Browsing by Author "Neophytou, Andreas, committee member"
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Item Open Access 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 memberIn 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.Item Open Access Methodology in air pollution epidemiology for large-scale exposure prediction and environmental trials with non-compliance(Colorado State University. Libraries, 2023) Ryder, Nathan, author; Keller, Kayleigh, advisor; Wilson, Ander, committee member; Cooley, Daniel, committee member; Neophytou, Andreas, committee memberExposure to airborne pollutants, both long- and short-term, can lead to harmful respiratory, cardiovascular, and cardiometabolic outcomes. Multiple challenges arise in the study of relationships between ambient air pollution and health outcomes. For example, in large observational cohort studies, individual measurements are not feasible so researchers use small sets of pollutant concentration measurements to predict subject-level exposures. As a second example, inconsistent compliance of subjects to their assigned treatments can affect results from randomized controlled trials of environmental interventions. In this dissertation, we present methods to address these challenges. We develop a penalized regression model that can predict particulate matter exposures in space and time, including penalties to discourage overfitting and encourage smoothness in time. This model is more accurate than spatial-only and spatiotemporal universal kriging (UK) models when the exposures are missing in a regular (semi-daily) pattern. Our penalized regression model is also faster than both UK models, allowing the use of bootstrap methods to account for measurement error bias and monitor site selection in a two-stage health model. We introduce methods to estimate causal effects in a longitudinal setting by latent "at-the-time" principal strata. We implement an array of linear mixed models on data subsets, each with weights derived from principal scores. In addition, we estimate the same stratified causal effects with a Bayesian mixture model. The weighted linear mixed models outperform the Bayesian mixture model and an existing single-measure principal scores method in all simulation scenarios, and are the only method to produce a significant estimate for a causal effect of treatment assignment by strata when applied to a Honduran cookstove intervention study. Finally, we extend the "at-the-time" longitudinal principal stratification framework to a setting where continuous exposure measurements are the post-treatment variable by which the latent strata are defined. We categorize the continuous exposures to a binary variable in order to use our previous method of weighted linear mixed models. We also extend an existing Bayesian approach to the longitudinal setting, which does not require categorization of the exposures. The previous weighted linear mixed model and single-measure principal scores methods are negatively biased when applied to simulated samples, while the Bayesian approach produces the lowest RMSE and bias near zero. The Bayesian approach, when applied to the same Honduran cookstove intervention study as before, does not find a significant estimate for the causal effect of treatment assignment by strata.Item Open Access Reference values of the distal sensory median and ulnar nerves among newly hired workers(Colorado State University. Libraries, 2021) Hischke, Molly, author; Rosecrance, John, advisor; Neophytou, Andreas, committee member; Anderson, Brooke, committee member; Gerr, Fredric, committee member; Reiser, Raoul F., II, committee memberCarpal tunnel syndrome (CTS) is the most common entrapment neuropathy in the upper extremity and more common among workers in industrial occupations than in the general population (Atroshi et al., 1999; Mattioli et al., 2009; Palmer, Harris, & Coggon, 2007). Because of the high prevalence of CTS in certain industries, some employers have implemented post-offer pre-placement screening programs using nerve conduction studies (NCS) to identify those at higher risk of developing CTS. NCS are commonly used to identify the median neuropathy characteristic of CTS by assessing the nerve conduction speed of the median nerve. There have been a number of retrospective and prospective cohort studies that have examined the relationship between NCS indicating median neuropathy among workers and the subsequent development of CTS (Werner et al., 2001; Franzblau et al., 2004; Gell et al., 2005; Silverstein et al., 2010; Dale et al., 2014). These studies have indicated that workers with NCS indicating median neuropathy across the carpal tunnel who were initially asymptomatic for CTS, eventually developed CTS at a statistically significant greater rate than workers with normal nerve studies. Some employers have used NCS to identify workers at higher risk of developing CTS and placing them into low hand-intensive work tasks to reduce the high prevalence of work-related CTS. To identify workers at higher risk, their NCS results are often compared to population-based reference values. However, many of these published reference values are limited by their small samples sizes and unsuitable statistical methodologies (Dillingham et al., 2016). Further, some researchers have questioned whether population-based reference values are representative of working populations, especially those in industries with a high prevalence of abnormal NCS (Dale, Gardner, Buckner-petty, Strickland, & Evanoff, 2016; Salerno et al., 1998). The purpose of this dissertation research was to (1) establish reference values for NCS outcomes of the distal upper extremity from a large sample (N=17,630) of newly hired manufacturing workers using novel statistical methods more appropriate for nerve conduction data, (2) investigate comorbid conditions associated with nerve conduction outcomes, and (3) determine the sensitivity and specificity of CTS symptoms for identifying workers with median mononeuropathy.