Biostatistics Commons^™

Advancing Periodic Time Series Analysis: Application, Bias Assessment, And Optimal Window Selection In The Variable Bandpass Periodic Block Bootstrap Method, Yanan Sun

Electronic Theses & Dissertations (2024 - present)

Time series analysis is essential for understanding long-term patterns, periodic behavior, and underlying correlations in complex datasets. The periodically correlated (PC) time series is a type of time series where the correlation structure repeats over fixed intervals. The Variable Bandpass Periodic Block Bootstrap (VBPBB) has recently been proposed as a resampling method that preserves PC structures through the use of periodogram, bandpass filters, and block bootstrap resampling. Although promising, VBPBB remains underutilized, and its limitations have not been fully examined. This dissertation advances both the application and methodological development of the VBPBB.

The first project applies the VBPBB to a …

Bayesian Modelling On Periodically And Multiple Periodically Correlated Time Series Data, Jie Yao

Electronic Theses & Dissertations (2024 - present)

Time series with multiple periodically correlated (MPC) components present a complex challenge, with relatively limited prior research. Most existing models are designed for simpler periodically correlated (PC) components and often struggle with over-parameterization, optimization issues, and capturing complex PC patterns within a time series. Frequency separation techniques can help preserve the correlation structure of individual PC components, while Bayesian methods can integrate new and prior information to refine beliefs about these components. This study proposes a two-stage approach that combines frequency separation and Bayesian techniques to forecast PC and MPC time series data. This method aims to demonstrate improved effectiveness …

The Maxima Method For Identification Of Principal Periodic Components In Time Series Analysis, Megan Di Maio

Electronic Theses & Dissertations (2024 - present)

This dissertation investigates methods for mean estimation in periodically correlated time series, focusing on the Variable Bandpass Periodic Block Bootstrap (VBPBB) and a novel data-driven maxima method. Time series require specific methods because of the temporal correlation in the data. Traditional methods like the General Seasonal Block Bootstrap (GSBB) account for this correlation but often produce wide confidence intervals because they cannot isolate multiple periodicities, allowing noise and other frequencies to interfere with analysis. The VBPBB method addresses this by applying a Kolmogorov-Zurbenko Fourier Transform (KZFT) filter to the data before bootstrapping, which suppresses interfering frequencies and results in narrower, …

Time Series Decomposition And Forecasting Of Alzheimer’S Disease Mortality Using A Kolmogorov-Zurbenko Filter, Jack D. Farrell

Electronic Theses & Dissertations (2024 - present)

Alzheimer’s disease mortality has substantially risen in recent history, placing a significant burden on public health infrastructure and highlighting the need for improved analytical methods to better understand mortality data patterns and offer reliable predictions. Time series methods often struggle to balance both accuracy and interpretability, which hinders the ability to gather meaningful insights from time series data. To address these limitations, this study applies the Kolmogorov-Zurbenko (KZ) filter to monthly U.S. Alzheimer’s mortality data spanning from 1999 to 2023 and decomposes the series into long-term trend, seasonal, and noise components on a logarithmic scale. Long-term trend accounts for 86.49% …

Spectral Analysis Of Traffic Accidents In New York's Capital Region, Michael Barr

Electronic Theses & Dissertations (2024 - present)

In this paper we estimate the spectral density of traffic accident events in the Capital Distict, NY area using a band-pass filter known as the Kolmogorov-Zurbenko Fourier Transform (KZFT). The source data is provided by Moosavi, et al. (2019) and originally captured from various public entities and sensors in the road network. Spectral density estimation with KZFT suppresses noise to reveal the constituent frequencies embedded in the noisy signal. Signal reconstruction based on KZFT produces an approximate weekly accident arrivals for this noisy signal, or in other words a pattern which is proportionate to the event expectation viewed over a …

Theoretical Foundations And Applied Performance Of Periodicity-Aware Imputation: Variable Bandpass Block Bootstrap Methods For Incomplete Time Series, Asmaa Ahmad

Electronic Theses & Dissertations (2024 - present)

Time series data are prevalent across a wide range of disciplines, including health surveillance, public policy, and environmental monitoring. In the presence of underlying cyclical patterns, the integrity of time series analysis depends critically on the ability to detect, model, and impute structured missing data without compromising the temporal structure. This dissertation introduces and validates a novel imputation framework that integrates the Variable Bandpass Periodic Block Bootstrap (VBPBB) into multiple imputation procedures, improving the accuracy, robustness, and interpretability of time series models under high rates of missingness and noise. The overarching goal of this dissertation was to develop and evaluate …

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 …

Complex Missing Data Problems In Education Surveys, Thomas Wesley Robertson

Electronic Theses & Dissertations (2024 - present)

Missing data are a nearly universal problem in human subjects research, including in education. However, reporting and addressing missing data is an issue, despite guidelines from the APA style guide and the What Works Clearinghouse, as well as guidance from prominent statisticians on the best methods to use. Prior research conducted in 2004 and 2014 found that in the field of education, most studies do not report or address missing data. In addition, no study has looked specifically at how missing data are reported and addressed in complex surveys. The current study has two main objectives: first, to determine if …

Metabolic Alterations And Cardiovascular Risk After Hepatitis C Cure In Subjects With Or At Risk For Hiv, Christophe Maxime Fokoua Dongmo

Legacy Theses & Dissertations (2009 - 2024)

Background. Hepatitis C virus (HCV) infection engenders substantial metabolic changes. These changes are altered when the virus is cleared after successful treatment. We measured these metabolic alterations that occur after HCV cure; further, we assessed whether these alterations differed in subgroups defined by patients’ characteristics.

Evaluating The Health Impacts Of Fruit And Vegetable Intake At The Individual Level And Food Pantry Level Among Food Pantry Users, Jiacheng Chen

Legacy Theses & Dissertations (2009 - 2024)

Background: Chronic diseases impose heavy burdens on individuals and the healthcare system in the US. Many factors were found to be associated with chronic diseases, including demographics, family history, social environmental factors, and individual behavioral factors such as diet and physical activity. Among those factors, fruit and vegetable intake can have substantial health impacts via a variety of causal pathways. Fruit and vegetable (F&V) consumption is generally lower among individuals living in households experiencing food insecurity and rely on food assistance programs. Decreased F&V intake among food pantry users may negatively impact health. However, conducting quantitative analysis on this population …

Multiple Imputation In High-Dimensional Data With Variable Selection, Qiushuang Li

Legacy Theses & Dissertations (2009 - 2024)

This dissertation focuses on the development of multiple imputation models and algorithms for high-dimensional data with variable selection structures. Leveraging on the multivariate linear mixed-effects model with missing responses for clustered data, we incorporate the variable selection routines using spike-and-slab priors within the Bayesian variable selection framework. Specific choice of these priors allow us to "force'' variables of importance (e.g. design variables or variables known to play role in missingness mechanism) into the imputation models. Our ultimate goal is to improve computational speed by removing unnecessary variables. Markov chain Monte Carlo techniques have been designed to sample from the implied …

Conditional And Marginal Imputation Models For Multilevel Data, Gang Liu

Legacy Theses & Dissertations (2009 - 2024)

This dissertation study extends sequential hierarchical regression imputation (SHRIMP) methods to multilevel datasets with three levels of nesting and proposes a marginal method based on marginalized multilevel model (MMM) framework. Specifically, the proposed model consists of two levels such that the first level relates the marginal mean of responses with covariates through a generalized regression model and the second level includes subject specific random effects within the same generalized regression model. To draw the inference on the population-averaged or subject-specified coefficients, the hierarchical regression and/or MMM is applied as the imputation and estimation models. We employ Markov Chain Monte Carlo …

Impact Of Inconsistent Imputation Models In Mediation Analysis, Bo Ye

Legacy Theses & Dissertations (2009 - 2024)

In this dissertation, we study the impact of inconsistent imputation methods in mediation analysis and its application. We present the study in three papers.

Finite Mixture Models : Applications To Length Of Stay For Delivery Hospitalizations, Eva Williford

Legacy Theses & Dissertations (2009 - 2024)

In the United States (U.S.), childbirth is the most common reason for hospitalization, and the maternal mortality rate per 100,000 (2017-2018) is markedly elevated in the U.S. (17.4) compared to neighboring Canada (10), the United Kingdom (7), and Japan (5) (Trends in Maternal Mortality, 2000 to 2017: Estimates by WHO, UNICEF, UNFPA, World Bank Group and the United Nations Population Division). These data, the increased focus on addressing severe maternal morbidity and mortality to improve patient outcomes and reduce healthcare costs is well deserved. These women often have a longer delivery length of stay (LOS) and experience complications of varying …

A Comparative Spatial And Climate Analysis Of Human Granulocytic Anaplasmosis And Human Babesiosis In New York State (2013-2018), Collin J. O'Connor

Legacy Theses & Dissertations (2009 - 2024)

Human granulocytic anaplasmosis (HGA) and human babesiosis are tick-borne diseases spread by Ixodes scapularis (the blacklegged or deer tick) and are the result of infection with Anaplasma phagocytophilum and Babesia microti, respectively. In New York State (NYS), incidence rates of these diseases increased concordantly until around 2013, when rates of HGA began to increase more rapidly than human babesiosis, and the spatial extent of the diseases diverged. Surveillance data of tick-borne pathogens (2007 to 2018) and reported human cases of HGA (n=4,297) and human babesiosis (n=2,986) (2013 to 2018) from the New York State Department of Health (NYSDOH) showed a …

The Effect Of Maternal Dietary Habits During Pregnancy On Neonate Leptin Methylation Patterns And Gestational Age, Sean Fitzpatrick

Legacy Theses & Dissertations (2009 - 2024)

The health of a newborn baby is inextricably linked to the health status of its mother and in turn the mother’s diet during pregnancy. Leptin (LEP) is an adipokine hormone involved in metabolism regulation and has been linked fetal development through the hypothalamic-pituitary-adrenal axis (HPA). Prior work suggests that gestational epigenetic alterations the LEP gene may be sensitive to adverse exposures during pregnancy, which in turn could explain variation in neonate outcomes. However, no prior work has examined this possibility explicitly. The objective of this study was to investigate the association between dietary patterns of mothers during pregnancy and their …

Depression, Sensation-Seeking Behavior And Violence As Mediators Of The Association Between Childhood Adversity And Substance Use Disorder, Calvin Wong

Legacy Theses & Dissertations (2009 - 2024)

Background:

Spatio-Temporal Frequency Separation With Application Of Kolmogorov-Zurbenko Filters To The Multivariate Analysis Of Melanoma Prevalence, Edward Valachovic

Legacy Theses & Dissertations (2009 - 2024)

Time Series Analysis is the observation of variables recorded across time. Observations are visualized and analysis often performed in the native time domain. It is common for a time series to be the dependent variable of more than one factor. Several factors can have concurrent and combined effects. The time domain presents an obstacle due to constructive and destructive interference of factors at each time point. Unless effects are clearly pronounced and separable, the entanglement of factors along with the presence and intensity of random variation can obscure true relationships.

Stress-Strength Estimation And Its Applications In Clinical Trials, Dinesh Kumar

Legacy Theses & Dissertations (2009 - 2024)

Stress Strength model P(X

Association Between Hiv And Violence Among Female Commercial Sex Workers In Ukraine : Analysis Of Bio-Behavioral Surveillance Conducted In 2015-2016, Ganna Momotyuk

Legacy Theses & Dissertations (2009 - 2024)

A cross-sectional analysis investigated the association between HIV and violence in female commercial sex workers (FCSW) in Ukraine between 10/2015 and 01/2016. Methods: 3,885 FCSW from a total of 4,300 were questioned about behavioral and social demographics and tested for HIV in mobile testing van. Results: of the 3,885 respondents, 5.89% were HIV positive, and 47.00% had experienced violence. We tested for and found that drug use was an effect modifier for the association between HIV and violence. Analyses were stratified by injecting drug use and no injecting drug use. High risk for HIV was found in the non-IDU stratum …

Raman Spectroscopy And Chemometrics For Forensic Bloodstain Analysis : Species Differentiation, Donor Age Estimation, And Dating Of Bloodstains, Kyle C. Doty

Legacy Theses & Dissertations (2009 - 2024)

The field of forensic science is constantly growing, so the advancement of old and unreliable techniques is at the forefront of what will lead to future progress and improvement. Current methods for identification and analysis of bloodstains are underwhelming due to the insignificant amount of information provided in a destructive, unreliable, and unsafe manner. As is the purpose of this research, creating new methodologies that are rapid, nondestructive, robust, statistically reliable, and safe would significantly advance the way bloodstains are currently analyzed, while providing more useful and relevant information for investigations and criminal proceedings. Raman spectroscopy, along with advanced statistical …

Computationally Efficient Multiple Imputation Routines In Clustered Data, Tugba Akkaya-Hocagil

Legacy Theses & Dissertations (2009 - 2024)

Presence of missing data in correlated data settings is a non-trivial problem. Inference by multiple imputation offers a viable solution to analysts. However, the missing data problem is typically more complicated due to diverse measurement scales, skip patterns, bounds and restrictions. Sequential regression imputation also known as variable-by-variable imputation has emerged as a popular imputation modeling technique, especially in the complex data structures. In this dissertation, we develop three methods to handle incomplete data in hierarchically nested and non-nested multilevel data structures using sequential regression imputation approach.

Socio-Demographic Determinants Of Racial Disparities In Stage At Diagnosis Of Prostate Cancer In New York State, Christophe Maxime Fokoua Dongmo

Legacy Theses & Dissertations (2009 - 2024)

ABSTRACT

Kz Spatial Wave Separation With Applications To Atmospheric Data, Ming Luo

Legacy Theses & Dissertations (2009 - 2024)

Unlike one-dimensional wave reconstruction, reconstruction 2D spatial wave via Fourier Transform doesn’t look like a non-parametric algorithm. In other words, we need the wave frequency and wave direction information to recover the spatial wave via Fourier Transform, especially when the stress of noise is present. The direct consequence is that accurate estimations of wave parameters are need for reconstructing of spatial waves. To this end, we propose to improve the accuracy of motion image scale detection and parameter estimations with optimization based on Kolmogorov-Zurbenko periodogram (KZP) information. Related methods and algorithms are denoted under the name of Kolmogorov-Zurbenko wave separations. …

Causal Inference In Observational Studies With Clustered Data, Meng Wu

Legacy Theses & Dissertations (2009 - 2024)

In this thesis, we study causal inference in observational studies with clustered data.

Estimating Survival Distributions, Important Covariates And Time-Varying Associations, Yan Wu

Legacy Theses & Dissertations (2009 - 2024)

There are three papers each on a different topic in this thesis. The first paper proposes a new objective methodology to estimate any subject specific survival distribution with potential time-varying effect by adjusting approximated polynomial censored survival function with estimated censoring distribution under three different assumptions: uniform censoring, independent censoring and non-informative censoring. The coefficients of the polynomial censored survival function and underlying censoring probability are estimated at each event or censoring time point across the study time frame, which naturally accommodates potential non-proportional hazards along with time-varying effect. An extensive simulation study indicates that the proposed methods usually perform …

Developing A Weibull Model Extension To Estimate Cancer Latency Times, Diana L. Nadler

Legacy Theses & Dissertations (2009 - 2024)

More than one-third of all Americans will be diagnosed with cancer sometime in their lives. Though their illness may be invisible now, it presents a great, and largely unexamined, opportunity to find and treat their cancers early. Early detection represents one of the most promising approaches to reduce the growing cancer burden by identifying cancer while it is localized and curable, preventing not only mortality, but also reducing morbidity and costs.

Two Step Parsimonious Variable Selection For Right Censored Continuous Survival Time Models, Anju Menon

Legacy Theses & Dissertations (2009 - 2024)

Variable selection is fundamental in any kind of statistical modeling. There has been ex- tensive research by different authors on methods of variable selection from linear regression models to more complex non-linear applications. Modeling survival data especially poses challenges because of a more complicated data structure as the time variable T is usually subject to censoring. This thesis presents a two step objective approach to choose between several candidate models based on the the ability of the model to predict survival times using loss functions. Once potentially important variables are selected using a screening method called Iterative Sure Independence Screening(ISIS) …

Roughened Random Forests For Binary Classification, Kuangnan Xiong

Legacy Theses & Dissertations (2009 - 2024)

Binary classification plays an important role in many decision-making processes. Random forests can build a strong ensemble classifier by combining weaker classification trees that are de-correlated. The strength and correlation among individual classification trees are the key factors that contribute to the ensemble performance of random forests. We propose roughened random forests, a new set of tools which show further improvement over random forests in binary classification. Roughened random forests modify the original dataset for each classification tree and further reduce the correlation among individual classification trees. This data modification process is composed of artificially imposing missing data that are …

Raman Spectroscopy Of Blood Serum And Cerebrospinal Fluid And Multivariate Data Analysis For Alzheimer's Disease Diagnostics, Elena Ryzhikova

Legacy Theses & Dissertations (2009 - 2024)

The efficient and accurate diagnosis at the early stages of dementia is a key moment for effective treatment and productive research to find a new ways to combat the disease. It is especially true for Alzheimer's disease (AD) for which there is no effective cure, but several treatments are known to allow slowing down the degenerative processes. Alzheimer's disease (AD) displays only non-specific clinical symptoms of mental decline for decades after the initiation and is very challenging to differentiate even at the later stages when it becomes very aggressive. Despite the great need, current diagnostic tests are unable to diagnose …