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Bayesian methods for environmental exposures: mixtures and missing data




Hoskovec, Lauren, author
Wilson, Ander, advisor
Magzamen, Sheryl, committee member
Hoeting, Jennifer, committee member
Cooley, Dan, committee member

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Air pollution exposure has been linked to increased morbidity and mortality. Estimating the association between air pollution exposure and health outcomes is complicated by simultaneous exposure to multiple pollutants, referred to as a multipollutant mixture. In a multipollutant mixture, exposures may have both independent and interactive effects on health. In addition, observational studies of air pollution exposure often involve missing data. In this dissertation, we address challenges related to model choice and missing data when studying exposure to a mixture of environmental pollutants. First, we conduct a formal simulation study of recently developed methods for estimating the association between a health outcome and exposure to a multipollutant mixture. We evaluate methods on their performance in estimating the exposure-response function, identifying mixture components associated with the outcome, and identifying interaction effects. Other studies have reviewed the literature or compared performance on a single data set; however, none have formally compared such a broad range of new methods in a simulation study. Second, we propose a statistical method to analyze multiple asynchronous multivariate time series with missing data for use in personal exposure assessments. We develop an infinite hidden Markov model for multiple time series to impute missing data and identify shared time-activity patterns in exposures. We estimate hidden states that represent latent environments presenting a unique distribution of a mixture of environmental exposures. Through our multiple imputation algorithm, we impute missing exposure data conditional on the hidden states. Finally, we conduct an individual-level study of the association between long-term exposure to air pollution and COVID-19 severity in a Denver, Colorado, USA cohort. We develop a Bayesian multinomial logistic regression model for data with partially missing categorical outcomes. Our model uses Polya-gamma data augmentation, and we propose a visualization approach for inference on the odds ratio. We conduct one of the first individual-level studies of air pollution exposure and COVID-19 health outcomes using detailed clinical data and individual-level air pollution exposure data.


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