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

用于估计空气污染对慢性肺病纵向影响的灵活因果推理方法

• 批准号: 10680381
• 负责人: Daniel Malinsky
• 金额: $ 10.91万
• 依托单位: COLUMBIA UNIVERSITY HEALTH SCIENCES
• 依托单位国家: 美国
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-08-16 至 2027-05-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10680381
• 关键词: Address; Air; Air Pollutants; Air Pollution; Applications Grants; Area; Award; Behavioral; Biometry; Biostatistical Methods; Black race; Body mass index; Chronic Obstructive Pulmonary Disease; Chronic lung disease; Climate; Complement; Complex; Computer software; Data; Data Science; Data Sources; Dependence; Disease; Disease Outcome; Disease Progression; Dose; Environmental Epidemiology; Environmental Health; Environmental Science; Epidemiologist; Epidemiology; Ethnic Origin; Etiology; Exposure to; Funding; Goals; Grant; Health; Heterogeneity; Hispanic; Image; Individual; Instruction; K-Series Research Career Programs; Knowledge; Link; Literature; Longitudinal Studies; Longitudinal cohort; Longitudinal, observational study; Lung diseases; Machine Learning; Measurement; Measures; Mentors; Metals; Methodology; Methods; Modeling; Multi-Ethnic Study of Atherosclerosis; Nitrogen Oxides; Not Hispanic or Latino; Outcome; Ozone; Particulate Matter; Policies; Policy Analysis; Pollution; Probability; Pulmonary Emphysema; Race; Research; Research Design; Research Personnel; Risk; Science; Shapes; Site; Statistical Methods; Statistical Models; Structural Models; Supervision; Techniques; Time; Training; Uncertainty; United States; Variant; Weight; X-Ray Computed Tomography; career; career development; cohort; computer science; design; epidemiologic data; fine particles; flexibility; health determinants; intervention effect; lung health; machine learning prediction; mortality; novel; open source; pollutant; pulmonary function; respiratory; response; semiparametric; sex; social; statistics; tool

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.

抽象的 该申请已提交了指导定量研究职业发展奖 支持马林斯基博士在生物统计学交集的定量研究人员职业的目标， 环境健康的流行病学和数据科学。培训和研究计划以博士为基础 马林斯基在统计和计算机科学方面的定量跨学科背景，尤其是他的 因果推理和机器学习方面的专业知识。总体研究目标是开发小说 因果推断的统计方法，符合观察性的重要分析挑战 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， 使用长期以来对动脉粥样硬化多民族研究（MESA）的数据。这些方法将用于 估计几种环境空气污染物（臭氧，特殊物质和氮的氧化物）对 在延长的时间段内，先得研究肺气肿的进展和肺功能下降。 这些关系对于促进我们对病因和机制的理解很重要 潜在的肺部疾病并为有关污染浓度水平的监管政策提供信息。重点 将从观察性纵向数据中扩展和适应因果推断的方法， 以前已经开发出来适应随着时变的混杂和量化不确定性 无法衡量的混杂，但从未应用于空气污染和慢性肺的复杂纵向数据 疾病。这些将用于估计假设变化的长期肺部疾病后果 空气污染的水平。研究计划的目标1扩展了现有方法以应对挑战 特定于空气污染流行病学，即通过利用机器学习的进步来估算强大 暴露建议和灵活的剂量反应功能。目的2的研究计划利用了这些 调查有关相关性与关系之间关系的假设的方法 MESA数据中肺部疾病的度量并确定脆弱的亚群。 AIM 3将延伸 统计文献中反事实敏感性分析的方法，该方法量化了由于 无法衡量的混杂在MESA的设置中，并将这种方法应用于MESA数据。应用程序 通过监督和教学教学在 空气污染科学，环境流行病学，气候，纵向研究设计以及其他相关主题 构建用于MESA数据的可靠分析模型。马林斯基博士将得到 指导团队具有空气污染科学与测量，肺部疾病，生物统计学的考虑专业知识 方法和健康的环境决定者。该奖项将建立马林斯基博士为独立 这个跨学科地区的调查员使他能够成功竞争R01资金。