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

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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.