Advisor: Reka Howard

]]>Advisor: Kent Eskridge

]]>Advisor: Kent Eskridge

]]>Advisers: Susan VanderPlas and Reka Howard

]]>To address this, I collaboratively designed an online introductory statistics course which focuses on simulation-based inference for the University of Nebraska-Lincoln. The course design was informed by the Community of Inquiry framework (Garrison, Anderson, & Archer, 2000). The course is delivered asynchronously and has the capacity for high enrollment. Following the development of the course, I co-taught this course from Fall 2018 to Spring 2021 and recruited enrolled students to participate in my study. Participants granted research access to several components of their normal coursework and completed three surveys: Survey of Attitudes Toward Statistics (36-question version pre-test and post-test; Schau, 2003a, 2003b) and the Distance Education and Technological Advancements Survey (Joosten & Reddy, 2015).

The primary goal of this study was to understand factors that influence student outcomes in this course. An intervention was designed to support the community of inquiry within the course and was implemented during Fall 2019 and Fall 2020. Using Bayesian hierarchical models, there was no evidence of an effect of the intervention on student outcomes. However, there were a variety of other self-reported factors that were found to be associated with student outcomes. The secondary aim of the study was to understand whether students' attitudes toward statistics changed during the term; however, descriptive statistics suggest that students' attitudes did not change during the term.

To address some of the limitations of this study, future research could examine these research questions for simulation-based introductory statistics courses across multiple institutions. This study may help create recommendations for developing online introductory statistics courses.

Adviser: Erin E. Blankenship

]]>A total of 297 lung and 111 nasal swabs from bovine were tested for AMR using the gold standard and rtPCR for three specific drugs. The level of agreement between the two tests were measured using Cohen’s Kappa (. Using the first approach, the optimal Ct for lung tissue samples was between 32.6 and 35.7, with a good level of agreement between the two tests. For the nasal tissue, the rtPCR results were only validated for one drug with a Ct of 33.3 with a moderate level of agreement. For the second approach, the lungs and nasal tissues are combined, and the optimal Ct is evaluated by taking the average from AUC and H measure and lies between 32.0 and 32.9 with a moderate level of agreement.

Adviser: Jennifer Clarke

]]>Advisor: Walter S Stroup

]]>An important decision that needs to be made prior to implementation is the group sizes to use. In best practice, an objective function is minimized to determine the optimal set of group sizes, known as the optimal testing configuration (OTC). We examine several different objective functions and show that the OTCs and corresponding results (e.g., number of tests, accuracy) are largely the same for these functions when using standard group testing algorithms.

Both estimating the probability of disease and identifying positive individuals are goals of group testing. We present the first general R functions for identification and make these available in the new binGroup2 package. We also include in this package estimation functions from the binGroup package by creating a unified framework for them.

We developed a web-based Shiny application to assist laboratory personnel in determining how well a group testing algorithm is expected to perform before implementation. The app utilizes binGroup2 functions to calculate the expected number of tests and diagnostic accuracy measures for a wide variety of algorithms using one- and two-disease assays. The OTC can be found with the app as well.

Most group testing research using one-disease assays makes the assumption of equal sensitivity and equal specificity values across all stages of testing. We present derivations of operating characteristics for group testing algorithms that allow the diagnostic test accuracy to differ across stages of testing. These resulting expressions are incorporated into the binGroup2 package.

Adviser: Christopher R. Bilder

]]>Adviser: Bertrand Clarke

]]>Historically, traditional optimal design theory focuses on univariate linear, nonlinear, and mixed models. There is no current literature on the subject of optimal design for a causal structure, therefore this research is the first contribution in the field. There are five objectives for this dissertation research. For a given causal structure, the objectives of this research are to obtain an optimal design: (1) For a completely randomized experiment that produces the most precise estimates for the endogenous and exogenous parameters, (2) For an experiment with random blocks or split-plots that produces the most precise estimates for the endogenous and exogenous parameters, (3) For an experiment with fixed blocks that produces the most precise estimates for the endogenous and exogenous parameters, (4) For an experiment with random blocks or split-plots that produces the most precise estimates for the endogenous parameters, exogenous parameters, and the variance components, and (5) Using the methods above to demonstrate the improvement in efficiency for two applications published in previous research.

In each case, the causal relationship dramatically changed the optimal designs. The new optimal designs were more efficient. Even orthogonal designs, which are universally optimal in the univariate case, are not optimal when considering a causal structure.

Advisor: Kent M. Eskridge

]]>Heart Rate Variability (HRV) has been used to study stressed induced reaction in humans, and mammal, in general, using non-linear analysis. Studies have also been done to establish synchrony in humans. Non-linear analysis of HRV using recurrence and cross recurrence plots, recurrence and cross recurrence quantification analysis, have also been done to study the feather pecking behavior in chickens. The main purpose of this study is to see if the human study on the degree of synchrony can be replicated for the avian population. If such synchrony exists in the avian population, then it will establish that the degree of synchrony is a primal instinct. Female leghorn chickens were used in the study as they have similar cardiac structure but are evolutionarily distant from mammals. If the presence of synchrony can be established for cagemate hens, it might lead to significant improvement in poultry well-being.

Advisers: Professors Kathryn J Hanford and Erin E Blankenship

]]>Research using VAM typically occurs in school systems with a large number of students (e.g. New York City, Los Angeles, Chicago, etc.) or in statewide assessments that are combined across school districts (e.g. Tennessee). VAM performance in school systems with small numbers of students is unknown.

One common issue with estimation based on small samples is lack of precision. An area of statistics that has developed methodology for small sample sizes is small area estimation. One approach in this area is indirect estimation which links similar subjects together allowing the small groups to “borrow strength” from each other.

This dissertation introduces a multi-stage model that incorporates small area estimation techniques with the traditional TVAAS. The performance of both the multi-stage and TVAAS models are studied through data simulated for small school systems. The precision of predicted teacher value added scores is assessed for both modeling methods.

Adviser: Walter W. Stroup, Erin E. Blankenship

]]>Two models based on breed specific haplotype clusters where developed to account for differences across multiple breeds. The first model utilizes the breed composition of the individual, while the second utilizes the breed composition from the sire and dam. Haplotype clusters were modeled as hidden states in a hidden Markov model where the genomic effects are associated with loci located on the unobserved clusters. Similar to the Bayes C model, we can model the genomic effects at the loci using a prior, which consists of a mixture of a multivariate normal and a point mass at zero distribution.

The performance of the first model will be evaluated in a composite beef cattle population, representing various fractions of several breeds, using five weight traits, seven carcass traits, and two other traits related to calving on 6,552 cattle genotyped for 99,827 mapped SNPs. The performance of the second model will be evaluated in a two-way cross population, which was a cross between two independent lines, using age of puberty records on 1,654 swine genotyped for 48,408 mapped SNPs. Both models will also be evaluated in a simulated composite population of two lines of 12,500 individuals and 61,255 mapped SNPs.

Overall, the breed specific haplotype models led to larger and more clearly observed estimated QTL. However, the prediction accuracy for the haplotype models were typically lower than those for the traditional Bayesian GWAS models. Therefore, while our ability to locate QTLs was increased, the traditional models are still the preferred choice for prediction as they have higher prediction accuracy when it comes to estimating an animal’s genetic merit.

]]>This dissertation consists of simulated investigations into frequentist and ethical properties of an new RAR biased coin design. Chapter 2 proposes a new adaptive design for phase III clinical trials, a modification of the 2001 Bandyopadhyay and Biswas biased coin design. Simulations show how the new design continues to ethically expose patients to the better treatment while simultaneously mitigating power loss inherent in the original design. Chapters 2 and 3 expand the applicability of the new design to scenarios where treatment variances or covariate-treatment impacts are unequal. In Chapter 4, simulations demonstrate that the new response-adaptive biased coin design can be more ethical than equal allocation, even when patient outcomes are not immediately available. Each chapter illustrates the utility and benefits of the new design through a real-world application of an HIV treatment adherence intervention. Asymptotic results are applied to a special case of the BBS design and small sample implications are compared with simulated outcomes in Chapter 5.

Adviser: Kent M. Eskridge

]]>Advisor: David B. Marx

]]>Advisor: Anne M. Parkhurst

]]>Adviser: David Marx

]]>Adviser: Walter W. Stroup

]]>Statistical methods are the main data analysis technique used for developing quantitative predictions in the life sciences, but these methods are rarely applied to long-term datasets because the methods are underdeveloped in most cases. This underdevelopment of statistical methods and applications was the motivation for my research. In Chapter 1, I develop a time series analysis method for populations that accounts for errors in detection. In Chapter 2, I develop and apply a variety of methods to predict an extinction threshold using long-term monitoring data from a population of bobwhite quail (*Colinus virginianus*). In Chapter 3, I link the unified framework of missing data developed in the statistical literature to species distribution modelling, which is a common method used to analyze historical location reports of a species. In Chapter 4 I introduce an example using location records of one of the rarest avian species in the world—the whooping crane (*Grus americana*). The whooping crane location records were imprecisely recorded, and in Chapter 4, I extend regression calibration methods to correct for the location error. In Chapter 5, I explore when a commonly used statistical estimation method will fail for analyses using historical location records; I then test several alternative estimation methods. Finally, in Chapter 6, I present an application by predicting the spatial and temporal distribution of whooping cranes using historical location records. This application was developed to determine what habitat is used by whooping cranes during migration and what habitat may require special protection to ensure survival of the species.

Advisors: Erin E. Blankenship and Richard A.J. Tyre

]]>A simulation study was conducted using a closed network made up of ten nodes and three different edge density values (low, moderate, and high) to randomly generate the edges (connections) between nodes. A Poisson AR(1) process was used to generate the number of communications between nodes at each time period. Changes were then randomly assigned in time periods 26 and 52, and the aR^{2}’s calculated between adjacent time periods. A separate simulation was conducted for each combination of edge density (3 levels), AR(1) correlation parameter (3 levels), number of edges perturbed (3 levels), perturbation factor (3 levels), time period of perturbation (2 levels), and configuration dimension (2 levels). The results suggest that under these conditions the method as proposed has reasonable power for detecting “abnormal” changes in the number of communications.

Adviser: David B. Marx

]]>