Transporting established insights from classical experimental design to address causal questions in environmental epidemiology including the understanding of biological mediating mechanisms

 描述（由申请人提供）：由于数据缺失、缺乏随机化以及时间性引起的并发症，在正确处理观察性研究中的因果关系方面存在根本性差距。关联的措施不适合提出相关的政策建议，因为这些措施涉及干预措施的建议，而干预措施是因果关系的陈述。长期目标是解决重要的环境健康因果问题（例如，（c）发现将空气污染与低出生体重或自闭症联系起来的生物机制，以及将极端天气条件与中暑或全球营养不良联系起来），这些都具有与政策相关的后果，并制定统计方法，正确处理环境健康研究中的因果关系。这项资助申请的总体目标是正确地制定和估计因果环境健康影响，特别是在存在中间变量的情况下，通过传输在过去80年（基本上是自Fisher，1935年以来）的缺失数据（Rubin 1978）和经典和现代多因子随机实验领域开发的成功统计工具。在初步发展和重要应用的指导下，本研究将包括四个具体目标：1）扩展高维缺失数据的成功多重插补方法，以便在使用标准完全数据方法面对缺失数据时进行有效的统计推断。将考虑两种不同的设置，第一种处理多变量时间序列，第二种处理根据不太准确但可用的测量值进行的“金标准”预测。2)发展统计学理论，从观察性研究收集的数据中估计偶然被估量，这些被估量将被重建为随机实验的近似数据;一个特别有趣的设置将考虑暴露和结果之间因果路径上的中间变量（也称为“介质”）。3)扩展因果中介分析的标准方法;中介分析已成为一种流行的开发工具，用于检查因果生物途径及其对不良健康影响的相对贡献。4)使用与生物医学研究人员目前使用的软件兼容的新软件实现前三个目标中开发的这些方法。拟议的以正确制定和估计环境健康因果影响为目标的研究是创新性的，因为它通过在两个领域传播经典和现代统计学中发展的成功方法和概念，代表了对现状的重大偏离：1）处理缺失数据的多重插补技术，2）复杂多因素随机实验的分析，特别是在存在中间变量和复杂数据的情况下（例如，纵向、生存和高维）。拟议的统计方法将是重要的生物医学研究，因为它将产生有效的因果环境健康影响的估计下，精确陈述的假设，预计将提供积极的影响政策决定，并建议适当的干预措施。

 DESCRIPTION (provided by applicant): There is a fundamental gap in correctly addressing causality in observational studies due to missing data, and lack of randomization, and complications due to temporality. Measures of association are inapposite for making relevant policy recommendations because these involve suggestions for interventions, which are causal statements. The long-term goal is to address important environmental health causal questions (e.g., discovering biological mechanisms linking air pollution to low birth weight or autism, and relating extreme weather conditions to heat stroke or worldwide nutritional deficiency) that have policy-relevant consequences, and develop statistical methodology that correctly addresses causality in environmental health studies. The overall objective of this grant application is to correctly formulate and estimate causal environmental health effects, especially in the presence of intermediate variables, by transporting successful statistical tools developed in the fields of missing data (Rubin 1978) and classical and modern multi-factorial randomized experiments over the past 80 years (essentially since Fisher, 1935). Guided by preliminary development and significant applications, this proposed research will consist of four specific aims: 1) Expand successful multiple- imputation methods for high-dimensional missing data to enable valid statistical inference when confronted with missing data using standard complete-data methods. Two different settings will be considered, the first one dealing with multivariate time series and the second with "gold standard" prediction from less accurate but available measurements. 2) Develop statistical theory to estimate casual estimands from data collected by observational studies, which would be reconstructed to approximate data from a randomized experiment; one particularly interesting setting will consider intermediate variables on the causal pathway between an exposure and an outcome (also called "mediators"). 3) Expand standard methods developed for causal mediation analysis; analysis of mediation has become a popular developing tool to examine causal biological pathways and their relative contribution to adverse health effects. 4) Implement these methods developed in the three previous aims with new software that is compatible with software currently used by biomedical researchers. The proposed research targeting correct formulation and estimation of causal environmental health effects is innovative because it represents a substantial departure from the status quo by transporting successful methods and concepts developed in classical and modern statistics in two areas: 1) multiple imputation techniques for handling missing data, 2) analysis of complex multi-factorial randomized experiments, especially in the presence of intermediate variables and complex data (e.g., longitudinal, survival, and high-dimensional). The proposed statistical methodology will be significant to biomedical research because it will yield valid causal environmental health effect estimates under precisely stated assumptions, which are expected to provide positive impacts on policy decisions and suggest appropriate interventions.