下的小样本设置中应用主分层的思想。将对提出的方法与现有的大样本方法以及传统的意向治疗方法进行比较。为这一目的进行研究的动机是疫苗的概念验证临床试验，在这些试验中很少发生预期的事件。具体目标3是发展因果推理方法，以评估疫苗对传染性的影响。本研究将结合目标1和目标2的各个方面，因为评估疫苗对传染性的影响的研究通常需要对原发病例的感染进行调节（目标2），而原发病例的疫苗接种状况可能影响暴露的密切接触者的感染结果（目标1）。为此目的制定的方法将利用塞内加尔一项研究的数据来估计百日咳疫苗接种对传染性的因果影响。具体目标4是发展具有主要分层和竞争风险的因果推理的统计方法。这项研究的动机是预防产后艾滋病毒母婴传播（MTCT）的研究，其中无艾滋病毒死亡和断奶是相互竞争的风险。在这类研究中，研究人员通常对以生存为条件的干预策略与特定时间点的比较感兴趣，因此这一目标将利用主要分层框架。根据这一目标开发的方法将使用马拉维最近一项研究的数据来估计抗逆转录病毒治疗对MTCT的因果效应

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 PUBLIC HEALTH RELEVANCE: This statistical methods developed in this research will lead to improved estimation of the effects of interventions to prevent infectious diseases. Accurate and precise quantification of intervention effects are important in regulatory decisions and public health policy regarding infectious disease control.