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Self-Reported Sleep Apnea And Dementia Risk: Findings From The Prevention Of Alzheimer's Disease With Vitamin E And Selenium Trial, Xiuhua Ding, Richard J. Kryscio, Joshua Turner, Gregory A. Jicha, Gregory E. Cooper, Allison M. Caban-Holt, Frederick A. Schmitt, Erin L. Abner

Sanders-Brown Center on Aging Faculty Publications

OBJECTIVES: To investigate the association between baseline sleep apnea and risk of incident dementia in the Prevention of Alzheimer's Disease with Vitamin E and Selenium (PREADViSE) study and to explore whether the association depends on apolipoprotein E (APOE) ɛ4 allele status.

DESIGN: Secondary analysis based on data collected during PREADViSE.

SETTING: Participants were assessed at 128 local clinical study sites during the clinical trial phase and later were followed by telephone from a centralized location.

PARTICIPANTS: Men enrolled in PREADViSE (without dementia or other active neurological conditions that affect cognition such as major psychiatric disorders, including depression; N = …

Genomics And Csf Analyses Implicate Thyroid Hormone In Hippocampal Sclerosis Of Aging, Peter T. Nelson, Yuriko Katsumata, Kwangsik Nho, Sergey C. Artiushin, Gregory A. Jicha, Wang-Xia Wang, Erin L. Abner, Andrew J. Saykin, Walter A. Kukull, Alzheimer’S Disease Neuroimaging Initiative (Adni), David W. Fardo

Sanders-Brown Center on Aging Faculty Publications

We report evidence of a novel pathogenetic mechanism in which thyroid hormone dysregulation contributes to dementia in elderly persons. Two single nucleotide polymorphisms (SNPs) on chromosome 12p12 were the initial foci of our study: rs704180 and rs73069071. These SNPs were identified by separate research groups as risk alleles for non-Alzheimer’s neurodegeneration. We found that the rs73069071 risk genotype was associated with hippocampal sclerosis (HS) pathology among people with the rs704180 risk genotype (National Alzheimer’s Coordinating Center/Alzheimer’s Disease Genetic Consortium data; n = 2113, including 241 autopsy-confirmed HS cases). Furthermore, both rs704180 and rs73069071 risk genotypes were associated with widespread brain …

Using Low-Dose Radiation To Potentiate The Effect Of Induction Chemotherapy In Head And Neck Cancer: Results Of A Prospective Phase 2 Trial, Susanne M. Arnold, Mahesh Kudrimoti, Emily V. Dressler, John F. Gleason, Natalie L. Silver, William F. Regine, Joseph Valentino

Internal Medicine Faculty Publications

Purpose: Low-dose fractionated radiation therapy (LDFRT) induces effective cell killing through hyperradiation sensitivity and potentiates effects of chemotherapy. We report our second investigation of LDFRT as a potentiator of the chemotherapeutic effect of induction carboplatin and paclitaxel in locally advanced squamous cell cancer of the head and neck (SCCHN).

Experimental Design: Two cycles of induction therapy were given every 21 days: paclitaxel (75 mg/m²) on days 1, 8, and 15; carboplatin (area under the curve 6) day 1; and LDFRT 50 cGy fractions (2 each on days 1, 2, 8, and 15). Objectives included primary site complete response …

Pleiotropic Effects Of Csf Levels Of Alzheimer’S Disease Proteins, Olga A. Vsevolozhskaya, Ilai Keren, David W. Fardo, Dmitri V. Zaykin

Biostatistics Presentations

Cerebrospinal fluid (CSF) analytes harbor potential as diagnostic biomarkers for Alzheimer’s Disease (AD). Quantitative measures of CSF proteins comprise a set of often highly correlated endophenotypes that have previously shown promise in genetic analyses (Cruchaga et al., 2013; Kauwe et al., 2014). Pleiotropic impact of genetic variations on this set may provide additional insights into AD pathology at its earliest stages. To determine which specific endophenotypes are pleiotropic, one can employ methods based on the reverse regression of genotype on phenotypes. Recently, we proposed a method based functional linear models (Vsevolozhskaya et al, 2016) that utilizes reverse regression and simultaneously …

Weighted-Samgsr: Combining Significance Analysis Of Microarray-Gene Set Reduction Algorithm With Pathway Topology-Based Weights To Select Relevant Genes, Suyan Tian, Howard H. Chang, Chi Wang

Biostatistics Faculty Publications

Background: It has been demonstrated that a pathway-based feature selection method that incorporates biological information within pathways during the process of feature selection usually outperforms a gene-based feature selection algorithm in terms of predictive accuracy and stability. Significance analysis of microarray-gene set reduction algorithm (SAMGSR), an extension to a gene set analysis method with further reduction of the selected pathways to their respective core subsets, can be regarded as a pathway-based feature selection method.

Methods: In SAMGSR, whether a gene is selected is mainly determined by its expression difference between the phenotypes, and partially by the number of pathways to …

Diffuse Optical Measurements Of Head And Neck Tumor Hemodynamics For Early Prediction Of Chemoradiation Therapy Outcomes, Lixin Dong, Mahesh Kudrimoti, Daniel Irwin, Li Chen, Sameera Kumar, Yu Shang, Chong Huang, Ellis L. Johnson, Scott D. Stevens, Brent J. Shelton, Guoqiang Yu

Biomedical Engineering Faculty Publications

This study used a hybrid near-infrared diffuse optical instrument to monitor tumor hemodynamic responses to chemoradiation therapy for early prediction of treatment outcomes in patients with head and neck cancer. Forty-seven patients were measured once per week to evaluate the hemodynamic status of clinically involved cervical lymph nodes as surrogates for the primary tumor response. Patients were classified into two groups: complete response (CR) (n = 29) and incomplete response (IR) (n = 18). Tumor hemodynamic responses were found to be associated with clinical outcomes (CR/IR), wherein the associations differed depending on human papillomavirus (HPV-16) status. In HPV-16 …

Diabetes Is Associated With Cerebrovascular But Not Alzheimer's Disease Neuropathology, Erin L. Abner, Peter T. Nelson, Richard J. Kryscio, Frederick A. Schmitt, David W. Fardo, Randall L. Woltjer, Nigel J. Cairns, Lei Yu, Hiroko H. Dodge, Chengjie Xiong, Kamal Masaki, Suzanne L. Tyas, David A. Bennett, Julie A. Schneider, Zoe Arvanitakis

Sanders-Brown Center on Aging Faculty Publications

INTRODUCTION: The relationship of diabetes to specific neuropathologic causes of dementia is incompletely understood.

METHODS: We used logistic regression to evaluate the association between diabetes and infarcts, Braak neurofibrillary tangle stage, and neuritic plaque score in 2365 autopsied persons. In a subset of >1300 persons with available cognitive data, we examined the association between diabetes and cognition using Poisson regression.

RESULTS: Diabetes increased odds of brain infarcts (odds ratio [OR] = 1.57, P < .0001), specifically lacunes (OR = 1.71, P < .0001), but not Alzheimer's disease neuropathology. Diabetes plus infarcts was associated with lower cognitive scores at end of life than infarcts or diabetes alone, and diabetes plus high level of Alzheimer's neuropathologic changes was associated with lower mini-mental state examination scores than the pathology alone.

DISCUSSION: This study supports the conclusions that diabetes increases the risk of cerebrovascular but not Alzheimer's disease pathology, and at least some of diabetes' relationship to …

Evaluating A Bystander Intervention Program On Reproductive Coercion: Using Quasi-Experimental Design Strategies To Address Methodologic Issues In Randomized Community Prevention Trials, Catherine P. Starnes

Theses and Dissertations--Epidemiology and Biostatistics

Community (or cluster) randomized trials are trials in which communities or groups of individuals (clusters) are randomized to receive the intervention of interest. Community randomized trials frequently more closely resemble a natural experiment than a randomized controlled trial (RCT) following intervention allocation. In particular, the effects of non-compliance can pose methodologic challenges in estimating the intervention effect which may require a quasiexperimental approach in order to minimize bias.

The motivating example to illustrate these issues is the Green Dot High School (GDHS) study. The GDHS study was a longitudinal, cluster-randomized controlled trial designed to assess the effectiveness of a bystander …

Statistical Inference On Dynamical Systems, Hongyuan Wang

Theses and Dissertations--Statistics

The ordinary differential equation (ODE) is one representative and popular tool in modeling dynamical systems, which are widely implemented in physics, biology, economics, chemistry and biomedical sciences, etc. Because of the importance of dynamical systems in scientific studies, they are the main focuses of my dissertation.

The first chapter of the dissertation is introduction and literature review, which mainly focuses on numerical integration algorithms of ODEs that are difficult to solve analytically, as well as derivative-free optimization algorithms for the so-called inverse problem.

The second chapter is on the estimation method based on numerical solvers of differential equations. We start …

Multi-State Models With Missing Covariates, Wenjie Lou

Theses and Dissertations--Statistics

Multi-state models have been widely used to analyze longitudinal event history data obtained in medical studies. The tools and methods developed recently in this area require the complete observed datasets. While, in many applications measurements on certain components of the covariate vector are missing on some study subjects. In this dissertation, several likelihood-based methodologies were proposed to deal with datasets with different types of missing covariates efficiently when applying multi-state models.

Firstly, a maximum observed data likelihood method was proposed when the data has a univariate missing pattern and the missing covariate is a categorical variable. The construction of the …

Continuous Time Multi-State Models For Interval Censored Data, Lijie Wan

Theses and Dissertations--Statistics

Continuous-time multi-state models are widely used in modeling longitudinal data of disease processes with multiple transient states, yet the analysis is complex when subjects are observed periodically, resulting in interval censored data. Recently, most studies focused on modeling the true disease progression as a discrete time stationary Markov chain, and only a few studies have been carried out regarding non-homogenous multi-state models in the presence of interval-censored data. In this dissertation, several likelihood-based methodologies were proposed to deal with interval censored data in multi-state models.

Firstly, a continuous time version of a homogenous Markov multi-state model with backward transitions was …

Improved Models For Differential Analysis For Genomic Data, Hong Wang

Theses and Dissertations--Statistics

This paper intend to develop novel statistical methods to improve genomic data analysis, especially for differential analysis. We considered two different data type: NanoString nCounter data and somatic mutation data. For NanoString nCounter data, we develop a novel differential expression detection method. The method considers a generalized linear model of the negative binomial family to characterize count data and allows for multi-factor design. Data normalization is incorporated in the model framework through data normalization parameters, which are estimated from control genes embedded in the nCounter system. For somatic mutation data, we develop beta-binomial model-based approaches to identify highly or lowly …

Topics In Logistic Regression Analysis, Zhiheng Xie

Theses and Dissertations--Statistics

Discrete-time Markov chains have been used to analyze the transition of subjects from intact cognition to dementia with mild cognitive impairment and global impairment as intervening transient states, and death as competing risk. A multinomial logistic regression model is used to estimate the probability distribution in each row of the one-step transition matrix that correspond to the transient states. We investigate some goodness of fit tests for a multinomial distribution with covariates to assess the fit of this model to the data. We propose a modified chi-square test statistic and a score test statistic for the multinomial assumption in each …