Flexible causal inference methods for estimating longitudinal effects of air pollution on chronic lung disease

摘要 这份量化研究导师职业发展奖的申请书已与 目标是支持马林斯基博士作为生物统计学交叉学科的定量研究人员的职业生涯， 流行病学和环境健康的数据科学。培训和研究计划建立在Dr。 马林斯基在统计学和计算机科学方面的定量跨学科背景，特别是他的 具有因果推理和机器学习方面的专业知识。首要的研究目标是开发小说 在观测中迎接重要分析挑战的因果推断的统计方法 环境流行病学，并将这些方法应用于空气污染和慢性肺部疾病的研究， 使用的数据来自长期的动脉粥样硬化多种族研究(MESA)。这些方法将用于 估计几种环境空气污染物(臭氧、细颗粒物和氮氧化物)对 随着时间的延长，肺气肿的进展和肺功能的下降。严查 这些关系对于促进我们对病因和发病机制的理解都是重要的 这项工作的目的是为潜在的肺部疾病提供信息，并向有关污染浓度水平的监管政策提供信息。焦点 将对从观测纵向数据进行因果推断的方法进行扩展和调整， 以前被开发来适应时变的混淆和量化由于 未测量的混杂，但从未适用于关于空气污染和慢性肺的复杂纵向数据 疾病。这些将被用来估计假设性变化对肺部疾病的长期后果。 空气污染暴露水平。研究计划的目标1扩展现有方法以应对挑战 具体到空气污染流行病学，即通过利用机器学习的进展来估计稳健性 暴露倾向和灵活的剂量-反应函数。研究计划的目标2利用了这些 方法调查上述污染物与环境污染之间关系的假设 在MESA数据中衡量肺部疾病，并确定易受伤害的亚群。AIM 3将扩展一个 对统计文献中的反事实敏感度分析的方法，该方法量化了由于 对MESA的设置进行不可测量的混淆，并将这一方法应用于MESA数据。应用程序 通过以下领域的监督和教学指导，制定指导和职业发展计划 空气污染科学、环境流行病学、气候、纵向研究设计等相关主题 为MESA数据建立可信的分析模型。马林斯基博士将得到一个 指导团队在空气污染科学和测量、肺部疾病、生物统计学方面拥有丰富的专业知识 方法，以及健康的环境决定因素。该奖项将使马林斯基博士成为一名独立的 他是这一跨学科领域的一名研究人员，并使他能够成功地竞争R01资金。

Abstract This application for a Mentored Quantitative Research Career Development Award has been submitted with the goal of supporting Dr. Malinsky’s career as a quantitative researcher at the intersection of biostatistics, epidemiology, and data science for environmental health. The training and research plan build on Dr. Malinsky’s quantitative interdisciplinary background in statistics and computer science, in particular his expertise in causal inference and machine learning. The overarching research goal is to develop novel statistical methods for causal inference that meet important analytical challenges in observational environmental epidemiology and apply these methods to the study of air pollution and chronic lung diseases, using data from the longstanding Multi-Ethnic Study of Atherosclerosis (MESA). The methods will be used to estimate the effects of several ambient air pollutants (ozone, fine particulate matter, and oxides of nitrogen) on progression of emphysema and decline in lung function over an extended time period. Rigorously investigating these relationships is important both for advancing our understanding of the etiology and mechanisms underlying lung disease and to inform regulatory policies concerning pollution concentration levels. The focus will be on extending and adapting methods for causal inference from observational longitudinal data, which have been previously developed to accommodate time-varying confounding and quantify uncertainty due to unmeasured confounding, but never applied to complex longitudinal data on air pollution and chronic lung disease. These will be used to estimate the long-term lung disease consequences of hypothetical changes to air pollution exposure levels. Aim 1 of the research plan extends existing methods to address challenges specific to air pollution epidemiology, namely by exploiting advances in machine learning to estimate robust exposure propensities and flexible dose-response functions. Aim 2 of the research plan leverages these methods to investigate hypotheses about the relationships between the aforementioned pollutants and measures of lung disease in the MESA data and identify vulnerable subpopulations. Aim 3 will extend an approach to counterfactual sensitivity analysis in the statistical literature that quantifies uncertainty due to unmeasured confounding to the setting of MESA and apply this approach to the MESA data. The application delineates plans for mentoring and career development via supervision and didactic instruction in the areas of air pollution science, environmental epidemiology, climate, longitudinal study design, and other topics relevant to the construction of credible analysis models for the MESA data. Dr. Malinsky will be supported by a mentoring team with considerable expertise in air pollution science & measurement, lung disease, biostatistical methods, and environmental determinants of health. The award will establish Dr. Malinsky as an independent investigator in this interdisciplinary area and enable him to successfully compete for R01 funding.