Statistics and Probability Commons^™

Integrated Profiling Identifies Itgb3bp As Prognostic Biomarker For Hepatocellular Carcinoma, Qiuli Liang, Chao Tan, Feifei Xiao, Fuqiang Yin, Meiliang Liu, Lei Lei, Liuyu Wu, Yu Yang, Jennifer Hui Juan Tan, Shun Liu, Xiaoyun Zeng

Faculty Publications

Hepatocellular carcinoma (HCC) is a highly malignant tumor. In this study, we sought to identify a novel biomarker for HCC by analyzing transcriptome and clinical data. The R software was used to analyze the differentially expressed genes (DEGs) in the datasets GSE74656 and GSE84598 downloaded from the Gene Expression Omnibus database, followed by a functional annotation. A total of 138 shared DEGs were screened from two datasets. They were mainly enriched in the “Metabolic pathways” pathway (Padj = 8.21E-08) and involved in the carboxylic acid metabolic process (Padj = 0.0004). The top 10 hub genes were found by protein-protein interaction …

Time-Varying Insomnia Symptoms And Incidence Of Cognitive Impairment And Dementia Among Older Us Adults, Nicholas V. Resciniti, Valerie Yelverton, Bezawit Eyob Kase, Jiajia Zhang, Matthew C. Lohman

Faculty Publications

There is conflicting evidence regarding the association between insomnia and the onset of mild cognitive impairment (MCI) or dementia. This study aimed to evaluate if time-varying insomnia is associated with the development of MCI and dementia. Data from the Health and Retirement Study (n = 13,833) from 2002 to 2014 were used (59.4% female). The Brief Insomnia Questionnaire was used to identify insomnia symptoms which were compiled in an insomnia severity index, ranging from 0 to 4. In analysis, participants’ symptoms could vary from wave-to-wave. Dementia was defined using results from the Health and Retirement Study (HRS) global cognitive …

Toward Uncharted Territory Of Cellular Heterogeneity: Advances And Applications Of Single-Cell Rna-Seq, Brandon Lieberman, Meena Kusi, Chia Nung Hung, Chih Wei Chou, Ning He, Yen Yi Ho, Josephine A. Taverna, Tim H.M. Huang, Chun Liang Chen

Faculty Publications

Among single-cell analysis technologies, single-cell RNA-seq (scRNA-seq) has been one of the front runners in technical inventions. Since its induction, scRNA-seq has been well received and undergone many fast-paced technical improvements in cDNA synthesis and amplification, processing and alignment of next generation sequencing reads, differentially expressed gene calling, cell clustering, subpopulation identification, and developmental trajectory prediction. scRNA-seq has been exponentially applied to study global transcriptional profiles in all cell types in humans and animal models, healthy or with diseases, including cancer. Accumulative novel subtypes and rare subpopulations have been discovered as potential underlying mechanisms of stochasticity, differentiation, proliferation, tumorigenesis, and …

Nutritional Approach For Increasing Public Health During Pandemic Of Covid-19: A Comprehensive Review Of Antiviral Nutrients And Nutraceuticals, Vahideh Ebrahimzadeh-Attari, Ghodratollah Panahi, James R. Hébert Scd, Alireza Ostadrahimi, Maryam Saghafi-Asl, Neda Lotfi-Yaghin, Behzad Baradaran

Faculty Publications

Background: The novel coronavirus (COVID-19) is considered as the most life-threatening pandemic disease during the last decade. The individual nutritional status, though usually ignored in the management of COVID-19, plays a critical role in the immune function and pathogenesis of infection. Accordingly, the present review article aimed to report the effects of nutrients and nutraceuticals on respiratory viral infections including COVID-19, with a focus on their mechanisms of action.

Methods: Studies were identified via systematic searches of the databases including PubMed/ MEDLINE, ScienceDirect, Scopus, and Google Scholar from 2000 until April 2020, using keywords. All relevant clinical and experimental studies …

Association Between Dietary Inflammatory Index And Type 2 Diabetes Mellitus In Xinjiang Uyghur Autonomous Region, China, Wenhui Fu, Hualian Pei, Nitin Shivaooa, James R. Hébert, Tao Luo, Tian Tian, Dilibaier Alimu, Zewen Zhang, Jianghong Dai

Faculty Publications

Background Diet and inflammation have both been studied in relation to type 2 diabetes mellitus (T2DM). The aim of this cross-sectional study was to examine the association between the Dietary Inflammatory Index (DII®) and T2DM. Methods Subjects were adults enrolled in the baseline study of the Xinjiang multi-ethnic natural population cohort and health follow-up study from January to May 2019. The study involved 5,105 subjects (58.7% men) between 35 and 74 years of age. The DII score was calculated from a data obtained via a food frequency questionnaire consisting of 127 food items. Results Logistic regression analyses were used to …

Estimation And Inference Under Model Uncertainty, Yizheng Wei

Theses and Dissertations

Chapter 1 of this dissertation proposes a consistent and locally efficient estimator to estimate the model parameters for a logistic mixed effect model with random slopes. Our approach relaxes two typical assumptions: the random effects being normally distributed, and the covariates and random effects being independent of each other. Adhering to these assumptions is particularly difficult in health studies where in many cases we have limited resources to design experiments and gather data in long-term studies, while new findings from other fields might emerge, suggesting the violation of such assumptions. So it is crucial if we could have an estimator …

Categorical And Fuzzy Ensemble-Based Algorithms For Cluster Analysis, Bridget Nicole Manning

Theses and Dissertations

This dissertation focuses on improving multivariate methods of cluster analysis. In Chapter 3 we discuss methods relevant to the categorical clustering of tertiary data while Chapter 4 considers the clustering of quantitative data using ensemble algorithms. Lastly, in Chapter 5, future research plans are discussed to investigate the clustering of spatial binary data.

Cluster analysis is an unsupervised methodology whose results may be influenced by the types of variables recorded on observations. When dealing with the clustering of categorical data, solutions produced may not accurately reflect the structure of the process that generated them. Increased variability within the latent structure …

Incorporation And Measurement Of Uncertainty In Clustered And Spatial Data, Yuan Hong

Theses and Dissertations

Analyzing population representative datasets for local estimation and predictions over time is important for monitoring related public health issues, however, there are many statistical challenges associated with such analyses. Mixed effect models are one of the common options which can incorporate time and spatial effect in the model and related inference is well established.

In the first part of this dissertation, to estimate area-level prevalence using individuallevel data, small area estimation (SAE) with post-stratified mixed effect models were used where sampling weights were also incorporated into it. However, if poststratification which requires more computation effort can improve estimation accuracy is …

The Practical Advantages And Disadvantages Of Laplace Regression As An Alternative To Cox Proportional Hazards Model: A Comparison Via Simulation, Sydney Smith

Theses and Dissertations

The Cox proportional hazards model is the most common regression technique for survival analysis. However, the proportional hazards assumption restricts it’s use to a limited group of multiplicative models. Laplace regression is a flexible quantile regression technique for censored observations that is appropriate in a wider variety of applications as compared to the Cox proportional hazards model. Instead of estimating a hazard ratio, Laplace regression which is free from a proportionality assumption, can be used to estimate many adjusted percentiles of survival time allowing for a more complete description of the association of interest. This paper compares the performance of …

The Arabidopsis Transcription Factor Aintegumenta Orchestrates Patterning Genes And Auxin Signaling In The Establishment Of Floral Growth And Form, Beth A. Krizek, Ivory C. Blakley, Yen Yi Ho, Nowlan Freese, Ann E. Loraine

Faculty Publications

Understanding how flowers form is an important problem in plant biology, as human food supply depends on flower and seed production. Flower development also provides an excellent model for understanding how cell division, expansion and differentiation are coordinated during organogenesis. In the model plant Arabidopsis thaliana, floral organogenesis requires AINTEGUMENTA (ANT) and AINTEGUMENTA-LIKE 6 (AIL6)/PLETHORA 3 (PLT3), two members of the Arabidopsis AINTEGUMENTA-LIKE/PLETHORA (AIL/PLT) transcription factor family. Together, ANT and AIL6/PLT3 regulate aspects of floral organogenesis, including floral organ initiation, growth, identity specification and patterning. Previously, we used RNA-Seq to identify thousands of genes with disrupted expression in ant ail6 …

Network-Based Statistical Analysis Of Functional Magnetic Resonance Imaging Data From Aphasia Patients, Xingpei Zhao

Theses and Dissertations

Functional magnetic resonance imaging (fMRI) is a neuroimaging technique that provides insight into brain function and activity. Network models of fMRI signals can reveal functional connectivity related to certain brain disorders, such as post-stroke aphasia. This thesis aims to identify the functional connections that distinguish anomic and Broca’s aphasia by comparing the resting-state fMRI from the patients with these two types of aphasia. The network-based statistic (NBS) approach is used to detect such connections. After the analytic pipeline is applied to the fMRI data, the NBS approach identifies a distinct subnetwork between the two types of aphasia, which involves the …

Bayesian Zero-Inflated Model For Ordinal Data, Huizhong Yang

Theses and Dissertations

Datasets with a relatively large number of zeros is commonly seen in medical applications. Although models like Zero-inflated Poisson (ZIP) model are proposed for counts data, there is still some issues with ordinal data which have excess zeros. In this paper, we developed a Bayesian approach to accommodate the excess zero in ordinal data. Intellectual disability (ID), also known as mental retardation (MR), is a disability characterized by below-average intelligence or mental ability and a lack of the learning necessary skills for daily life. A person with intellectual disability has intellectual functioning and adaptive behaviors limitations. Intellectual disability is a …

High-Dimensional Inference Based On The Leave-One-Covariate-Out Regularization Path, Xiangyang Cao

Theses and Dissertations

The increasingly rapid emergence of high dimensional data, where the number of variables p may be larger than the sample size n, has necessitated the development of new statistical methodologies. LASSO and variants of LASSO are proposed and have been the most popular estimators for the high dimensional regression models. However, not much work has focused on analyzing and summarizing the information contained in the entire solution path of the LASSO. This dissertation consists of three research projects that propose and extend the Leave-One-Covariate-Out(LOCO) solution path statistic to regression and graphical models.

In the first chapter, we propose a new …

Semiparametric Regression Analysis Of Survival Data And Panel Count Data, Lu Wang

Theses and Dissertations

Both censored survival data and panel count data arise commonly in real-life studies in many fields such as epidemiology, social science, and medical research. In these studies, subjects are usually examined multiple times at periodical or irregular follow-up examinations. Censored data are studied when the exact failure times of the events are of interest but not all of these exact times are directly observed. Some of the failure times of event of interest are only known to fall within some intervals formed by the observation times. Panel count data are under investigation when the exact times of the recurrent events …

Boom Or Bust: Examining The Relationship Between High School Recruiting Rankings And The Nfl Draft, Nicholas E. Tice

Senior Theses

The goal of this thesis is to model the probability of a high school football player’s chance of being drafted based on information taken from their recruiting profile. The response variable is binary and defined as drafted (1) or undrafted (0). The independent variables were collected by scraping data from the recruiting websites including height, weight, position, hometown, recruiting grade and other socioeconomic factors based on the player’s high school. 247Sports and ESPN were the two recruiting services used and compared in this study. Because of the binary nature of the dependent variable, logistic regression and decision trees were chosen …

Preparing For The Future: The Effects Of Financial Literacy On Financial Planning For Young Professionals, Tanay Singh

Senior Theses

Purpose – Many people between the age of 20 and 34 have not considered planning financially for the future in any significant capacity and in doing so, they limit their potential savings. The purpose of this study is to examine what financial expectations are for people in the early stages of their career and determine if improving financial literacy and revealing financial realities helps to produce more accurate or realistic expectations. Ultimately, the goal is to better prepare participants in the study for the working world and increased responsibilities outside of the college/university environment by getting them to start thinking …

Multivariate Joint Models And Dynamic Predictions, Md Akhtar Hossain

Theses and Dissertations

The joint modeling of longitudinal and time-to-event data is an active area of statistical research that has received a lot of attention. The standard joint models, referred to as univariate joint models, allow simultaneous modeling of a single longitudinal outcome and a single time-to-event under an assumption of independent censoring. The majority of the joint modeling research in the last two decades has focused on extending and improving the univariate joint models. While many of the practical applications involve data on multivariate longitudinal outcomes and multiple timeto- events possibly informatively censored by some other terminal time-to-event, the developments of joint …

Studies Of Group Fused Lasso And Probit Model For Right-Censored Data, Tuan Quoc Do

Theses and Dissertations

This document is composed of three main chapters. In the first chapter, we study the mixture of experts, a powerful machine learning model in which each expert handles a different region of the covariate space. However, it is crucial to choose an appropriate number of experts to avoid overfitting or underfitting. A group fused lasso (GFL) term is added to the model with the goal of making the coefficients of the experts and the gating network closer together. An algorithm to optimize the problem is also developed using block-wise coordinate descent in the dual counterpart. Numerical results on simulated and …

Flexible Regression Models For Survival Data, Ennan Gu

Theses and Dissertations

Survival analysis is a branch of statistics to analyze the time-to-event data or survival data. One important feature of survival data is censoring, which means that not all the subjects’ survival time are observed directly. Among all the survival data, right-censored data are the most common type and consist of some exactly observed survival times and some right-censored observations. In this dissertation, we focus on studying flexible regression models for complicated right-censored survival data when the classical proportional hazards (PH) assumption is not satisfied. Flexible semiparametric regression models can largely avoid misspecification of parametric distributions and thus provide more modeling …

Bayesian Analysis Of Binary Diagnostic Tests And Panel Count Data, Chunling Wang

Theses and Dissertations

This dissertation mainly explores several challenging topics that arise in diagnostic tests and panel count data in the Bayesian framework. Binary diagnostic tests, particularly multiple diagnostic tests with repeated measures and diagnostic procedures with a large number of raters, are studied. For panel count data, most traditional methods only handle panel count data for a single type of recurrent event. In this dissertation, we primarily focus on the case with multiple types of recurrent events.

In Chapter 1, an introduction to the binary diagnostic tests data and panel count data is presented and related literature works are briefly reviewed. To …

An Empirical Comparison Of Machine Learning Models For Classification, Nubaira Rizvi

Theses and Dissertations

Classification problems are tackled across various industries throughout multiple disciplines. A model used for classification attempts to predict the class of an outcome variable based on some predictors. There are number of classification models available. But as the underlying population distribution of the predictors is always unknown it is difficult to know which model fits the situation best. Several studies have been done on which supervised model performs better given specific datasets. But little work has been done to compare the models’ performance for predicting one or more outcomes under multivariate settings.

This study compares the performance of seven popular …

Validity Study Of The R-Pla, A Resilience Scale For People Living With Hiv, Jinxiang Hu, Julianne M. Serovich, Monique J. Brown, Judy A. Kimberly, Yi-Hsin Chen

Faculty Publications

This study provides psychometric assessment of a resilience scale with a sample of women living with HIV. Baseline data were used from a longitudinal HIV disclosure study of 124 women aged between 18-63 collected between 2001 and 2004 in a large Midwestern city. The Rasch model was used to examine the psychometric properties of the resilience scale. Results indicated that the resilience instrument meets the Rasch model application assumptions. Evidence of validity suggested the resilience instrument demonstrated good item and person fit, as well as good item and person reliability. Most items showed measurement invariance across different age and racial …

Chorioamnionitis: Case Definition & Guidelines For Data Collection, Analysis, And Presentation Of Immunization Safety Data, Alisa Kachikis, Linda O. Eckert, Christie Walker, Azucena Bardají, Frederick Varricchio, Heather S. Lipkind, Khady Diouf, Wan Ting Huang, Ronald Mataya, Mustapha Bittaye, Clare Cutland, Nansi S. Boghossian, Tamala Mallett Moore, Rebecca Mccall, Jay King, Shuchita Mundle, Flor M. Munoz, Caroline Rouse, Michael Gravett, Lakshmi Katikaneni, Kevin Ault, Nicola P. Klein, Drucilla J. Roberts, Sonali Kochhar, Nancy Chescheir

Faculty Publications

No abstract provided.

Neurodevelopmental Delay: Case Definition & Guidelines For Data Collection, Analysis, And Presentation Of Immunization Safety Data, Adrienne N. Villagomez, Flor M. Muñoz, Robin L. Peterson, Alison M. Colbert, Melissa Gladstone, Beatriz Macdonald, Rebecca Wilson, Lee Fairlie, Gwendolyn J. Gerner, Jackie Patterson, Nansi S. Boghossian, Vera Joanna Burton, Margarita Cortés, Lakshmi D. Katikaneni, Jennifer C.G. Larson, Abigail S. Angulo, Jyoti Joshi, Mirjana Nesin, Michael A. Padula, Sonali Kochhar, Amy K. Connery

Faculty Publications

No abstract provided.

Time Series Analysis Of Weather Data In South Carolina, Geophrey Odero

Theses and Dissertations

This thesis discusses time series analysis of weather data in South Carolina for the last fifteen years (January 2003 to December 2017) for Columbia, Greenville and North Myrtle Beach. The first part presents a brief overview of different variables that are used in the analysis. That is, temperature, dew point, humidity and sea level pressure. A short discussion of time series data is also introduced. The second part is about modeling the variables. The models of choice are presented, fitted and model diagnostics is carried out. In the third part, we discuss background on climates of the cities and model …

Dietary Inflammatory Index And Non-Communicable Disease Risk: A Narrative Review, Catherine M. Phillips, Ling-Wei Chen, Barbara Heude, Jonathan Y. Bernard, Nicholas C. Harvey, Liesbeth Duijts, Sara M. Mensink-Bout, Kinga Polanska, Giulia Mancano, Matthew Suderman, Nitin Shivappa, James R. Hébert

Faculty Publications

There are over 1,000,000 publications on diet and health and over 480,000 references on inflammation in the National Library of Medicine database. In addition, there have now been over 30,000 peer-reviewed articles published on the relationship between diet, inflammation, and health outcomes. Based on this voluminous literature, it is now recognized that low-grade, chronic systemic inflammation is associated with most non-communicable diseases (NCDs), including diabetes, obesity, cardiovascular disease, cancers, respiratory and musculoskeletal disorders, as well as impaired neurodevelopment and adverse mental health outcomes. Dietary components modulate inflammatory status. In recent years, the Dietary Inflammatory Index (DII^®), a literature-derived …

Increased Dietary Inflammatory Index Is Associated With Schizophrenia: Results Of A Case–Control Study From Bahrain, Haitham Jahrami, Moez Al-Islam Faris, Hadeel Ghazzawi, Zahra Saif, Layla Habib, Nitin Shivappa, James R. Hébert

Faculty Publications

Background: Several studies have indicated that chronic low-grade inflammation is associated with the development of schizophrenia. Given the role of diet in modulating inflammatory markers, excessive caloric intake and increased consumption of pro-inflammatory components such as calorie-dense, nutrient-sparse foods may contribute toward increased rates of schizophrenia. This study aimed to examine the association between dietary inflammation, as measured by the dietary inflammatory index (DII^®), and schizophrenia. Methods: A total of 120 cases attending the out-patient department in the Psychiatric Hospital/Bahrain were recruited, along with 120 healthy controls matched on age and sex. The energy-adjusted DII (E-DII) was computed …

Dietary Inflammatory Index And Its Relationship With Cervical Carcinogenesis Risk In Korean Women: A Case-Control Study, Sundara Raj Sreeja, Hyun Yi Lee, Minji Kwon, Nitin Shivappa, James R. Hébert, Mi Kyung Kim

Faculty Publications

Several studies have reported that diet’s inflammatory potential is related to chronic diseases such as cancer, but its relationship with cervical cancer risk has not been studied yet. The aim of this study was to investigate the association between Dietary Inflammatory Index (DII^®) and cervical cancer risk among Korean women. This study consisted of 764 cases with cervical intraepithelial neoplasia (CIN)1, 2, 3, or cervical cancer, and 729 controls from six gynecologic oncology clinics in South Korea. The DII was computed using a validated semiquantitative Food Frequency Questionnaire (FFQ). Odds ratios and 95% CI were calculated using multinomial …

Investigations On Multiple Interval Estimators, Taeho Kim

Theses and Dissertations

Multiple interval estimation for a set of parameters is investigated. To begin, a strategy of optimization for a multiple interval estimator (MIE) is introduced. This approach allocates distinct optimized levels to individual interval estimators so that the global expected content can be minimized while the global coverage probability is still maintained at a global level. This optimal allocation is achieved by a decision theoretic procedure which consists of two global risk functions. The major part of this manuscript is devoted to two multiple interval estimation procedures. Both procedures adopt prior information added to the classical setting, but these procedures do …

Statistical Analysis Of Interval-Censored Data Subject To Additional Complications, Qiang Zheng

Theses and Dissertations

Survival analysis is an important branch of statistics that studies time to event data (or survival data), in which the response variable is time to a certain event of interest. The most prominent feature of survival data is that the response is not exactly observed due to limits of the study design or nature of the event of interest. Interval-censored data are a common type of survival data and occur frequently in real life studies where subjects are examined at periodical follow ups. The response time is usually not observed, but the status of the event of interest is known …