Physical Sciences and Mathematics Commons^™

Nonparametric Derivative Estimation Using Penalized Splines: Theory And Application, Bright Antwi Boasiako

Doctoral Dissertations

This dissertation is in the field of Nonparametric Derivative Estimation using
Penalized Splines. It is conducted in two parts. In the first part, we study the L2
convergence rates of estimating derivatives of mean regression functions using penalized splines. In 1982, Stone provided the optimal rates of convergence for estimating derivatives of mean regression functions using nonparametric methods. Using these rates, Zhou et. al. in their 2000 paper showed that the MSE of derivative estimators based on regression splines approach zero at the optimal rate of convergence. Also, in 2019, Xiao showed that, under some general conditions, penalized spline estimators …

Genetic Associations Of Alzheimer’S Disease And Mild Cognitive Impairment, Scott Hebert

Masters Theses

Over 6 million people are estimated to have been living with Alzheimer’s Disease (AD) in 2020, with another 12 million living with Mild Cognitive Impairment (MCI). Research has been conducted to evaluate genetic links to AD, but more research is needed on the subject. The Alzheimer’s Disease Neuroimaging Initiative (ADNI) has been conducting a longitudinal study of AD and MCI since 2004 and offering their data to research teams around the world. Diagnostic and demographic data was collected from participants, as well as data regarding single nucleotide polymorphisms (SNPs). SNP data was transformed to a binary format regarding whether the …

Forecasting Covid-19 With Temporal Hierarchies And Ensemble Methods, Li Shandross

Masters Theses

Infectious disease forecasting efforts underwent rapid growth during the COVID-19 pandemic, providing guidance for pandemic response and about potential future trends. Yet despite their importance, short-term forecasting models often struggled to produce accurate real-time predictions of this complex and rapidly changing system. This gap in accuracy persisted into the pandemic and warrants the exploration and testing of new methods to glean fresh insights.

In this work, we examined the application of the temporal hierarchical forecasting (THieF) methodology to probabilistic forecasts of COVID-19 incident hospital admissions in the United States. THieF is an innovative forecasting technique that aggregates time-series data into …

Inverse Probability Weighting In Survival Analysis And Network Analysis, Yukun Lu

Doctoral Dissertations

Inverse probability weighting is a popular technique to accommodate selection bias due to non-random sampling and missing data. In the first chapter, we develop an inverse probability weighted estimator and an augmented inverse probability weighted estimator of regression coefficients for a linear model with randomly censored covariates, when the censoring mechanism may be dependent on the outcome. We investigate the asymptotic properties of both estimators and evaluate their finite sample performance through extensive simulation studies. We apply the proposed methods to an Alzheimer’s disease study. In the second chapter, we present an application of network analysis in a study of …

Vibrio Cholerae In Rural And Urban Bangladesh, Findings From Hospital-Based Surveillance, 2000–2021, Rina Das, Sabiha Nasrin, Parag Palit, Rukaeya Amin Sobi, Al-Afroza Sultana, Soroar Hossain Khan, Md. Ahshanul Haque, Sharika Nuzhat, Tahmeed Ahmed, A. S. G. Faruque, Mohammod Jobayer Chisti

Biostatistics and Epidemiology Faculty Publications Series

With more than 100,000 cases estimated each year, Bangladesh is one of the countries with the highest number of people at risk for cholera. Moreover, Bangladesh is formulating a countrywide cholera-control plan to satisfy the GTFCC (The Global Task Force on Cholera Control) Roadmap's goals. With a particular focus on cholera trends, variance in baseline and clinical characteristics of cholera cases, and trends in antibiotic susceptibility among clinical isolates of Vibrio cholerae, we used data from facility-based surveillance systems from icddr,b’s Dhaka, and Matlab Hospitals from years 2000 to 2021. Female patients comprised 3,553 (43%) in urban and 1,099 (51.6%) …

A Cross-Ancestry Genome-Wide Meta-Analysis, Fine-Mapping, And Gene Prioritization Approach To Characterize The Genetic Architecture Of Adiponectin, Casssandra N. Spracklen, Et. Al.

Biostatistics and Epidemiology Faculty Publications Series

No abstract provided.

Childhood And Adulthood Passive And Active Smoking, And The Abo Group As Risk Factors For Pancreatic Cancer In Women, Anne-Laure Vedie, Nasser Laouali, Amandine Gelot, Gianluca Severi, Marie-Christine Boutron-Ruault, Vinciane Rebours

Biostatistics and Epidemiology Faculty Publications Series

Objectives

Active smoking and the A blood group are associated with pancreatic adenocarcinoma (PC) risk. However, potential interactions between those risk factors and the role of passive smoking have been little investigated. We aimed to explore specific and joint associations of passive and active smoking, and effect modification by the ABO blood group in French women.

Methods

The study included 96,594 women from the E3N prospective cohort, mean age: 49 years (SD 6.7). Information on active and passive smoking was reported at inclusion and throughout follow-up. Cases were classified according to the International Classification of Diseases 10. Associations with passive …

Comparison Of Combination Methods To Create Calibrated Ensemble Forecasts For Seasonal Influenza In The U.S., Nutcha Wattanachit, Evan L. Ray, Thomas C. Mcandrew, Nicholas G. Reich

Biostatistics and Epidemiology Faculty Publications Series

The characteristics of influenza seasons vary substantially from year to year, posing challenges for public health preparation and response. Influenza forecasting is used to inform seasonal outbreak response, which can in turn potentially reduce the impact of an epidemic. The United States Centers for Disease Control and Prevention, in collaboration with external researchers, has run an annual prospective influenza forecasting exercise, known as the FluSight challenge. Uniting theoretical results from the forecasting literature with domain-specific forecasts from influenza outbreaks, we applied parametric forecast combination methods that simultaneously optimize model weights and calibrate the ensemble via a beta transformation and made …

Estimation Of Causal Effects In Complex Clustered Data, Joshua R. Nugent

Doctoral Dissertations

Analysis of clustered data from randomized trials or observational data often poses theoretical and practical statistical challenges, including but not limited to small numbers of independent units, many adjustment variables, continuous exposures, and/or differential clustering across trial arms. Further, commonly-used parametric methods rely on assumptions that may be violated in practice. Motivated by three scientific questions in public health, methods are developed and/or demonstrated for non-parametric estimation of causal effects. In Chapter 1, methods are elaborated for a cluster randomized trial (CRT) with missing individual-level data at baseline and follow-up, a complex sampling strategy, and limited number of clusters. Chapter …

Applications Of Statistical Physics To Ecology: Ising Models And Two-Cycle Coupled Oscillators, Vahini Reddy Nareddy

Doctoral Dissertations

Many ecological systems exhibit noisy period-2 oscillations and, when they are spatially extended, they undergo phase transition from synchrony to incoherence in the Ising universality class. Period-2 cycles have two possible phases of oscillations and can be represented as two states in the bistable systems. Understanding the dynamics of ecological systems by representing their oscillations as bistable states and developing dynamical models using the tools from statistical physics to predict their future states is the focus of this thesis. As the ecological oscillators with two-cycle behavior undergo phase transitions in the Ising universality class, many features of synchrony and equilibrium …

Bayesian Hierarchical Temporal Modeling And Targeted Learning With Application To Reproductive Health, Herbert P. Susmann

Doctoral Dissertations

The international community via the United Nations Sustainable Development Goals has set the target of universal access to reproductive health-care services, including family planning, by 2030. Progress towards reaching this goal is assessed by tracking appropriate demographic and health indicators at national and subnational levels. This task is challenging, however, in populations where relevant data are limited or of low quality. Statistical models are then needed to estimate and project demographic and health indicators in populations based on the available data. Our first contribution, in Chapter 1, is to unify many existing demographic and health indicator models by proposing an …

Statistical Methods To Study Transposon Sequencing Data: Nonparametric Bayesian Models With Sampling Algorithms, Shai He

Doctoral Dissertations

As the development of Next Generation Sequencing(NGS) technology, researchers can easily obtain data from millions of cells( bulk samples) or just collecting data from a single cell. However, while bulk samples can capture broad changes, it may risk providing an average measurement that is not representative of the genetic state of any individual cell. While single-cell experiments can capture the genetic state of the individual cell, a single cell sample can increase uncertainty, sampling enough cells to gain a representative sample of population is expensive. Therefore, there is a need to integrate information from both bulk and single-cell data to …

Three Dimensional Spatio-Temporal Cluster Analysis Of Sars-Cov-2 Infections, Keith W. Allison

Masters Theses

The COVID-19 pandemic has heightened the need for fine-scale analysis of the clustering of cases of infectious disease in order to better understand and prevent the localized spread of infection. The students living on the University of Massachusetts, Amherst campus provided a unique opportunity to do so, due to frequent mandatory testing during the 2020-2021 academic year, and dense living conditions. The South-West dormitory area is of particular interest due to its extremely high population density, housing around half of students living on campus during normal conditions. Using data gathered by the Public Health Promotion Center (PHPC), we analyzed the …

Gaussian Graphical Models For Omics Data: New Methodology And Applications, Katherine H. Shutta

Doctoral Dissertations

Gaussian graphical models (GGMs) are useful network estimation tools for modeling direct dependencies that characterize multivariate data. The GGM modeling framework is one way to elucidate complex systems-level properties that can be difficult to detect in univariate analyses. In this dissertation, we begin by presenting a tutorial and review of the current state of the field of GGM theory and application. Next, we present a motivating application of GGMs in a study of metabolomic networks associated with chronic distress in women in the Women's Health Initiative (WHI) and in the Nurses' Health Study cohorts. In the third chapter, we present …

Impact Of Loss To Follow-Up And Time Parameterization In Multiple-Period Cluster Randomized Trials And Assessing The Association Between Institution Affiliation And Journal Publication, Jonathan Moyer

Doctoral Dissertations

Difference-in-difference cluster randomized trials (CRTs) use baseline and post-test measurements. Standard power equations for these trials assume no loss to follow-up. We present a general equation for calculating treatment effect variance in difference-in-difference CRTs, with special cases assuming loss to follow-up with replacement of lost participants and loss to follow-up with no replacement but retaining the baseline measurements of all participants. Multiple-period CRTs can represent time as continuous using random coefficients (RC) or categorical using repeated measures ANOVA (RM-ANOVA) analytic models. Previous work recommends the use of RC over RM-ANOVA for CRTs with more than two periods because RC exhibited …

Methods To Improve Inference From Dependent Network Data, Dongah Kim

Doctoral Dissertations

Over the past decade, network research has increased dramatically. Network data are used in many fields because they contain not only covariates of each observation, but also `relationships' between observations. Therefore, statistical analysis of network data has been rapidly developed. However, network data presents many challenges, such as collecting network data, inferring the prevalence of an outcome of interest, and valid statistical testing typically with highly dependent data. The methods discussed in this thesis are developed to improve statistical inference from dependent network data.

Statistical Improvements For Ecological Learning About Spatial Processes, Gaetan L. Dupont

Masters Theses

Ecological inquiry is rooted fundamentally in understanding population abundance, both to develop theory and improve conservation outcomes. Despite this importance, estimating abundance is difficult due to the imperfect detection of individuals in a sample population. Further, accounting for space can provide more biologically realistic inference, shifting the focus from abundance to density and encouraging the exploration of spatial processes. To address these challenges, Spatial Capture-Recapture (“SCR”) has emerged as the most prominent method for estimating density reliably. The SCR model is conceptually straightforward: it combines a spatial model of detection with a point process model of the spatial distribution of …

High-Dimensional Feature Selection And Multi-Level Causal Mediation Analysis With Applications To Human Aging And Cluster-Based Intervention Studies, Hachem Saddiki

Doctoral Dissertations

Many questions in public health and medicine are fundamentally causal in that our objective is to learn the effect of some exposure, randomized or not, on an outcome of interest. As a result, causal inference frameworks and methodologies have gained interest as a promising tool to reliably answer scientific questions. However, the tasks of identifying and efficiently estimating causal effects from observed data still pose significant challenges under complex data generating scenarios. We focus on (1) high-dimensional settings where the number of variables is orders of magnitude higher than the number of observations; and (2) multi-level settings, where study participants …

Monitoring Mammals At Multiple Scales: Case Studies From Carnivore Communities, Kadambari Devarajan

Doctoral Dissertations

Carnivores are distributed widely and threatened by habitat loss, poaching, climate change, and disease. They are considered integral to ecosystem function through their direct and indirect interactions with species at different trophic levels. Given the importance of carnivores, it is of high conservation priority to understand the processes driving carnivore assemblages in different systems. It is thus essential to determine the abiotic and biotic drivers of carnivore community composition at different spatial scales and address the following questions: (i) What factors influence carnivore community composition and diversity? (ii) How do the factors influencing carnivore communities vary across spatial and temporal …

Measurement Invariance Across Immigrant And Non-Immigrant Populations On Pisa Cognitive And Non-Cognitive Scales, Maritza Casas

Doctoral Dissertations

International large-scale educational assessments (ILSAs) have played a relevant role in educational policies targeting immigrant students across countries as their results are used by governments as input for decision-making purposes. Given the potential impact that ILSAs can have, the psychometric features of these assessments must be carefully assessed and empirical evidence about the extent to which the inferences made based on test results are valid must be collected. To do so, the first step is to determine if the test results have the same meaning across countries and groups of examinees that is, if the measures are invariant so that …

Using Generalizability And Rasch Measurement Theory To Ensure Rigorous Measurement In An International Development Education Evaluation, Louise Bahry

Doctoral Dissertations

Between the United States and Great Britain, over 30 billion USD was spent in 2018 on international aid, over a billion of which is dedicated to education programs alone. Recently, there has been increased attention on the rigorous evaluation of aid-funded programs, moving beyond counting outputs to the measurement of educational impact. The current study uses two methodological approaches (Generalizability (Brennan, 1992, 2001) and Rasch Measurement Theory (Andrich, 1978; Rasch, 1980; Wright & Masters, 1982) to analyze data from math and literacy assessments, and self-report surveys used in an international evaluation of an educational initiative in the Democratic Republic of …

Evaluating Public Masking Mandates On Covid-19 Growth Rates In U.S. States, Angus K. Wong

Masters Theses

U.S. state governments have implemented numerous policies to help mitigate the spread of COVID-19. While there is strong biological evidence supporting the wearing of face masks or coverings in public spaces, the impact of public masking policies remains unclear. We aimed to evaluate how early versus delayed implementation of state-level public masking orders impacted subsequent COVID-19 growth rates. We defined “early” implementation as having a state-level mandate in place before September 1, 2020, the approximate start of the school-year. We defined COVID-19 growth rates as the relative increase in confirmed cases 7, 14, 21, 30, 45, 60-days after September 1. …

Model-Free Descriptive Modeling For Multivariate Categorical Data With An Ordinal Dependent Variable, Li Wang

Doctoral Dissertations

In the process of statistical modeling, the descriptive modeling plays an essential role in accelerating the formulation of plausible hypotheses in the subsequent explanatory modeling and facilitating the selection of potential variables in the subsequent predictive modeling. Especially, for multivariate categorical data analysis, it is desirable to use the descriptive modeling methods for uncovering and summarizing the potential association structure among multiple categorical variables in a compact manner. However, many classical methods in this case either rely on strong assumptions for parametric models or become infeasible when the data dimension is higher. To this end, we propose a model-free method …

Geometric Representation Learning, Luke Vilnis

Doctoral Dissertations

Vector embedding models are a cornerstone of modern machine learning methods for knowledge representation and reasoning. These methods aim to turn semantic questions into geometric questions by learning representations of concepts and other domain objects in a lower-dimensional vector space. In that spirit, this work advocates for density- and region-based representation learning. Embedding domain elements as geometric objects beyond a single point enables us to naturally represent breadth and polysemy, make asymmetric comparisons, answer complex queries, and provides a strong inductive bias when labeled data is scarce. We present a model for word representation using Gaussian densities, enabling asymmetric entailment …

Per- And Polyfluoroalkyl Substances And Kidney Function: Follow-Up Results From The Diabetes Prevention Program Trial, Pi-I D. Lin, Andres Cardenas, Russ Hauser, Diane R. Gold, Ken P. Kleinman, Marie-France Hivert, Antonia M. Calafat, Thomas F. Webster, Edward S. Horton, Emily Oken

Biostatistics and Epidemiology Faculty Publications Series

Per- and polyfluoroalkyl substances (PFAS) are ubiquitously detected in populations worldwide and may hinder kidney function. The objective of the study was to determine longitudinal associations of plasma PFAS concentrations with estimated glomerular filtration rate (eGFR) and evaluate whether a lifestyle intervention modify the associations. We studied 875 participants initially randomized to the lifestyle or placebo arms in the Diabetes Prevention Program (DPP, 1996–2002) trial and Outcomes Study (DPPOS, 2002–2014). We ran generalized linear mixed models accounting a priori covariates to evaluate the associations between baseline PFAS concentrations and repeated measures of eGFR, separately, for six PFAS (PFOS, PFOA, PFHxS, …

The Zoltar Forecast Archive, A Tool To Standardize And Store Interdisciplinary Prediction Research, Nicholas G. Reich, Matthew Cornell, Evan L. Ray, Katie House, Khoa Le

Biostatistics and Epidemiology Faculty Publications Series

Forecasting has emerged as an important component of informed, data-driven decision-making in a wide array of felds. We introduce a new data model for probabilistic predictions that encompasses a wide range of forecasting settings. This framework clearly defnes the constituent parts of a probabilistic forecast and proposes one approach for representing these data elements. The data model is implemented in Zoltar, a new software application that stores forecasts using the data model and provides standardized API access to the data. In one real-time case study, an instance of the Zoltar web application was used to store, provide access to, and …

Effect Of A Patient-Centered Hypertension Delivery Strategy On All-Cause Mortality: Secondary Analysis Of Search, A Community-Randomized Trial In Rural Kenya And Uganda, Matthew D. Hickey, James Ayieko, Asiphas Owaraganise, Nicholas Sim, Laura B. Balzer, Jane Kabami, Mucunguzi Atukunda, Frederick J. Opel, Erick Wafula, Et. Al.

Biostatistics and Epidemiology Faculty Publications Series

Background

Hypertension treatment reduces morbidity and mortality yet has not been broadly implemented in many low-resource settings, including sub-Saharan Africa (SSA). We hypothesized that a patient-centered integrated chronic disease model that included hypertension treatment and leveraged the HIV care system would reduce mortality among adults with uncontrolled hypertension in rural Kenya and Uganda.

Methods and findings

This is a secondary analysis of the SEARCH trial (NCT:01864603), in which 32 communities underwent baseline population-based multidisease testing, including hypertension screening, and were randomized to standard country-guided treatment or to a patient-centered integrated chronic care model including treatment for hypertension, diabetes, and HIV. …

Civilian-Military Malaria Outbreak Response In Thailand: An Example Of Multi-Stakeholder Engagement For Malaria Elimination, Michelle E. Roh, Kanyat Lausatianragit, Nithinart Chaitaveep, Krisada Jongsakul, Prayuth Sudathip, Chatree Raseebut, Sutchana Tabprasit, Prasert Nonkaew, Michele Spring, Montri Arsanok, Parat Boonyarangka, Sabaithip Sriwichai, Piayporn Sai-Ngam, Chaiyaporn Chaisatit, Peerapol Pokpong, Preecha Prempree, Sara Rossi, Mita Feldman, Mariusz Wojnarski, Adam Bennett, Roly Gosling, Danai Jearakul, Wanchai Lausatianragit, Philip L. Smith, Nicholas J. Martin, Andrew A. Lover, Mark M. Fukuda

Biostatistics and Epidemiology Faculty Publications Series

Background

In April 2017, the Thai Ministry of Public Health (MoPH) was alerted to a potential malaria outbreak among civilians and military personnel in Sisaket Province, a highly forested area bordering Cambodia. The objective of this study was to present findings from the joint civilian-military outbreak response.

Methods

A mixed-methods approach was used to assess risk factors among cases reported during the 2017 Sisaket malaria outbreak. Routine malaria surveillance data from January 2013 to March 2018 obtained from public and military medical reporting systems and key informant interviews (KIIs) (n = 72) were used to develop hypotheses about potential factors …

Prevalence Of Glucose-6-Phosphate Dehydrogenase Deficiency (G6pdd) Carestart Qualitative Rapid Diagnostic Test Performance, And Genetic Variants In Two Malaria-Endemic Areas In Sudan, Musab M. Ali Albsheer, Andrew A. Lover, Sara B. Eltom, Leena Omereltinai, Nouh Mohamed, Mohamed S. Muneer, Abdelrahim O. Mohamad, Muzamil Mahdi Abdel Hamid

Biostatistics and Epidemiology Faculty Publications Series

Glucose-6-phosphate dehydrogenase deficiency (G6PDd) is the most common enzymopathy globally, and deficient individuals may experience severe hemolysis following treatment with 8-aminoquinolines. With increasing evidence of Plasmodium vivax infections throughout sub-Saharan Africa, there is a pressing need for population-level data at on the prevalence of G6PDd. Such evidence-based data will guide the expansion of primaquine and potentially tafenoquine for radical cure of P. vivax infections. This study aimed to quantify G6PDd prevalence in two geographically distinct areas in Sudan, and evaluating the performance of a qualitative CareStart rapid diagnostic test as a point-of-care test. Blood samples were analyzed from 491 …

In Utero Exposure To Persistent Organic Pollutants And Childhood Lipid Levels, Maegan E. Boutot, Brian W. Whitcomb, Nadia Abdelouahab, Andrea A. Baccarelli, Amélie Boivin, Artuela Caku, Virginie Gillet, Guillaume Martinez, Jean-Charles Pasquier, Jiping Zhu, Larissa Takser, Lindsay St-Cyr, Alexander Suvorov

Biostatistics and Epidemiology Faculty Publications Series

Animal studies have shown that developmental exposures to polybrominated diphenyl ethers (PBDE) permanently affect blood/liver balance of lipids. No human study has evaluated associations between in utero exposures to persistent organic pollutants (POPs) and later life lipid metabolism. In this pilot, maternal plasma levels of PBDEs (BDE-47, BDE-99, BDE-100, and BDE-153) and polychlorinated biphenyls (PCB-138, PCB-153, and PCB-180) were determined at delivery in participants of GESTation and Environment (GESTE) cohort. Total cholesterol (TCh), triglycerides (TG), low- and high-density lipoproteins (LDL-C and HDL-C), total lipids (TL), and PBDEs were determined in serum of 147 children at ages 6–7. General linear regression …