- ItemOpen AccessStatistical 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 memberThe 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.
- ItemOpen AccessMethodology 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.
- ItemOpen AccessApplication of statistical and deep learning methods to power grids(Colorado State University. Libraries, 2023) Rimkus, Mantautas, author; Kokoszka, Piotr, advisor; Wang, Haonan, advisor; Nielsen, Aaron, committee member; Cooley, Dan, committee member; Chen, Haonan, committee memberThe structure of power flows in transmission grids is evolving and is likely to change significantly in the coming years due to the rapid growth of renewable energy generation that introduces randomness and bidirectional power flows. Another transformative aspect is the increasing penetration of various smart-meter technologies. Inexpensive measurement devices can be placed at practically any component of the grid. As a result, traditional fault detection methods may no longer be sufficient. Consequently, there is a growing interest in developing new methods to detect power grid faults. Using model data, we first propose a two-stage procedure for detecting a fault in a regional power grid. In the first stage, a fault is detected in real time. In the second stage, the faulted line is identified with a negligible delay. The approach uses only the voltage modulus measured at buses (nodes of the grid) as the input. Our method does not require prior knowledge of the fault type. We further explore fault detection based on high-frequency data streams that are becoming available in modern power grids. Our approach can be treated as an online (sequential) change point monitoring methodology. However, due to the mostly unexplored and very nonstandard structure of high-frequency power grid streaming data, substantial new statistical development is required to make this methodology practically applicable. The work includes development of scalar detectors based on multichannel data streams, determination of data-driven alarm thresholds and investigation of the performance and robustness of the new tools. Due to a reasonably large database of faults, we can calculate frequencies of false and correct fault signals, and recommend implementations that optimize these empirical success rates. Next, we extend our proposed method for fault localization in a regional grid for scenarios where partial observability limits the available data. While classification methods have been proposed for fault localization, their effectiveness depends on the availability of labeled data, which is often impractical in real-life situations. Our approach bridges the gap between partial and full observability of the power grid. We develop efficient fault localization methods that can operate effectively even when only a subset of power grid bus data is available. This work contributes to the research area of fault diagnosis in scenarios where the number of available phasor measurement unit devices is smaller than the number of buses in the grid. We propose using Graph Neural Networks in combination with statistical fault localization methods to localize faults in a regional power grid with minimal available data. Our contribution to the field of fault localization aims to enable the adoption of effective fault localization methods for future power grids.
- ItemOpen AccessCausality 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 memberCausality 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.
- ItemOpen AccessRandomization tests for experiments embedded in complex surveys(Colorado State University. Libraries, 2022) Brown, David A., author; Breidt, F. Jay, advisor; Sharp, Julia, committee member; Zhou, Tianjian, committee member; Ogle, Stephen, committee memberEmbedding experiments in complex surveys has become increasingly important. For scientific questions, such embedding allows researchers to take advantage of both the internal validity of controlled experiments and the external validity of probability-based samples of a population. Within survey statistics, declining response rates have led to the development of new methods, known as adaptive and responsive survey designs, that try to increase or maintain response rates without negatively impacting survey quality. Such methodologies are assessed experimentally. Examples include a series of embedded experiments in the 2019 Triennial Community Health Survey (TCHS), conducted by the Health District of Northern Larimer County in collaboration with the Department of Statistics at Colorado State University, to determine the effects of monetary incentives, targeted mailing of reminders, and double-stuffed envelopes (including both English and Spanish versions of the survey) on response rates, cost, and representativeness of the sample. This dissertation develops methodology and theory of randomization-based tests embedded in complex surveys, assesses the methodology via simulation, and applies the methods to data from the 2019 TCHS. An important consideration in experiments to increase response rates is the overall balance of the sample, because higher overall response might still underrepresent important groups. There have been advances in recent years on methods to assess the representativeness of samples, including application of the dissimilarity index (DI) to help evaluate the representativeness of a sample under the different conditions in an incentive experiment (Biemer et al. ). We develop theory and methodology for design-based inference for the DI when used in a complex survey. Simulation studies show that the linearization method has good properties, with good confidence interval coverage even in cases when the true DI is close to zero, even though point estimates may be biased. We then develop a class of randomization tests for evaluating experiments embedded in complex surveys. We consider a general parametric contrast, estimated using the design-weighted Narain-Horvitz-Thompson (NHT) approach, in either a completely randomized design or a randomized complete block design embedded in a complex survey. We derive asymptotic normal approximations for the randomization distribution of a general contrast, from which critical values can be derived for testing the null hypothesis that the contrast is zero. The asymptotic results are conditioned on the complex sample, but we include results showing that, under mild conditions, the inference extends to the finite population. Further, we develop asymptotic power properties of the tests under moderate conditions. Through simulation, we illustrate asymptotic properties of the randomization tests and compare the normal approximations of the randomization tests with corresponding Monte Carlo tests, with a design-based test developed by van den Brakel, and with randomization tests developed by Fisher-Pitman-Welch and Neyman. The randomization approach generalizes broadly to other kinds of embedded experimental designs and null hypothesis testing problems, for very general survey designs. The randomization approach is then extended from NHT estimators to generalized regression estimators that incorporate auxiliary information, and from linear contrasts to comparisons of nonlinear functions.