Statistics and Probability Commons^™

The South Carolina Safety Belt Study: Large-Scale Location Sampling, Stephanie Jones

Theses and Dissertations

The South Carolina Safety Belt Study is a statewide survey completed yearly to assess the prevalence of safety belt usage on of South Carolina roads through observations from different locations across the state. Every five years the sites for observation are resampled. This thesis breaks down the most recent sampling done for the years of 2018 through 2022. Both the methodology of large scale location sampling and the mathematical idea behind the strategy employed are covered. Further, three different software packages were utilized: R, SAS, and ArcGIS. The steps that were taken and the written function code run for each …

Comparison Of The Performance Of Simple Linear Regression And Quantile Regression With Non-Normal Data: A Simulation Study, Marjorie Howard

Theses and Dissertations

Linear regression is a widely used method for analysis that is well understood across a wide variety of disciplines. In order to use linear regression, a number of assumptions must be met. These assumptions, specifically normality and homoscedasticity of the error distribution can at best be met only approximately with real data. Quantile regression requires fewer assumptions, which offers a potential advantage over linear regression. In this simulation study, we compare the performance of linear (least squares) regression to quantile regression when these assumptions are violated, in order to investigate under what conditions quantile regression becomes the more advantageous method …

Semiparametric Regression In The Presence Of Measurement Error, Xiang Li

Theses and Dissertations

The error-in-covariates problem has received great attention among researchers who study semiparametric and nonparametric inference for regression models over the past two decades. Without correcting for the measurement error in covariates, estimators for covariate effect usually contain bias. To account for measurement error, much research have been done in mean regression (Liang et al., 1999; Fuller, 2009; Carroll et al., 2006) and quantile regression (He and Liang, 2000; Hardle et al., 2000; Wei and Carroll, 2009). In contrast, there is little research in mode regression and this motivates us to propose semiparametric methods to address this error-incovariates problem in Chapters …

Bayesian Semiparametric Methods For Analyzing Panel Count Data, Jianhong Wang

Theses and Dissertations

Panel count data commonly arise in epidemiological, social science, medical studies, in which subjects have repeated measurements on the recurrent events of interest at different observation times. Since the subjects are not under continuous monitoring, the exact times of those recurrent events are not observed but the counts of such events within the adjacent observation times are known. Panel count data can be considered as a special type of longitudinal data with a count response variable in the literature. Compared to the frequentist literature, very limited Bayesian approaches have been developed to analyze panel count data. In this dissertation, several …

Semiparametric Statistical Estimation And Inference With Latent Information, Qianqian Wang

Theses and Dissertations

In Chapter 1, we predicted disease risk by transformation models in the presence of missing subgroup identifiers. When a discrete covariate defining subgroup membership is missing for some of the subjects in a study, the distribution of the outcome follows a mixture distribution of the subgroup-specific distributions. Taking into account the uncertain distribution of the group membership and the covariates, we model the relation between the disease onset time and the covariates through transformation models in each sub-population, and develop a nonparametric maximum likelihood based estimation implemented through EM algorithm along with its inference procedure. We further propose methods to …

Estimation Procedures For Complex Survival Models And Their Applications In Epidemiology Studies, Jie Zhou

Theses and Dissertations

In this dissertation, we aim to address three important questions in practice, which can be solved through complex survival models. The first project focuses on studying the longitudinal fitness effect on cardiovascular disease (CVD) mortality. In the second project, we study the disease-death relation between CVD and all-cause mortality and evaluate important covariate effects on the disease or death transitions. In the third project, we compare antiretroviral treatment (ART) for HIV patients and consider both treatment effect and side effect of the drugs. The first two projects are motivated by the Aerobics Center Longitudinal Study (ACLS) datasets and the third …

Classification Of High-Dimensional Data Based On Multiple Testing Methods, Chong Ma

Theses and Dissertations

Supervised and unsupervised classification are common topics in machine learning in both scientific and industrial fields, which usually involve three tasks: prediction, exploration, and explanation. False discovery rate (FDR) theory has a close connection to classical classification theory, which must be employed in a sophisticated way to achieve good performance in various contexts. The study aims to explore novel supervised classifiers and unsupervised classification approaches for functional data and high-dimensional data in genome study by using FDR, respectively. One work develops a novel classifier for functional data by casting the classification problem into a multiple testing task, which involves using …

Fetal Growth Restriction: Case Definition & Guidelines For Data Collection, Analysis, And Presentation Of Immunization Safety Data, Sarah Rae Easter, Linda O. Eckert, Nansi Boghossian, Rebecca Spencer, Eugene Oteng-Ntim, Christos Ioannou, Manasi Patwardhan, Margo S. Harrison, Asma Khalil, Michael Gravett, Robert Goldenberg, Alastair Mckelvey, Manish Gupta, Vitali Pool, Stephen C. Robson, Jyoti Joshi, Sonali Kochhar, Tom Mcelrath

Faculty Publications

No abstract provided.

Small For Gestational Age: Case Definition & Guidelines For Data Collection, Analysis, And Presentation Of Maternal Immunisation Safety Data, Elizabeth P. Schlaudecker, Flor M. Munoz, Azucena Bardají, Nansi S. Boghossian, Asma Khalil, Hatem Mousa, Mirjana Nesin, Muhammad Imran Nisar, Vitali Pool, Hans M.L. Spiegel, Milagritos D. Tapia, Sonali Kochhar, Steven Black

Faculty Publications

No abstract provided.

Dietary Inflammatory Index And Colorectal Cancer Risk – A Meta-Analysis, Nitin Shivappa, Justyna Godos, James R. Hébert, Michael David Wirth, Gabriele Piuri, Attilio Speciani, Giuseppe Grosso

Faculty Publications

Diet and chronic inflammation of the colon have been suggested to be risk factors in the development of colorectal cancer (CRC). The possible link between inflammatory potential of diet, measured through the Dietary Inflammatory Index (DII®), and CRC has been investigated in several populations across the world. The aim of this study was to conduct a meta-analysis on studies exploring this association. Data from nine studies were eligible, of which five were case-control and four were cohort studies. Results from meta-analysis showed a positive association between increasing DII scores, indicating a pro-inflammatory diet, and CRC. Individuals in the highest versus …

The Relationship Of Plasma Trans Fatty Acids With Dietary Inflammatory Index Among Us Adults, Mohsen Mazidi, Hong-Kai Gao, Nitin Shivappa, Michael David Wirth, James R. Hébert, Andre Pascal Kengne

Faculty Publications

Background: It has been suggested that trans fatty acids (TFAs) play an important role in cardiovascular diseases. We investigated the association between plasma TFAs and the dietary inflammatory index (DII) ™ in US adults.

Methods: National Health and Nutrition Examination Survey (NHANES) participants with data on plasma TFAs measured from 1999 to 2010 were included. Energy-adjusted-DII ™ (E-DII ™) expressed per 1000 kcal was calculated from 24-h dietary recalls. All statistical analyses accounted for the survey design and sample weights.

Results: Of the 5446 eligible participants, 46.8% (n = 2550) were men. The mean age of the population was 47.1 …

Bayesian Flexible Modeling Of Interval-Censored Failure Time Data, Sheng-Yang Wang

Theses and Dissertations

Interval-censored data are a special type of survival data, in which the survival time is not accurately observed but known to fall within a specific time interval. Interval censored data commonly arise in real-life epidemiological and medical studies that involve periodic examinations. In this dissertation, several semi-parametric regression models are investigated to provide flexible modeling and robust inference for interval censored data from Bayesian perspectives.

Chapter 1 provides a detailed description about interval-censored data and gives several examples. Existing models and methods for analyzing such interval-censored data are reviewed as well. Chapter 2 develops a unified Bayesian estimation approach under …

Topics In Group Testing With Multiple Infections, Peijie Hou

Theses and Dissertations

Group testing, dating back to the early 1940s, was first proposed to screen for syphilis among US inductees during World War II (Dorfman, 1943). Since then, the benefits of reducing testing costs by employing group testing have been demonstrated in many areas, such as drug discovery, genetics, and infectious disease testing. Traditionally, statistical research in group testing has largely been motivated by applications involving a single infection. With the recent development of multiplex assays that can diagnose multiple infections simultaneously, generalizing the existing group testing literature to incorporate multiple infections is a natural and necessary next step. This dissertation consists …

Pre-Pregnancy Body Mass Index Is Associated With Dietary Inflammatory Index And C-Reactive Protein Concentrations During Pregnancy, Dayeon Shin, Junguk Hur, Eun-Hee Cho, Hae-Kyung Chung, Nitin Shivappa, Michael D. Wirth, James R. Hébert, Kyung Won Lee

Faculty Publications

There have been a limited number of studies examining the association between pre-pregnancy body mass index (BMI) and dietary inflammation during pregnancy. Our aim is to examine the association between pre-pregnancy BMI and the Dietary Inflammatory Index (DII)™ and C-reactive protein (CRP) concentrations during pregnancy. The study included 631 pregnant American women from the National Health and Nutrition Examination Survey (NHANES) cross-sectional examinations from 2003 to 2012. Pre-pregnancy BMI was calculated based on self-reported pre-pregnancy weight and measured height. The cut-offs of <18.5 (underweight), 18.5–24.9 (normal), 25.0–29.9 (overweight), and ≥30 kg/m2 (obese) were used to categorize the weight status of pregnant women prior to pregnancy. The DII, a literature-based dietary index to assess the inflammatory properties of diet, was estimated based on a one-day 24-h recall. Multivariable linear and logistic regressions were performed to estimate beta coefficients and the adjusted odds ratios (AORs) and 95% confidence intervals (95% CIs) on the association of pre-pregnancy BMI categories with the DII and CRP concentrations during pregnancy. After controlling for variables including: race/ethnicity, family poverty income ratio, education, marital status, month in pregnancy, and smoking status during pregnancy; women who were obese before pregnancy (n = 136) had increased odds for being in the highest tertile of the DII and CRP concentrations compared to women with normal weight (AORs 2.40, 95% CIs 1.01–5.71; AORs 24.84, 95% CIs 6.19–99.67, respectively). These findings suggest that women with pre-pregnancy obesity had greater odds of reporting higher DII and having elevated CRP. In conclusion, high pre-pregnancy BMI was associated with increased odds of pro-inflammatory diet and elevated CRP levels during pregnancy in the USA.

Semiparametric Estimation And Inference In Causal Inference And Measurement Error Models, Jianxuan Liu

Theses and Dissertations

This dissertation research has focused on theoretical and practical developments of semiparametric modeling and statistical inference for high dimensional data and measurement error data. In causal inference framework, when evaluating the effectiveness of medical treatments or social intervention policies, the average treatment effect becomes fundamentally important. We focus on propensity score modelling in treatment effect problems and develop new robust tools to overcome the curse of dimensionality. Furthermore, estimating and testing the effect of covariates of interest while accommodating many other covariates is an important problem in many scientific practices, including but not limited to empirical economics, public health and …

Neighborhood Environment And Falls Among Community-Dwelling Older Adults, Emily Joy Nicklett, Matthew C. Lohman Ph.D., Matthew Lee Smith

Faculty Publications

Background: Falls present a major challenge to active aging, but the relationship between neighborhood factors and falls is poorly understood. This study examined the relationship between fall events and neighborhood factors, including neighborhood social cohesion (sense of belonging, trust, friendliness, and helpfulness) and physical environment (vandalism/graffiti, rubbish, vacant/deserted houses, and perceived safety walking home at night). Methods: Data were analyzed from 9259 participants over four biennial waves (2006–2012) of the Health and Retirement Study (HRS), a nationally representative sample of adults aged 65 and older in the United States. Results: In models adjusting for demographic and health-related covariates, a one-unit …

Improved Simultaneous Estimation Of Location And System Reliability Via Shrinkage Ideas, Beidi Qiang

Theses and Dissertations

In decision theory, when several parameters need to be estimated simultaneously, many standard estimators can be improved, in terms of a combined loss function. The problem of finding such estimators has been well studied in the literature, but mostly under parametric settings, which is inappropriate for heavy-tailed distributions. In the first part of this dissertation, a robust simultaneous estimator of location is proposed using the shrinkage idea. A nonparametric Bayesian estimator is also discussed as an alternative. The proposed estimators do not assume a specific parametric distribution and they do not require the existence of finite moments. The performance of …

Longitudinal And Geographical Modeling Of Circular Data With An Application To Sudden Infant Death Syndrome, Xinyan Cai

Theses and Dissertations

The aim of this thesis is to study seasonality of death in U.S. infants who died from SIDS. We also propose to investigate secular trends and geographical patterns of seasonal patterns of mortality. The application of circular statistics is used to describe the seasonality of the month of death in infants who died from SIDS in 1990, 2000 and 2010. The secular trends of seasonal patterns of SIDS mortality are investigated using a circular linear regression model after adjusting for potential confounders. The geographical variation in seasonal patterns of SIDS mortality is explored from the U.S. map and quantified by …

Statistical Methods For Multivariate And Correlated Data, Xinling Xu

Theses and Dissertations

A commonly encountered data type in real life is count data, especially in selfreported behavioral studies. One issue of the self-reported count data is the inaccuracy. In the first part of the dissertation, we are going to address one specific type of inaccuracy in bivariate count data–heaping. Copula functions are used for the formulation of the bivariate distribution. Using copula functions for solving data inaccuracy problems is still a new area, which we are going to explore in this dissertation.

We also discuss the methods for variable selection when the explanatory variables are highly correlated. In particular, our method is …

Evaluation Of Goodness-Of-Fit Tests For The Cox Proportional Hazards Model With Time-Varying Covariates, Shanshan Hong

Theses and Dissertations

The proportional hazards (PH) model, proposed by Cox (1972), is one of the most popular survival models for analyzing time-to-event data. To use the PH model properly, one must examine whether the data satisfy the PH assumption. An alternative model should be suggested if the PH assumption is invalid. The main purpose of this thesis is to examine the performance of five existing methods for assessing the PH assumption. Through extensive simulations, the powers of five different existing methods are compared; these methods include the likelihood ratio test, the Schoenfeld residuals test, the scaled Schoenfeld residuals test, Lin et al. …

Functional Data Smoothing Methods And Their Applications, Songqiao Huang

Theses and Dissertations

In many subjects such as psychology, geography, physiology or behavioral science, researchers collect and analyze non-traditional data, i.e., data that do not consist of a set of scalar or vector observations, but rather a set of sequential observations measured over a fine grid on a continuous domain, such as time, space, etc. Because the underlying functional structure of the individual datum is of interest, Ramsay and Dalzell (1991) named the collection of topics involving analyzing these functional observations functional data analysis (FDA). Topics in functional data analysis include data smoothing, data registration, regression analysis with functional responses, cluster analysis on …

Nonparametric Inference For Orderings And Associations Between Two Random Variables, Chuan-Fa Tang

Theses and Dissertations

Ordering and dependency are two aspects to describe the relationship between two random variables. In this thesis, we choose two hypothesis testing problems to tackle; i.e., a goodness-of-fit test for uniform stochastic ordering and one for positive quadrant dependence. For the test for uniform stochastic ordering, we propose new nonparametric tests based on ordinal dominance curves. We derive the limiting distributions of test statistics and provide the least favorable configuration to determine critical values. Numerical evidence is presented to support our theoretical results, and we apply our methods to a real data set. An extension for random right-censored data is …

Marginal Structural Cox Model For Survival Data With Treatment-Confounder Feedback, Yanan Zhang

Theses and Dissertations

In an observational longitudinal study, there can be time-varying exposure/treatment and time-varying confounders. When the confounders affect the exposure and prior exposure also has an impact on levels of confounders, there is treatment confounder feedback. To admit estimation of unbiased causal effects, these conditions need to be hold, exchangeability, positivity, consistency. The traditional method of conditioning on potential confounders does not meet these 3 conditions. Therefore, parameter estimates from traditional Cox model are biased casual effect estimates when the treatment confounder feedback exists. The marginal structural Cox model can be used to address this issue. By calculating and including inverse …

Inflammatory Properties Of Diet And Glucose-Insulin Homeostasis In A Cohort Of Iranian Adults, Nazanin Moslehi, Behnaz Ehsani, Parvin Mirmiran, Nitin Shivappa, Maryam Tohidi, James R. Hébert, Fereidoun Azizi

Faculty Publications

We aimed to investigate associations of the dietary inflammatory index (DII) with glucose-insulin homeostasis markers, and the risk of glucose intolerance. This cross-sectional study included 2975 adults from the Tehran Lipid and Glucose Study. Fasting plasma glucose (FPG), 2-h post-load glucose (2h-PG), and fasting serum insulin were measured. Homeostatic model assessment of insulin resistance index (HOMA-IR) and β-cell function (HOMA-B), and the quantitative insulin sensitivity check index (QUICKI) were calculated. Glucose tolerance abnormalities included impaired fasting glucose (IFG), impaired glucose tolerance (IGT), and type 2 diabetes (T2DM). DII scores were positively associated with 2h-PG (β = 0.04; p = 0.05). …

Development And Application Of Bayesian Semiparametric Models For Dependent Data, Junshu Bao

Theses and Dissertations

Dependent data are very common in many research fields, such as medicine (repeated measures), finance (time series), traffic (clustered), etc. Effective control/modeling of the dependency among data can enhance the performance of the models and result in better prediction. In many cases, the correlation itself may be of great interest. In this dissertation, we develop novel Bayesian semi-/nonparametric regression models to analyze data with various dependence structures. In Chapter 2, a Bayesian non- parametric multivariate ordinal regression model is proposed to fit drinking behavior survey data from DWI offenders. The responses are two-dimensional ordinal data, drinking frequency and drinking quantity …

Novel Methods For Analyzing Longitudinal Data With Measurement Error In The Time Variable, Caroline Munindi Mulatya

Theses and Dissertations

In some longitudinal studies, the observed time points are often confounded with measurement error due to the sampling conditions, resulting into data with measurement error in the time variable. This type of data occurs mainly in observational studies when the onset of a longitudinal process is unknown or in clinical trials when individual visits do not take place as specified by the study protocol, but are often rounded to coincide with the study protocol. Methodological and inferential implications of error in time varying covariates for both linear and nonlinear models have been studied widely. In this dissertation, we shift attention …

Bayesian Nonparametric Approaches To Multiple Testing, Density Estimation, And Supervised Learning, William Cipolli Iii

Theses and Dissertations

This dissertation presents methods for several applications of Polya tree models. These novel nonparametric approaches to the problems of multiple testing, density estimation and supervised learning provide an alternative to other parametric and nonparametric models. In Chapter 2, the proposed approximate finite Polya tree multiple testing procedure is very successful in correctly classifying the observations with non-zero mean in a computationally efficient manner; this holds even when the non-zero means are simulated from a mean-zero distribution. Further, the model is capable of this for “interestingly different” observations in the cases where that is of interest. Chapter 3 proposes discrete, and …

Case-Oriented Pathways Analysis In Pancreatic Adenocarcinoma Using Data From A Sleeping Beauty Transposon Mutagenesis Screen, Yen Yi Ho, Timothy K. Starr, Rebecca S. Larue, David A. Largaespada

Faculty Publications

Background: Mutation studies of pancreatic ductal adenocarcinoma (PDA) have revealed complicated heterogeneous genomic landscapes of the disease. These studies cataloged a number of genes mutated at high frequencies, but also report a very large number of genes mutated in lower percentages of tumors. Taking advantage of a well-established forward genetic screening technique, with the Sleeping Beauty (SB) transposon, several studies produced PDA and discovered a number of common insertion sites (CIS) and associated genes that are recurrently mutated at high frequencies. As with human mutation studies, a very large number of genes were found to be altered by transposon insertion …

Semiparametric Regression Analysis Of Panel Count Data And Interval-Censored Failure Time Data, Bin Yao

Theses and Dissertations

This dissertation discusses three important research topics on semiparametric regression analysis of panel count data and interval-censored data. Both types of 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. For panel count data, the response variable is the counts of some recurrent events, whose exact occurrence times are usually unknown. For interval-censored data, the response variable is the time to some events of interest, often called survival time or failure time, and the exact response time …

The Reflected-Shifted-Truncated-Gamma Distribution For Negatively Skewed Survival Data With Application To Pediatric Nephrotic Syndrome, Sophia D. Waymyers

Theses and Dissertations

Negatively skewed survival data arise occasionally in public health fields and in statistical research. Standard distributions such as the exponential, generalized F, generalized gamma, Gompertz, log-logistic, lognormal, Rayleigh, and Weibull distributions are not always well suited to this data. The primary goal of this dissertation is to find a viable alternative for modeling negatively skewed survival data such as the time to first remission for pediatric patients with frequently relapsing or steroid dependent nephrotic syndrome.

We begin with a brief introduction of survival analysis and the nature of pediatric nephrotic syndrome. A meta-analysis on atopy and pediatric nephrotic syndrome using …