Biostatistics Commons^™

Neighborhood Socioeconomic Status And Overall Survival Among Children With Acute Lymphoblastic Leukemia, Anna Hoppmann, Debroah M. Hurley, Stuart Cramer, Monique J. Brown Ph.D., Mph

Faculty Publications

A disadvantaged neighborhood, as represented by area-level socioeconomic status (SES) has been associated with adverse outcomes among children with acute lymphoblastic leukemia (ALL) in the US, but the duration of impact after ALL diagnosis is not well understood. This retrospective cohort study utilized the National Cancer Database (NCDB) to examine the impact of area-level SES on overall survival among children with ALL. Median income and education quartiles based on residential zip code were used to create a composite area-level SES variable. Individual-level variables included age, sex, race, year of diagnosis, primary payer, distance to care, rurality, time to treatment, and …

Changes In Mental Health Care Utilisation Before And During The Covid-19 Pandemic Among People Living With Hiv In The Usa: A Retrospective Cohort Study Using The All Of Us Dataset, Atena Pasha, Shan Qiao, Jiajia Zhang Ph.D., Ruilie Cai, Buwei He, Xueying Yang, Chen Liang Ph.D., Sharon Weissman, Xiaoming Li Ph.D.

Faculty Publications

Introduction Despite the profound impact of the COVID-19 pandemic on people living with HIV (PLWH) mental health, large-scale, real-world data on mental healthcare utilisation and associated factors among PLWH remain limited. This study explores mental healthcare utilisation and associated factors among PLWH during the COVID-19 pandemic.

Methods Using a retrospective cohort design, we identified and included 4575 PLWH through computational phenotyping based on relevant Observational Medical Outcomes Partnership Common Data Model concept sets from the All of Us programme between March 2018 and March 2022. Mental healthcare utilisation was measured using the yearly count of mental healthcare visits and compared …

Statistical Methods For Joint Outcome Modeling And Dynamic Assessment Of Recurrent Events, Zifang Kong

Statistical Science Theses and Dissertations

Recurrent event data frequently arise in clinical studies where individuals experience repeated, possibly related, events over time. These data are often accompanied by sparse and irregular longitudinal measurements, creating challenges for traditional joint modeling approaches that struggle to account for time-dependent associations and within-subject correlations. We propose FRAILTY (Functional Regression with AutoRegressIve fraiLTY), a novel two-step framework that integrates functional principal component analysis (PACE) with a dynamic frailty model featuring autoregressive structure. FRAILTY accommodates both scalar and functional predictors and captures within-subject dependence across recurrent events. To further extend its utility, we develop a multivariate joint modeling framework that simultaneously …

Towards Reliable Clinical Applications Of Ai Models In Radiotherapy, Biling Wang

Statistical Science Theses and Dissertations

Over the past decade, artificial intelligence (AI), particularly through deep learning (DL) techniques, has made significant strides in fields like computer vision (CV) and natural language processing (NLP), leading to transformative advancements across numerous applications. This progress has sparked considerable enthusiasm within the medical field, where DL-related research has grown exponentially since 2015. However, despite these promising developments, the real-world deployment of DL models in healthcare remains limited, especially in safety-critical domains such as radiotherapy (RT), where reliability, safety, and sustained performance are critical. This thesis addresses three core challenges associated with the clinical application of DL models: (1) post-deployment …

Evaluating Alpha Spending Functions Applied To Observational Time-To-Event Analysis, Moses Torgbenu

Electronic Theses and Dissertations

This thesis explores the theoretical foundation of the alpha spending approach and extends its application beyond the conventional setting of randomized controlled trials (RCTs) to observational studies with time to event analyses. In these less structured environments, key design parameters such as the total number of events are often unknown, posing challenges for the standard implementation of sequential analysis methods.

Through simulation studies, this research delivers several important contributions. First, it presents a modified approach that uses calendar time to define the timing of interim analyses while relying on event-based information to estimate the correlation among test statistics. This adjustment …

Predictor-Informed Bayesian Nonparametric Clustering., Md Yasin Ali Parh

Electronic Theses and Dissertations

In this dissertation, we performed clustering of observations such that the cluster membership is influenced by a set of predictors. To that end, we employ the Bayesian nonparametric Common Atom Model (CAM), which is a nested clustering algorithm that utilizes a (fixed) group membership for each observation to encourage more similar clustering of members of the same group. CAM operates by assuming each group has its own vector of cluster probabilities, which are themselves clustered to allow similar clustering for some groups. We extend this approach by treating the group membership as an unknown latent variable determined as a flexible …

Simultaneous Selection Of Inflations And Variables In Multiple Inflations Poisson Model (Mip), John Koomson

Open Access Theses & Dissertations

Count data frequently arise in biomedical, economic, and social science research and are often characterized by structural excesses at specific count levels. To accommodate such patterns, Su et al. (2013), among others, introduced the Multiple-Inflation Poisson (MIP) model, which allows for multiple inflated counts within the distribution. However, two critical challenges remain in modeling such data: (i) identifying the true inflation points where excess counts occur, and (ii) selecting the relevant covariates that explain variation in the inflation and count process. This dissertation addresses these issues by advancing the MIP model through a novel methodology that enables the simultaneous selection …

Analyzing High-Risk Fertility Behavior For Sustainable Maternal-Child Health: A 2017 Sociodemographic Study In Urban And Rural Indonesia, Asti Annisa Utami, Fadhaa Aditya Kautsar Murti, Popy Yuniar, Milla Herdayati

Kesmas

Indonesia's goal of achieving Indonesia Emas 2045 hinges on improving Maternal-Child Health (MCH), essential for building a healthy and competitive population. Despite some advancements, the Maternal Mortality Rate (MMR) and Under-five Mortality Rate (U5MR) remain high, particularly because of High-Risk Fertility Behavior (HRFB). The HRFB poses significant risks to MCH, affecting both urban and rural women. This study aimed to identify the factors associated with HRFB in these areas to enhance MCH outcomes and support Indonesia's sustainable health goals. This cross-sectional study used a secondary dataset from the 2017 Indonesian Demographic Health Survey. A total of 20,530 women of reproductive …

The Effectiveness Of Remote Patient Monitoring In Reducing The Risk Of Rehospitalizations In Covid-19 Patients: A Meta-Analysis, Dela Riadi, Indang Trihandini, Dewi Nirmala Sari, Fikri Wijaya

Kesmas

An integrated analysis of various Remote Patient Monitoring (RPM) studies is needed to evaluate the reduction rate of the risk of rehospitalization in COVID-19 patients. This meta-analysis aimed to provide an overview of the effectiveness of RPM. A literature search through online databases (PubMed, Science Direct, Scopus, ProQuest, and Embase) was conducted from 2019 to 2022. After using the Cochrane Collaboration's risk of bias tool, five studies on COVID-19 were selected. Based on the data collected from 2,685 participants (intervention = 1,060, control = 1,625), the use of RPM was found to reduce rehospitalization by 0.56 times compared to not …

Cross-Cultural Adaptation And Validation Of Ranas-Based Instrument For Measuring Latrine Use Behavior In Indonesia, Vera Yulyani, Fatwa Sari Tetra Dewi, Iswanto Iswanto

Kesmas

Using toilets is a simple way to prevent diarrhea, yet no validated tool exists to measure this habit. This study aimed to develop and validate instruments for measuring latrine use consistency. This questionnaire was adapted from the risk, attitude, norm, ability, and self-regulation (RANAS) framework developed in India and modified for Indonesia. It was evaluated by three experts using the content validity index (CVI). The face validity index (FVI) was pilot-tested on 40 community respondents. Variables measured included behavior, habits, intentions to use toilets, knowledge, attitudes, norms, abilities, and self-regulation. Question items with relevance and clarity scores of item CVI …

An Experimental Investigation Of Federal Messaging On Public Support For Enforcement- And Treatment-Based Approaches For Opioid Overdose Prevention In South Carolina, Lídia Gual-Gonzalez, Hunter M. Boehme, Peter Baker, Melissa Nolan Ph.D., Mph

Faculty Publications

Background

As the opioid overdose crisis continues to produce excessive morbidity and mortality in the United States, government agencies have applied various approaches to prevent overdoses, including law-enforcement efforts (e.g., arresting people who use drugs, interrupting drug traffickers, etc.) and treatment-based approaches (e.g., naloxone, medications for opioid use disorder, etc.). Public perception and support of these approaches are relevant for informing policy, allocating resources, and effectively implementing community interventions to prevent drug-related harms.

Methods

Using an embedded informational survey design, we experimentally assessed whether public support for strategies to prevent overdose in South Carolina is influenced by language from federal …

The Sodium-Glutamate Antagonist Riluzole Improves Outcome After Acute Spinal Cord Injury: Results From The Riscis Randomised Controlled Trial Analysed Using A Global Statistical Analytic Technique, Michael G. Fehlings, Karlo M. Pedro, Mohammed Ali Alvi, Ali Moghaddamjou, James S. Harrop, Ralph Stanford, Jonathon Ball, Bizhan Aarabi, Paul M. Arnold, James D. Guest, Shekar N. Kurpad, James M. Schuster, Ahmad N. Nassr, Karl M. Schmitt, Jefferson R. Wilson, Darrel S. Brodke, Faiz U. Ahmad, Albert Yee, Wilson Z. Ray, Nathaniel P. Brooks, Jason Wilson, Diana S.L. Chow, Elizabeth G. Toups, Kevin E. Thorpe, Jiaxin Huang, Peng Huang

School of Medicine Faculty Publications

Background: Spinal cord injury (SCI) clinical trials typically rely on a single primary endpoint to assess drug efficacy. This strategy fails to adequately capture the full impact of treatment in heterogenous neurological conditions like SCI. A more patient-centric analysis requires assessment of neurological function, functional capacity, and quality of life, incorporating meaningful patient-reported outcomes. The global statistical test (GST) addresses this challenge using a unified statistical conclusion regarding the superiority of a treatment strategy over another by evaluating multiple trial endpoints simultaneously. Methods: The RISCIS trial (Safety and Efficacy of Riluzole in Acute Spinal Cord Injury Study) data was analysed …

Predicting Sleep And Sleep Stage In Children Using Actigraphy And Heartrate Via A Long Short-Term Memory Deep Learning Algorithm: A Performance Evaluation, Robert Weaver Med, Phd, James White, Olivia Finnegan, Hongpeng Yang, Zifei Zhong, Keagan Kiely, Catherine Jones, Yan Tong, Srihari Nelakuditi, Rahul Ghosal, David E. Brown, Russell R. Pate Ph.D., Gregory J. Welk, Massimiliano De Zambotti, Yuan Wang, Sarah Burkart, Elizabeth L. Adams Phd, Bridget Armstrong, Michael Beets Med, Mph, Phd

Faculty Publications

Children's ambulatory sleep is commonly measured via actigraphy. However, traditional actigraphy measured sleep (e.g., Sadeh algorithm) struggles to predict wake (i.e., specificity, values typically < 70) and cannot predict sleep stages. Long short-term memory (LSTM) is a machine learning algorithm that may address these deficiencies. This study evaluated the agreement of LSTM sleep estimates from actigraphy and heartrate (HR) data with polysomnography (PSG). Children (N = 238, 5–12 years,52.8% male, 50% Black 31.9% White) participated in an overnight laboratory polysomnography. Participants were referred be-cause of suspected sleep disruptions. Children wore an ActiGraph GT9X accelerometer and two of three consumer wearables(i.e., Apple Watch Series 7, Fitbit Sense, Garmin Vivoactive 4) on their non-dominant wrist during the polysomnogram. LSTM estimated sleep versus wake and sleep stage (wake, not-REM, REM) using raw actigraphy and HR data for each 30-s epoch. Logistic regression and random forest were also estimated as a benchmark for performance with which to compare the LSTM results. A 10-fold cross-validation technique was employed, and confusion matrices were constructed. Sensitivity and specificity were calculated to assess the agreement between research-grade and consumer wearables with the criterion polysomnography. For sleep versus wake classification, LSTM outperformed logistic regression and random forest with accuracy ranging from 94.1to 95.1, sensitivity ranging from 94.9 to 95.9 across different devices, and specificity ranging from 84.5 to 89.6. The addition of HR improved the prediction of sleep stages but not binary sleep versus wake. LSTM is promising for predicting sleep and sleep staging from actigraphy data, and HR may improve sleep stage prediction.

Approaches To Enhancing Multiple Hypothesis Testing Methods With Side-Information, Siyu Zheng

Theses and Dissertations

Lesion-symptom mapping (LSM) studies offer insight into the brain areas involved in various aspects of cognition. This is commonly done via behavioral testing in patients with a naturally occurring brain injury or lesions (e.g., strokes or brain tumors). This results in high-dimensional observational data where lesion status (present/absent) is non-uniformly distributed, with some voxels having lesions in very few (or no) subjects. In this situation, mass univariate hypothesis tests have severe power heterogeneity where many tests are known a priori to have little to no power. Additionally, high-dimensional observational data can be grouped according to brain anatomical structure.

In this …

A Spatial Scan Statistic For Group Testing Data, Vincent Onyame

Theses and Dissertations

Group testing involves pooling specimens from multiple individuals and offers an efficient means to surveil low-prevalence pathogens, but poses challenges for spatial cluster detection when only pooled results are observed. In this thesis, we develop a spatial scan statistic tailored to group-testing data with variable pool sizes. The statistic compares a null hypothesis of a homogeneous infection rate across all clusters to an alternative hypothesis that infection probabilities differ inside and outside a candidate cluster, with both models fitted by maximum likelihood estimation. We approximate the null distribution of the maximum likelihood ratio test via Monte Carlo simulation.

Through a …

Functional Time Transformation Model With Applications To Digital Health, Rahul Ghosal Ph.D., Marcos Matabuena, Sujit K. Ghosh

Faculty Publications

The advent of wearable and sensor technologies now leads to functional predictors which are intrinsically infinite dimensional. While the existing approaches for functional data and survival outcomes lean on the well-established Cox model, the proportional hazard (PH) assumption might not always be suitable in real-world applications. Motivated by physiological signals encountered in digital medicine, we develop a more general and flexible functional time-transformation model for estimating the conditional survival function with both functional and scalar covariates. A partially functional regression model is used to directly model the survival time on the covariates through an unknown monotone transformation and …

Sequential Data Modeling Of Influenza A Via Traditional, Dwt-Gpr Hybrid, And Deep Learning Architectures, Edmund Fosu Agyemang

Theses and Dissertations

Influenza A is responsible for 290,000 to 650,000 respiratory deaths a year, though this estimate is an improvement from years past due to improved sanitation, healthcare practices, and vaccination programs. In this study, we perform a comparative analysis of traditional, deep-learning and discrete wavelet (DWT)-Gaussian Process (GP) hybrid models to predict Influenza A outbreaks. Using historical data from January 2009 to December 2023, we compared the performance of traditional ARIMA and ETS models, four variants of DWT-GPR models and six distinct deep learning architectures: Simple RNN, LSTM, GRU, BiLSTM, BiGRU and Transformer. The results reveal a clear superiority of all …

Hypertensive Disorders Of Pregnancy And Perinatal Outcomes: Two Prospective Cohort Studies Of Nulliparous Women In India And Tanzania, Andrea B. Pembe, Pratibha Dwarkanath, Amani Kikula, John Michael Raj, Nandita Perumal Phd, Heavenlight A. Paulo, Rajalakshmi M, Christopher P. Duggan, Honorati M. Masanja, Nandini Chopra, Mary M. Sando, Tinku Thomas, Cara A. Yelverton, Alfa Muhihi, Anura V. Kurpad, Wafaie E. Fawzi, Blair J. Wylie, Christopher R. Sudfeld

Faculty Publications

Introduction Hypertensive disorders of pregnancy (HDP) have been linked with increased risk for maternal and offspring complications in high-income settings. However, in resource-limited settings, studies with robust measurement of HDP, including severity and timing, and perinatal outcomes are limited.

Methods We analysed data from two prospective cohorts of nulliparous women in India (n=10 570 pregnancies) and Tanzania (n=10 299 pregnancies) who were enrolled in calcium supplementation trials and had blood pressure and proteinuria assessments throughout pregnancy and at the time of labour and delivery. Generalised estimating equations were used to assess the relationship between HDP severity categories (gestational hypertension, preeclampsia …

Association Between Dietary Inflammatory And Antioxidant Potential And Systemic Inflammatory And Oxidative Status With The Risk And Severity Of Coronary Artery Disease, Zahara Namkhah, Elham Alipoor, Manhnaz Salmani, Negar Ebrahimi, Monireh Ahmadpanahi, Ali Vasheghani-Farahani, Mehdi Yaseri, Michael David Wirth, Longgang Zhao, James Hébert Scd, Javad Hosseinzadeh-Attar

Faculty Publications

Background and aims

Unhealthy diets have pro-inflammatory properties that have been shown to contribute to coronary artery disease (CAD). The dietary inflammatory index (DII®) and the dietary antioxidant quality score (DAQS) quantify the anti-/pro-inflammatory and antioxidant potential of a diet. This study aims to investigate the association between the energy-adjusted DII (E-DIITM), DAQS, oxidant/anti-oxidant biomarkers, and CAD risk and severity.

Methods and results

This cross-sectional study investigated 158 participants for the presence and severity of CAD based on coronary angiography. E-DII and DAQS scores, malondialdehyde (MDA), total oxidant status (TOS), glutathione peroxidase (GPX) activity, total antioxidant capacity (TAC) and conventional …

Incorporating Propensity Score Weighting And Nonresposne Adjustments Into Complex Survey Data With Survival Outcomes, Xinrui Shi

Theses and Dissertations

Propensity score weighting (PSW) plays a key role in minimizing confounding in observational research, especially when estimating treatment effects for time-to-event outcomes. However, its integration into survey data with complex design – particularly data with multiple stage sampling and censoring – remains underexplored. One significant challenge in such settings is the presence of nonresponse, which can introduce additional bias and complicate the use of standard weight adjustments. Moreover, there has been limited study on how PS weights can be effectively combined with nonresponse weighting adjustments in complex survey data that include survival outcomes. This dissertation aims to extend current methodologies …