Physical Sciences and Mathematics Commons^™

Prevalence Of Sars-Cov-2 Antibodies In Liberty University Student Population, Emily Bonus

Senior Honors Theses

In 2020, the virus SARS-CoV-2 gained attention as it spread around the world. Its antibodies are poorly understood, and little research focuses on those with few COVID-19 complications yet large numbers of close contacts: university students. This longitudinal study recorded SARS-CoV-2 antibody presence in 107 undergraduate Liberty University students twice during early 2021. After extensive data cleaning and the application of various statistical tests and ANOVAs, the data seems to show that in the case of COVID-19 infections, SARS-CoV-2 IgM antibodies are immediately produced, and then IgG antibodies follow later. However, the COVID-19 vaccine causes the production of both IgM …

Medical Outcomes, Quality Of Life, And Family Perceptions For Outpatient Vs Inpatient Neutropenia Management After Chemotherapy For Pediatric Acute Myeloid Leukemia, Kelly D Getz, Julia E Szymczak, Yimei Li, Rachel Madding, Yuan-Shung V Huang, Catherine Aftandilian, Staci D Arnold, Kira O Bona, Emi Caywood, Anderson B Collier, M Monica Gramatges, Meret Henry, Craig Lotterman, Kelly Maloney, Amir Mian, Rajen Mody, Elaine Morgan, Elizabeth A Raetz, Jeffrey Rubnitz, Anupam Verma, Naomi Winick, Jennifer J Wilkes, Jennifer C Yu, Brian T Fisher, Richard Aplenc

Department of Medicine Faculty Papers

Importance: Pediatric acute myeloid leukemia (AML) requires multiple courses of intensive chemotherapy that result in neutropenia, with significant risk for infectious complications. Supportive care guidelines recommend hospitalization until neutrophil recovery. However, there are little data to support inpatient over outpatient management.

Objective: To evaluate outpatient vs inpatient neutropenia management for pediatric AML.

Design, setting, and participants: This cohort study used qualitative and quantitative methods to compare medical outcomes, patient health-related quality of life (HRQOL), and patient and family perceptions between outpatient and inpatient neutropenia management. The study included patients from 17 US pediatric hospitals with frontline chemotherapy start dates ranging …

Estimation Of Exposure Distribution Adjusting For Association Between Exposure Level And Detection Limit, Yuchen Yang, Brent J. Shelton, Thomas Tucker, Li Li, Richard Kryscio, Li Chen

Statistics Faculty Publications

In environmental exposure studies, it is common to observe a portion of exposure measurements to fall below experimentally determined detection limits (DLs). The reverse Kaplan–Meier estimator, which mimics the well‐known Kaplan–Meier estimator for right‐censored survival data with the scale reversed, has been recommended for estimating the exposure distribution for the data subject to DLs because it does not require any distributional assumption. However, the reverse Kaplan–Meier estimator requires the independence assumption between the exposure level and DL and can lead to biased results when this assumption is violated. We propose a kernel‐smoothed nonparametric estimator for the exposure distribution without imposing …

Distance-Based Analysis Of Variance For Brain Connectivity, Russell T. Shinohara, Haochang Shou, Marco Carone, Robert Schultz, Birkan Tunc, Drew Parker, Ragini Verma

UPenn Biostatistics Working Papers

The field of neuroimaging dedicated to mapping connections in the brain is increasingly being recognized as key for understanding neurodevelopment and pathology. Networks of these connections are quantitatively represented using complex structures including matrices, functions, and graphs, which require specialized statistical techniques for estimation and inference about developmental and disorder-related changes. Unfortunately, classical statistical testing procedures are not well suited to high-dimensional testing problems. In the context of global or regional tests for differences in neuroimaging data, traditional analysis of variance (ANOVA) is not directly applicable without first summarizing the data into univariate or low-dimensional features, a process that may …

Binomial Regression With A Misclassified Covariate And Outcome., Sheng Luo, Wenyaw Chan, Michelle A Detry, Paul J Massman, R S. Doody

Faculty Publications

Misclassification occurring in either outcome variables or categorical covariates or both is a common issue in medical science. It leads to biased results and distorted disease-exposure relationships. Moreover, it is often of clinical interest to obtain the estimates of sensitivity and specificity of some diagnostic methods even when neither gold standard nor prior knowledge about the parameters exists. We present a novel Bayesian approach in binomial regression when both the outcome variable and one binary covariate are subject to misclassification. Extensive simulation results under various scenarios and a real clinical example are given to illustrate the proposed approach. This approach …

Statistical Handling Of Medical Data - An Ethical Perspective, Ajay Kumar Bansal Dr

COBRA Preprint Series

Medical Science is a delicate subject and the clinical data generated from the medical trials must be reliable and of good quality. Not only the quality of generated data is important, but the management is also crucial and is to be handled very carefully. In this paper, the ethical aspect of statistical handling of such data is discussed.

Every profession has some set of norms to follow to achieve its objectives. These norms are called professional ethics which shows the essence of human behaviour. Same way, the field of medical research is expected to follow ethical norms, to obtain reliable …

Evaluation Of Some Statistical Methods For The Identification Of Differentially Expressed Genes, Andrew L. Haddon

FIU Electronic Theses and Dissertations

Microarray platforms have been around for many years and while there is a rise of new technologies in laboratories, microarrays are still prevalent. When it comes to the analysis of microarray data to identify differentially expressed (DE) genes, many methods have been proposed and modified for improvement. However, the most popular methods such as Significance Analysis of Microarrays (SAM), samroc, fold change, and rank product are far from perfect. When it comes down to choosing which method is most powerful, it comes down to the characteristics of the sample and distribution of the gene expressions. The most practiced method is …

Normalization Techniques For Statistical Inference From Magnetic Resonance Imaging, Russell T. Shinohara, Elizabeth M. Sweeney, Jeff Goldsmith, Navid Shiee, Farrah J. Mateen, Peter A. Calabresi, Samson Jarso, Dzung L. Pham, Daniel S. Reich, Ciprian M. Crainiceanu

UPenn Biostatistics Working Papers

While computed tomography and other imaging techniques are measured in absolute units with physical meaning, magnetic resonance images are expressed in arbitrary units that are difficult to interpret and differ between study visits and subjects. Much work in the image processing literature on intensity normalization has focused on histogram matching and other histogram mapping techniques, with little emphasis on normalizing images to have biologically interpretable units. Furthermore, there are no formalized principles or goals for the crucial comparability of image intensities within and across subjects. To address this, we propose a set of criteria necessary for the normalization of images. …

Computationally Efficient Confidence Intervals For Cross-Validated Area Under The Roc Curve Estimates, Erin Ledell, Maya L. Petersen, Mark J. Van Der Laan

U.C. Berkeley Division of Biostatistics Working Paper Series

In binary classification problems, the area under the ROC curve (AUC), is an effective means of measuring the performance of your model. Most often, cross-validation is also used, in order to assess how the results will generalize to an independent data set. In order to evaluate the quality of an estimate for cross-validated AUC, we must obtain an estimate for its variance. For massive data sets, the process of generating a single performance estimate can be computationally expensive. Additionally, when using a complex prediction method, calculating the cross-validated AUC on even a relatively small data set can still require a …

Hypothesis Testing And Power Calculations For Taxonomic-Based Human Microbiome Data, P. S. Larossa, J. Paul Brooks, Elena Deych, Edward L. Boone, David J. Edwards, Qin Wang, Erica Sodergren, George Weinstock, William D. Shannon

Statistical Sciences and Operations Research Publications

This paper presents new biostatistical methods for the analysis of microbiome data based on a fully parametric approach using all the data. The Dirichlet-multinomial distribution allows the analyst to calculate power and sample sizes for experimental design, perform tests of hypotheses (e.g., compare microbiomes across groups), and to estimate parameters describing microbiome properties. The use of a fully parametric model for these data has the benefit over alternative non-parametric approaches such as bootstrapping and permutation testing, in that this model is able to retain more information contained in the data. This paper details the statistical approaches for several tests of …