Statistics and Probability Commons^™

Cancer Incidence And Stage At Diagnosis Among People With Psychotic Disorders: Systematic Review And Meta-Analysis., Jared C Wootten, Joshua C Wiener, Phillip S Blanchette, Kelly K. Anderson

Epidemiology and Biostatistics Publications

Research regarding the incidence of cancer among people with psychotic disorders relative to the general population is equivocal, although the evidence suggests that they have more advanced stage cancer at diagnosis. We conducted a systematic review and meta-analysis to examine the incidence and stage at diagnosis of cancer among people with, relative to those without, psychotic disorders. We searched the MEDLINE, EMBASE, PsycINFO, and CINAHL databases. Articles were included if they reported the incidence and/or stage at diagnosis of cancer in people with psychotic disorders. Random effects meta-analyses were used to determine risk of cancer and odds of advanced stage …

Bisc 504: Biometry, Jason Hoeksema

GMAS Course Syllabi

No abstract provided.

Math 775: Advanced Mathematical Statistics: Time Series And Data Analysis, Hailin Sang

GMAS Course Syllabi

No abstract provided.

Bayesian Estimation Of The Intensity Function Of A Non-Homogeneous Poisson Process, James Jensen

Theses

In this paper we explore Bayesian inference and its application to the problem of estimating the intensity function of a non-homogeneous Poisson process. These processes model the behavior of phenomena in which one or more events, known as arrivals, occur independently of one another over a certain period of time. We are concerned with the number of events occurring during particular time intervals across several realizations of the process. We show that given sufficient data, we are able to construct a piecewise-constant function which accurately estimates the mean rates on particular intervals. Further, we show that as we reduce these …

Public Acceptance Of Medical Screening Recommendations, Safety Risks, And Implied Liabilities Requirements For Space Flight Participation, Cory J. Trunkhill

Doctoral Dissertations and Master's Theses

The space tourism industry is preparing to send space flight participants on orbital and suborbital flights. Space flight participants are not professional astronauts and are not subject to the rules and guidelines covering space flight crewmembers. This research addresses public acceptance of current Federal Aviation Administration guidance and regulations as designated for civil participation in human space flight.

The research utilized an ordinal linear regression analysis of survey data to explore the public acceptance of the current medical screening recommended guidance and the regulations for safety risk and implied liability for space flight participation. Independent variables constituted participant demographic representations …

Machine Learning To Predict Warhead Fragmentation In-Flight Behavior From Static Data, Katharine Larsen

Doctoral Dissertations and Master's Theses

Accurate characterization of fragment fly-out properties from high-speed warhead detonations is essential for estimation of collateral damage and lethality for a given weapon. Real warhead dynamic detonation tests are rare, costly, and often unrealizable with current technology, leaving fragmentation experiments limited to static arena tests and numerical simulations. Stereoscopic imaging techniques can now provide static arena tests with time-dependent tracks of individual fragments, each with characteristics such as fragment IDs and their respective position vector. Simulation methods can account for the dynamic case but can exclude relevant dynamics experienced in real-life warhead detonations. This research leverages machine learning methodologies to …

An Attempt To Develop A Measurement Tool For Interpretation Performance Of Tourist Guides, Gizem Capar, Dilek Atci

University of South Florida (USF) M3 Publishing

The search for different experiences in touristic visits brings the necessity of differentiating the tours for tour guides with. Interpretation lies at the heart of this differentiation. This research aims to examine the structure of interpretation performance of tour guides empirically within the framework of E.R.O.T/T.O.R.E model. For this purpose, in line with the literature firstly conceptual structure of interpretation performance and interpretative guiding was determined, then expert opinion was sought with the expression pool consisting of draft statements. After expertising process, the measurement tool was first applied on a sample of 191 participants. For preliminary analysis the performance of …

The Link Between Democratic Institutions And Population Health In The American States, Julianna Pacheco, Scott Lacombe

Government: Faculty Publications

Context: This project investigates the role of state-level institutions in explaining variation in population health in the American states. Although cross-national research has established the positive effects of democracy on population health, little attention has been given to subnational units. The authors leverage a new data set to understand how political accountability and a system of checks and balances are associated with state population health. Methods: The authors estimate error correction models and two-way fixed effects models to estimate how the strength of state-level democratic institutions is associated with infant mortality rates, life expectancy, and midlife mortality. Findings: The authors …

Topics In Multilevel Mediation Analysis, Chung Li Wu

Theses and Dissertations

A proper study design assures adequate power to detect statistically significant differences. Existing power calculations for multilevel mediation analysis make a strong distributional assumption of normality. However, binary outcomes are commonly seen in real-world study. Motivated by this fact, we conduct a simulation-based power study for a multilevel mediation analysis with binary outcomes. The numbers of participants needed to achieve 80% power are summarized in tables for future reference.

Mixed-effect regression is commonly used in multilevel analysis for panel data. Yet, the estimated coefficients from the random-intercept model could represent either purely between-cluster, purely within-cluster, or weighted-average effects. Therefore, we …

Extensions Of Discrete Choice Experiment Theory For Public Health, Farahnaz Islam

Theses and Dissertations

A discrete choice experiment (DCE) allows researchers to understand how individuals value characteristics of a product or service in hypothetical scenarios and the trade-offs these individuals are willing to make between these characteristics. DCEs quickly gained traction in public health but as more researchers utilized DCEs in broader contexts, several methodological questions have arisen. This dissertation addresses some of these gaps in the literature.

One critique of DCEs is whether individuals would make the same choice in reality as they claimed they would have made in the hypothetical scenario. Perhaps the most efficient way to evaluate the predictive value of …

Classification Of Breast Cancer Histopathological Images Using Semi-Supervised Gans, Balaji Avvaru, Nibhrat Lohia, Sowmya Mani, Vijayasrikanth Kaniti

SMU Data Science Review

Breast cancer is diagnosed more frequently than skin cancer in women in the United States. Most breast cancer cases are diagnosed in women, while children and men are less likely to develop the disease. Various tissues in the breast grow uncontrollably, resulting in breast cancer. Different treatments analyze microscopic histopathology images for diagnosis that help accurately detect cancer cells. Deep learning is one of the evolving techniques to classify images where accuracy depends on the volume and quality of labeled images. This study used various pre-trained models to train the histopathological images and analyze these models to create a new …

Stock Forecasts With Lstm And Web Sentiment, Michael Burgess, Faizan Javed, Nnenna Okpara, Chance Robinson

SMU Data Science Review

Traditional time-series techniques, such as auto-regressive and moving average models, can have difficulties when applied to stock data due to the randomness inherent to the markets. In this study, Long Short-Term Memory Recurrent Neural Networks, or LSTMs, have been applied to pricing data along with sentiment scores derived from web sources such as Twitter and other financial media outlets. The project team utilized this approach to complement the technical indicators observed at the end of each trading day for three stocks from the NASDAQ stock exchange over a 12-year span. A common benchmark to assess model performance on time series …

Predicting Insulin Pump Therapy Settings, Riccardo L. Ferraro, David Grijalva, Alex Trahan

SMU Data Science Review

Millions of people live with diabetes worldwide [7]. To mitigate some of the many symptoms associated with diabetes, an estimated 350,000 people in the United States rely on insulin pumps [17]. For many of these people, how effectively their insulin pump performs is the difference between sleeping through the night and a life threatening emergency treatment at a hospital. Three programmed insulin pump therapy settings governing effective insulin pump function are: Basal Rate (BR), Insulin Sensitivity Factor (ISF), and Carbohydrate Ratio (ICR). For many people using insulin pumps, these therapy settings are often not correct, given their physiological needs. While …

Classification Of Pixel Tracks To Improve Track Reconstruction From Proton-Proton Collisions, Kebur Fantahun, Jobin Joseph, Halle Purdom, Nibhrat Lohia

SMU Data Science Review

In this paper, machine learning techniques are used to reconstruct particle collision pathways. CERN (Conseil européen pour la recherche nucléaire) uses a massive underground particle collider, called the Large Hadron Collider or LHC, to produce particle collisions at extremely high speeds. There are several layers of detectors in the collider that track the pathways of particles as they collide. The data produced from collisions contains an extraneous amount of background noise, i.e., decays from known particle collisions produce fake signal. Particularly, in the first layer of the detector, the pixel tracker, there is an overwhelming amount of background noise that …

Cov-Inception: Covid-19 Detection Tool Using Chest X-Ray, Aswini Thota, Ololade Awodipe, Rashmi Patel

SMU Data Science Review

Since the pandemic started, researchers have been trying to find a way to detect COVID-19 which is a cost-effective, fast, and reliable way to keep the economy viable and running. This research details how chest X-ray radiography can be utilized to detect the infection. This can be for implementation in Airports, Schools, and places of business. Currently, Chest imaging is not a first-line test for COVID-19 due to low diagnostic accuracy and confounding with other viral pneumonia. Different pre-trained algorithms were fine-tuned and applied to the images to train the model and the best model obtained was fine-tuned InceptionV3 model …

Application Of Probabilistic Ranking Systems On Women’S Junior Division Beach Volleyball, Cameron Stewart, Michael Mazel, Bivin Sadler

SMU Data Science Review

Women’s beach volleyball is one of the fastest growing collegiate sports today. The increase in popularity has come with an increase in valuable scholarship opportunities across the country. With thousands of athletes to sort through, college scouts depend on websites that aggregate tournament results and rank players nationally. This project partnered with the company Volleyball Life, who is the current market leader in the ranking space of junior beach volleyball players. Utilizing the tournament information provided by Volleyball Life, this study explored replacements to the current ranking systems, which are designed to aggregate player points from recent tournament placements. Three …

Dietary Inflammatory Index And Mortality From All Causes, Cardiovascular Disease, And Cancer: A Prospective Study, Zhen Lin, Yanfei Feng, Nitin Shivappa Mbbs, Mph, Ph.D., James R. Hébert Scd, Xin Xu

Faculty Publications

The Energy-adjusted Dietary Inflammatory Index (E-DIITM) is a comprehensive, literature-derived index for assessing the effect of dietary constituents on inflammatory biomarkers and inflammation-related chronic diseases. Several studies have examined the association between E-DII scores and mortality, with results that vary across populations. Therefore, in the present study, we aimed to investigate the potential association between E-DII scores and all-cause, cardiovascular disease (CVD), and cancer mortality using data from the Prostate, Lung, Colorectal and Ovarian (PLCO) Screening Trial. E-DII scores, calculated based on a food-frequency questionnaire, were analyzed both as a continuous variable and after categorization into quintiles. A multivariate Cox …

Availability Of Hospital-Based Cancer Services Before And After Rural Hospital Closure, 2008-2017, Whitney Zahnd Ph.D., Peiyin Hung Ph.D., Sylvia Kewei Shi, Anja Zgodic, Melinda A. Merrell Ph.D., Elizabeth L. Crouch, Janice C. Probst, Jan Eberth Ph.D.

Faculty Publications

Introduction

Rural populations have less access to cancer care services and experience higher cancer mortality rates than their urban counterparts, which may be exacerbated by hospital closures. Our objective was to examine the impact of hospital closures on access to cancer-relevant hospital services across hospital service areas (HSAs).

Methods

We used American Hospital Association survey data from 2008 to 2017 to examine the change in access to cancer-related screening and treatment services across rural HSAs that sustained hospitals over time, experienced any closures, or had all hospitals close. We performed a longitudinal analysis to assess the association between hospital closure …

Neighborhood Characteristics And Dementia Symptomology Among Community-Dwelling Older Adults With Alzheimer’S Disease, Dana M. Alhasan, Matthew C. Lohman Ph.D., Jana A. Hirsch, Margaret Chandlee Miller Ms, Ph.D., Bo Cai Ph.D., Chandra L. Jackson

Faculty Publications

Background: Neuropsychiatric symptoms (NPSs) lead to myriad poor health outcomes among individuals with Alzheimer’s disease (AD). Prior studies have observed associations between the various aspects of the home environment and NPSs, but macro-level environmental stressors (e.g., neighborhood income) may also disrupt the neuronal microenvironment and exacerbate NPSs. Yet, to our knowledge, no studies have investigated the relationship between the neighborhood environment and NPSs.

Methods: Using 2010 data among older adults with AD collected from a sample of the South Carolina Alzheimer’s Disease Registry, we estimated cross-sectional associations between neighborhood characteristics and NPSs in the overall population and by race/ethnicity. Neighborhood …

Yin And Yang Of Cannabinoid Cb1 Receptor: Cb1 Deletion In Immune Cells Causes Exacerbation While Deletion In Non-Immune Cells Attenuates Obesity, Kathryn Miranda, William Becker, Philip B. Busbee, Nicholas Dopkins, Osama A. Abdulla, Yin Zhong, Jiajia Zhang Ph.D., Mitzi Nagarkatti, Prakash S. Nagarkatti

Faculty Publications

While blockade of cannabinoid receptor 1 (CB1) has been shown to attenuate diet-induced obesity (DIO), its relative role in different cell types has not been tested. The current study investigated the role of CB1 in immune vs non-immune cells during DIO by generating radiation-induced bone marrow chimeric mice that expressed functional CB1 in all cells except the immune cells or expressed CB1 only in immune cells. CB1^-/- recipient hosts were resistant to DIO, indicating that CB1 in non-immune cells is necessary for induction of DIO. Interestingly, chimeras with CB1^-/- in immune cells showed exacerbation in DIO combined with …