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Browsing by Author "Wilson, Ander, advisor"

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    Bayesian methods for environmental exposures: mixtures and missing data
    (Colorado State University. Libraries, 2022) Hoskovec, Lauren, author; Wilson, Ander, advisor; Magzamen, Sheryl, committee member; Hoeting, Jennifer, committee member; Cooley, Dan, committee member
    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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    Bayesian tree based methods for longitudinally assessed environmental mixtures
    (Colorado State University. Libraries, 2024) Im, Seongwon, author; Wilson, Ander, advisor; Keller, Kayleigh, committee member; Koslovsky, Matt, committee member; Neophytou, Andreas, committee member
    In various fields, there is interest in estimating the lagged association between an exposure and an outcome. This is particularly common in environmental health studies, where exposure to an environmental chemical is measured repeatedly during gestation for the assessment of its lagged effects on a birth outcome. The relationship between longitudinally assessed environmental mixtures and a health outcome is also of greater interest. For a single exposure, a distributed lag model (DLM) is a widely used method that provides an appropriate temporal structure for estimating the time-varying effects. For mixture exposures, a distributed lag mixture model is used to address the main effect of each exposure and lagged interactions among exposures. The main inferential goals include estimating the lag-specific effects and identifying a window of susceptibility, during which a fetus is particularly vulnerable. In this dissertation, we propose novel statistical methods for estimating exposure effects of longitudinally assessed environmental mixtures in various scenarios. First, we propose a method that can estimate a linear exposure-time-response function between mixture exposures and a count outcome that may be zero-inflated and overdispersed. To achieve this, we employ a Bayesian Pólya-Gamma data augmentation with a treed distributed lag mixture model framework. We apply the method to estimate the relationship between weekly average fine particulate matter (PM2.5) and temperature and pregnancy loss with live-birth identified conception time series design with administrative data from Colorado. Second, we propose a tree triplet structure to allow for heterogeneity in exposure effects in an environmental mixture exposure setting. Our method accommodates modifier and exposure selection, which allows for personalized and subgroup-specific effect estimation and windows of susceptibility identification. We apply the method to Colorado administrative birth data to examine the heterogeneous relationship between PM2.5 and temperature and birth weight. Finally, we introduce an R package dlmtree that integrates tree structured DLM methods into convenient software. We provide an overview of the embedded tree structured DLMs and use simulated data to demonstrate a model fitting process, statistical inference, and visualization.
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    Bayesian treed distributed lag models
    (Colorado State University. Libraries, 2021) Mork, Daniel S., author; Wilson, Ander, advisor; Sharp, Julia, committee member; Keller, Josh, committee member; Neophytou, Andreas, committee member
    In many applications there is interest in regressing an outcome on exposures observed over a previous time window. This frequently arises in environmental epidemiology where either a health outcome on one day is regressed on environmental exposures (e.g. temperature or air pollution) observed on that day and several proceeding days or when a birth or children's health outcome is regressed on exposures observed daily or weekly throughout pregnancy. The distributed lag model (DLM) is a statistical method commonly implemented to estimate an exposure-time-response function by regressing the outcome on repeated measures of a single exposure over a preceding time period, for example, mean exposure during each week of pregnancy. Inferential goals include estimating the exposure-time-response function and identifying critical windows during which exposures can alter a health endpoint. In this dissertation, we develop novel formulations of Bayesian additive regression trees that allow for estimating a DLM. First, we propose treed distributed lag nonlinear models to estimate the association between weekly maternal exposure to air pollution and a birth outcome when the exposure-response relation is nonlinear. We introduce a regression tree-based model that accommodates a multivariate predictor along with parametric control for fixed effects. Second, we propose a tree-based method for estimating the association between repeated measures of a mixture of multiple pollutants and a health outcome. The proposed approach introduces regression tree pairs, which allow for estimation of marginal effects of exposures along with structured interactions that account for the temporal ordering of the exposure data. Finally, we present a framework to estimate a heterogeneous DLM in the presence of a potentially high dimensional set of modifying variables. We present simulation studies to validate the models. We apply these methods to estimate the association between ambient pollution exposures and birth weight for a Colorado, USA birth cohort.
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    Statistical models for COVID-19 infection fatality rates and diagnostic test data
    (Colorado State University. Libraries, 2023) Pugh, Sierra, author; Wilson, Ander, advisor; Fosdick, Bailey K., advisor; Keller, Kayleigh, committee member; Meyer, Mary, committee member; Gutilla, Molly, committee member
    The COVID-19 pandemic has had devastating impacts worldwide. Early in the pandemic, little was known about the emerging disease. To inform policy, it was essential to develop data science tools to inform public health policy and interventions. We developed methods to fill three gaps in the literature. A first key task for scientists at the start of the pandemic was to develop diagnostic tests to classify an individual's disease status as positive or negative and to estimate community prevalence. Researchers rapidly developed diagnostic tests, yet there was a lack of guidance on how to select a cutoff to classify positive and negative test results for COVID-19 antibody tests developed with limited numbers of controls with known disease status. We propose selecting a cutoff using extreme value theory and compared this method to existing methods through a data analysis and simulation study. Second, there lacked a cohesive method for estimating the infection fatality rate (IFR) of COVID-19 that fully accounted for uncertainty in the fatality data, seroprevalence study data, and antibody test characteristics. We developed a Bayesian model to jointly model these data to fully account for the many sources of uncertainty. A third challenge is providing information that can be used to compare seroprevalence and IFR across locations to best allocate resources and target public health interventions. It is particularly important to account for differences in age-distributions when comparing across locations as age is a well-established risk factor for COVID-19 mortality. There is a lack of methods for estimating the seroprevalence and IFR as continuous functions of age, while adequately accounting for uncertainty. We present a Bayesian hierarchical model that jointly estimates seroprevalence and IFR as continuous functions of age, sharing information across locations to improve identifiability. We use this model to estimate seroprevalence and IFR in 26 developing country locations.