Biostatistics Commons^™

Distribution And Correlates Of Long-Acting Injectable Antipsychotic Use Among Community Mental Health Center Patients, Matthew C. Lohman Ph.D., Victoria Scott, Mansi Verma, Paige Jones, Eve Fields

Faculty Publications

Although long-acting injectable antipsychotics (LAIs) are an important pharmaceutical option in the management of schizophrenia and related disorders, little is known about patient characteristics related to LAI use in real-world outpatient settings. We analyzed electronic medical records from 41,401 patients who received psychiatric services from one of 16 regional mental health centers operated by the South Carolina Department of Mental Health in 2022. We compared the use of first- and second-generation LAIs and oral antipsychotics by sociodemographic (age, gender, race/ethnicity, zip code, payment source) and clinical characteristics (psychiatric diagnoses, service use). We used logistic regression models to estimate associations between …

An Investigation Of Travel Distance And Timeliness Of Breast Cancer Treatment Among A Diverse Cohort In The United States, Swann Arp Adams, Oluwole Adeyemi Babatunde, Whitney Zahnd, Peiyin Hung, Karen E. Wickersham, Nathaniel Bell Ph.D., Jan M. Eberth Ph.D.

Publications

Travel to and from distant cancer treatment facilities can place a significant burden on cancer patients, particularly rural and minority survivors. Thus, the purpose of our investigation was to examine the association between patient travel distance and delays in types of treatment for breast cancer (surgery, radiation, chemotherapy, and hormonal therapy) and treatment delays. Using a novel linkage between the state cancer registry and administrative data from Medicaid and a private insurance plan, 2155 BC patients were successfully linked to create the cohort. ArcGIS was used to geocode all case residences and treatment facility addresses and calculate network distance between …

Ethnic Disparities In Hba1c And Hypoglycemia Among Youth With Type 1 Diabetes: Beyond Access To Technology, Social Deprivation And Mean Blood Glucose, J. Pemberton, Zhide Fang, Stuart Chalew, S. Uday

School of Public Health Faculty Publications

Introduction The UK national pediatric diabetes audit reports higher HbA1c for children and young people (CYP) with type 1 diabetes (T1D) of Black ethnicity compared with White counterparts. This is presumably related to higher mean blood glucose (MBG) due to lower socioeconomic status (SES) and less access to technology. We aimed to determine if HbA1c ethnic disparity persists after accounting for the above variables. Research design and methods A retrospective analysis of participants who received structured education in continuous glucose monitoring (CGM) use was conducted at a tertiary center. HbA1c was paired with glucose metrics from 90-day CGM data. The …

Sleep Duration, Dietary Inflammatory Potential, And Obesity In Relation To Colorectal Cancer Incidence In The Multiethnic Cohort, Penias Tembo, Longgang Zhao, Loïc Le Marchan, Lynne R. Wilkens, Song-Yi Park, Christopher A. Haiman, Michael David Wirth Msph, Phd, Face, James R. Hébert Scd

Faculty Publications

Background/Objectives: Colorectal cancer (CRC) is a leading cause of cancer-related morbidity and mortality worldwide. Sleep duration, diet, and obesity have each been identified as modifiable risk factors linked to CRC. However, their joint effect on CRC incidence is underexplored. This study investigated the association between sleep duration and CRC incidence and explored the joint effects of sleep duration, a pro-inflammatory diet, and obesity on CRC incidence in the Multiethnic Cohort (MEC). Methods: This prospective cohort study analyzed 193,027 participants from Hawaii and California enrolled in the MEC between 1993 and 1996. Sleep duration was self-reported and categorized as short (≤6 …

Racial Disparities Of Type 2 Diabetes Through Exercise: The Multi-Ethnic Study Of Atherosclerosis, Tong Xia, Roch A. Nianogo, Qingzhao Yu, Tamara Horwich, Preethi Srikanthan, Kosuke Inoue, Matthew Allison, Zuo-Feng Zhang, Karol E. Watson, Liwei Chen

School of Public Health Faculty Publications

Introduction: Persistent racial and ethnic disparities exist for Type 2 diabetes in the U.S. Racial and ethnic minorities have a higher risk of Type 2 diabetes, and studies suggest that they engage in less exercise than White population. This study examined whether and to what degree racial differences in Type 2 diabetes were explained by exercise. Methods: Adults aged 45–84 years without Type 2 diabetes at baseline (2000–2002) were included from the Multi-Ethnic Study of Atherosclerosis cohort and followed through 2020. Associations between race and Type 2 diabetes were examined using multivariable Cox proportional hazard regression. The effects of exercise …

Associations Between Social Determinants Of Health And Mental Health Disorders Among U.S. Population: A Cross-Sectional Study, S. Tanarsuwongkul

Faculty Publications

Aims

The impact of social determinants of health (SDOH) on mental health is increasingly realized. A comprehensive study examining the associations of SDOH with mental health disorders has yet to be accomplished. This study evaluated the associations between five domains of SDOH and the SDOH summary score and mental health disorders in the United States.

Methods

We analyzed data from a diverse group of participants enrolled in the All of Us research programme, a research programme to gather data from one million people living in the United States, in a cross-sectional design. The primary exposure was SDOH based on Healthy …

Beyond Homogeneity: Exploring Causal Heterogeneity In Psychopathology, Philip B. Vinh

Theses and Dissertations

Traditional models in psychiatric research often impose assumptions of causal homogeneity, treating population-level associations as reflective of uniform underlying mechanisms. This dissertation challenges that assumption by introducing statistical and machine learning frameworks designed to detect and model causal heterogeneity in the development of psychopathology. Central to this approach is the advancement of finite mixture structural equation modeling (FM-SEM) to identify latent subgroups characterized by distinct, and sometimes opposing, causal pathways.

The dissertation comprises three integrated empirical studies. The first introduces mixDoC, a finite mixture extension of the classical Direction of Causation (DoC) model applied to twin data, enabling the detection …

Association Between Lifetime Interpersonal Violence And Post– Covid-19 Condition Among Women In Kentucky, 2020-2022, Ayşe Güler, Heather M. Bush, Katie Schill, Nurlan Kussainov, Ann L. Coker

Biostatistics Faculty Publications

Objective: The COVID-19 pandemic increased the risk of interpersonal violence. We investigated the association between lifetime interpersonal violence experience and risk of post–COVID-19 condition (the persistence of symptoms of COVID-19 and severity of health problems associated with COVID-19 that last a few weeks, months, or years) among women with lifetime interpersonal violence experience.   Methods: Women participants aged ≥18 years in Kentucky’s Wellness, Health & You—COVID-19 study completed online quantitative surveys about the impacts of the pandemic, developing COVID-19, and symptoms of post–COVID-19 condition. We conducted cross-sectional analyses estimating rate ratios of developing COVID-19 and symptoms of post–COVID-19 condition during the …

Model-Free Organization Of Patient Reported Outcomes Data: Geometrical Rep-Resentation Of The Modified Compartmen-Talization Method, Manasi Sheth, N. Rao Chaganty

Mathematics & Statistics Faculty Publications

There is a recent advancement in the field of mathematics and statistics to understand the geometry or connectedness of the data due to the massive amounts of data being generated. The data provided for analyses are usually very large and need to be organized and minimized in order to make it more useful and meaningful. In biostatistics or medical field, it is important for patients to have access to high-quality, safe and effective and/ or efficacious medical products. It is quite necessary to ascertain that the patients and their care-partners stay at the center of the regulatory decision-making process. In …

Hybrid Mixtures Of Factor Analyzers For High Dimensional Data, Kazeem Abiodun Kareem

Dissertations, Master's Theses and Master's Reports

Factor analysis is a powerful tool for modeling latent structures in high-dimensional data, traditional approaches assume a single global structure, limiting their ability to capture heterogeneity. The Mixture of Factor Analyzers (MFA) extends classical factor analysis by modeling data as a mixture of Gaussian-distributed local subspaces, effectively uncovering cluster-specific latent structures. However, MFA relies on Gaussian mixtures, making it sensitive to outliers and ill-suited for heavy-tailed data. The Mixture of $t$-Factor Analyzers (M$t$FA) addresses these limitations by incorporating multivariate $t$-distributions, improving robustness. Despite their advantages, both MFA and M$t$FA face significant computational challenges in high-dimensional settings, particularly due to costly …

Comparison Of Machine Learning Models For Colon Cancer Survival: Predictive Modeling Approach, Reuben Adatorwovor, Motolani E. Ogunsanya, Bin Huang, Richard Charnigo, Olufunmilola Abraham

Biostatistics Faculty Publications

Background: Colon cancer is a leading cause of cancer-related deaths worldwide, with survival influenced by risk factors, treatment type, and patient characteristics. Traditional statistical models, such as Kaplan-Meier curves, have been widely used to estimate survival probabilities. However, these models often have difficulty handling complex interactions, covariates, and nonlinear relationships between risk factors. Recently, machine learning (ML) techniques have emerged as promising tools for improving survival prediction by handling large covariates and capturing complex patterns.

Objective: This study compares several ML models to accurately estimate colon cancer survival by leveraging data from the Kentucky Cancer Registry. By identifying key risk …

Disentangling The Inverse Relationship Between Cancer And Alzheimer’S Or Parkinson’S Disease: A Systematic Review On Mendelian Randomization Studies, Khine Zin Aung, Su Su Zin, Xian Wu, Zin W. Myint, Shama D. Karanth, Steven Estus, Christopher M. Norris, Peter T. Nelson, David W. Fardo, Erin L. Abner, Yuriko Katsumata

Biostatistics Faculty Publications

Introduction

Although studies have reported an inverse relationship between cancer and neurodegenerative diseases such as Alzheimer's disease (AD) and Parkinson's disease (PD), findings remain inconsistent. Observational studies are limited by survival bias and reverse causation. To better understand the relationship, we conducted a systematic review of Mendelian randomization (MR) studies examining both directions—assessing cancer as a risk factor for AD or PD, as well as AD or PD as exposures influencing cancer risk.

Methods

We systematically reviewed MR studies investigating the causal relation between cancer and either AD or PD. Cancer could be specified as either an exposure or an …

Incident Atherosclerotic Cardiovascular Disease Among Veterans By Gender Identity: A Cohort Study, Carl G. Streed Jr., Meredith S. Duncan, Kory R. Heier, T. Elizabeth Workman, Lauren B. Beach, Guneet K. Jasuja, Hill L. Wolfe, Landon D. Hughes, John R. O’Leary, Melissa Skanderson, Joseph L. Goulet

Biostatistics Faculty Publications

Background: Transgender and gender diverse (trans) populations are at elevated risk for atherosclerotic cardiovascular disease (ASCVD).

Objective: Measure the association of gender identity and gender-affirming hormone therapy (GAHT) with ASCVD outcomes.

Design: Cohort study.

Participants: Over 1 million veterans receiving care in the Veterans Health Administration.

Main Measures: Gender identity was identified via a validated natural language processing (NLP) algorithm. Incident ASCVD (acute myocardial infarction, ischemic stroke, or revascularization after the baseline date) was identified via International Classification of Diseases diagnosis codes among veterans without prevalent ASCVD. We calculated sample statistics stratified by gender identity and used Cox proportional hazard …

Comparative Efficacy Of Hallucinogens In Treating Mood Disorders Through A Meta-Analysis Of Symptom Reduction, Dosage, And Duration, John Marco D.F. Muniz

Honors Undergraduate Theses

Background: Hallucinogens including psilocybin, lysergic acid diethylamide (LSD), ketamine, N,N-dimethyltryptamine (DMT) (as ayahuasca), have re-emerged as potential rapid-acting treatments for mood disorders. We conducted a meta-analysis of placebo-controlled trials evaluating their efficacy in depression and anxiety disorders. Methods: A systematic review identified 12 trials (Total ≈ 670) meeting inclusion criteria (randomized, placebo-controlled). Data on Cohen’s d and Hedges’ g effect sizes for depression- and anxiety-related outcomes were extracted. We computed pooled effect sizes (weighted by sample size and inverse variance), performed subgroup analyses by drug, diagnosis, follow-up duration, and outcome measure type, and assessed heterogeneity (I², Q …

Predictive Modeling For Healthcare Data Using Nonlinear Bayesian Methods, Prince Kofi Asare

Theses and Dissertations

Unplanned hospital readmissions represent a significant challenge for healthcare systems, contributing to substantial financial burdens and highlighting gaps in patient care coordination. In the U.S., approximately 20% of Medicare beneficiaries are readmitted within 30 days, costing billions annually. Social determinants of health, such as income, housing stability, and social support, account for up to 80% of health outcomes, yet their integration into predictive models remains underexplored. This study introduces a novel Bayesian framework for predicting 30-day readmission risk, combining Gaussian Process models with spike-and-slab priors and Bayesian Lasso regression with Laplace priors. Utilizing Markov Chain Monte Carlo methods, the approach …

A New Robust Imputation Method For Longitudinal Data With Non-Normal Continuous Outcomes, Ahmed M. Gad Prof, Yasmie A. Mohamed, Nesma M. Daewish Dr, Abdelnaser S. Abdrabou Prof, Wafaa M. Ibrahim Dr

Business Administration

Missing values is very common in longitudinal data and it is the main challenge in analysis of longitudinal data. Missing values have a significant effect on longitudinal data analysis because they lead to loss of information, biased estimates, and misleading results. In practice there is a need for an imputation method to deal with missing values.

Aim: In this study a new robust regression-based imputation method to deal with missing values in longitudinal data is proposed. This method utilizes the modified adaptive linear regression model and does not require the normality of the responses. It is a novel robust imputation …

Comparative Study Of Single Imputation Techniques For The Prediction Of Missing Dairy Data, Ahmed M. Gad Prof, Ahmed Abdelhakim Ahmed Mr, Eman Manaa Prof, Basant Shafik Dr, Sakr Mostafa Prof

Business Administration

Dairy farm records are a crucial component of effective livestock business management. Record analysis allows a farm’s owner to make informed decisions. Incomplete records are less useful for data analysis, so it's important to handle missing values correctly. This study compares different imputation methods for handling missing values in a dataset of dairy records comprising 997 records collected from 234 cows between 2012 and 2022. The dataset was screened against records with missing values and then deleted, resulting in 858 observations from 200 animals. There were missing values in two variables, with a missing percentage of 13.9%: days in milk …

Analysis Of Cytokine Data In A Case-Control Study Of Me/Cfs, Navya Amaratunga

Electronic Theses & Dissertations (2024 - present)

This study investigated the role of cytokines in Myalgic Encephalomyelitis/Chronic Fatigue Syndrome (ME/CFS), a debilitating multi-system disease with unknown etiology. Using data from a case-control study (60 cases, 61 controls) previously collected by Dr. Roxana Moslehi’s lab, serum levels of 48 cytokines were analyzed. Statistical analyses, including T-tests, Wilcoxon Rank Sum tests, and logistic regression (unadjusted and adjusted for medication use), were performed to identify differences between cases and controls.

Results revealed a statistically significant difference in mean Fractalkine levels between cases and controls (p=0.031). Borderline significant differences were also observed for IL-6 (p=0.046) and MIP-1α (p=0.046). Unadjusted logistic regression …

Highest Risk Density Region For The Communication Of The Impact Of A Treatment Covariate On The Time-To-Event Distribution, Giacomo Biganzoli, Giuseppe Marano Phd, Patrizia Boracchi Phd

COBRA Preprint Series

Analysis of time-to-event (TTE) data is central to clinical research, yet conventional summary measures like the hazard ratio (HR) and restricted mean survival time (RMST) present significant challenges. The HR is often misinterpreted, and its validity depends on the frequently violated proportional hazards assumption, while the RMST is highly sensitive to the choice of time horizon. This paper introduces two novel, assumption-free estimands to address these limitations: the Highest Risk Density Region (HRDR) and the Highest Net Risk Difference Region (HNRDR).

The HRDR identifies the narrowest time interval containing a pre-specified probability mass of events, directly answering the clinical question: …

Food Insecurity Among Graduate Students At The University Of Alabama At Birmingham, Amy Elizabeth Callahan

All ETDs from UAB

College food insecurity (FI) has grown steadily as a field of research over the past fifteen years. Existing research primarily has demonstrated that FI is a persistent problem with various risk factors and myriad potential impacts on students, though graduate students are largely understudied in favor of undergraduate and aggregated student populations. Given the unique circumstances graduate students face, as well as expected demographic differences such as age and household characteristics, this is a serious gap in the literature. This dissertation aims to investigate graduate student food insecurity in the United States, and particularly at the University of Alabama at …