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Browsing by Author "Keller, Kayleigh, advisor"

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    Causality and clustering in complex settings
    (Colorado State University. Libraries, 2023) Gibbs, Connor P., author; Keller, Kayleigh, advisor; Fosdick, Bailey, advisor; Koslovsky, Matthew, committee member; Kaplan, Andee, committee member; Anderson, Brooke, committee member
    Causality and clustering are at the forefront of many problems in statistics. In this dissertation, we present new methods and approaches for drawing causal inference with temporally dependent units and clustering nodes in heterogeneous networks. To begin, we investigate the causal effect of a timeout at stopping an opposing team's run in the National Basketball Association (NBA). After formalizing the notion of a run in the NBA and in light of the temporal dependence among runs, we define the units under study with careful consideration of the stable unit-treatment-value assumption pertinent to the Rubin causal model. After introducing a novel, interpretable outcome based on the score difference, we conclude that while comebacks frequently occur after a run, it is slightly disadvantageous to call a timeout during a run by the opposing team. Further, we demonstrate that the magnitude of this effect varies by franchise, lending clarity to an oft-debated topic among sports' fans. Following, we represent the known relationships among and between genetic variants and phenotypic abnormalities as a heterogeneous network and introduce a novel analytic pipeline to identify clusters containing undiscovered gene to phenotype relations (ICCUR) from the network. ICCUR identifies, scores, and ranks small heterogeneous clusters according to their potential for future discovery in a large temporal biological network. We train an ensemble model of boosted regression trees to predict clusters' potential for future discovery using observable cluster features, and show the resulting clusters contain significantly more undiscovered gene to phenotype relations than expected by chance. To demonstrate its use as a diagnostic aid, we apply the results of the ICCUR pipeline to real, undiagnosed patients with rare diseases, identifying clusters containing patients' co-occurring yet otherwise unconnected genotypic and phenotypic information, some connections which have since been validated by human curation. Motivated by ICCUR and its application, we introduce a novel method called ECoHeN (pronounced "eco-hen") to extract communities from heterogeneous networks in a statistically meaningful way. Using a heterogeneous configuration model as a reference distribution, ECoHeN identifies communities that are significantly more densely connected than expected given the node types and connectivity of its membership without imposing constraints on the type composition of the extracted communities. The ECoHeN algorithm identifies communities one at a time through a dynamic set of iterative updating rules and is guaranteed to converge. To our knowledge this is the first discovery method that distinguishes and identifies both homogeneous and heterogeneous, possibly overlapping, community structure in a network. We demonstrate the performance of ECoHeN through simulation and in application to a political blogs network to identify collections of blogs which reference one another more than expected considering the ideology of its' members. Along with small partisan communities, we demonstrate ECoHeN's ability to identify a large, bipartisan community undetectable by canonical community detection methods and denser than modern, competing methods.
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    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 member
    Exposure 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.