Biostatistics Commons^™

Hypothesis Testing And Power Calculations For Taxonomic-Based Human Microbiome Data, P. S. Larossa, J. Paul Brooks, Elena Deych, Edward L. Boone, David J. Edwards, Qin Wang, Erica Sodergren, George Weinstock, William D. Shannon

Statistical Sciences and Operations Research Publications

This paper presents new biostatistical methods for the analysis of microbiome data based on a fully parametric approach using all the data. The Dirichlet-multinomial distribution allows the analyst to calculate power and sample sizes for experimental design, perform tests of hypotheses (e.g., compare microbiomes across groups), and to estimate parameters describing microbiome properties. The use of a fully parametric model for these data has the benefit over alternative non-parametric approaches such as bootstrapping and permutation testing, in that this model is able to retain more information contained in the data. This paper details the statistical approaches for several tests of …

Generalized Linear Latent Mixed Modeling Of Functional Independent Measures And Patient Outcomes, Maduranga Kasun Dassanayake

Open Access Theses & Dissertations

The Functional Independent Measure (FIM) is one of the most widely accepted functional assessment measures used in the rehabilitation community. Past research studies have investigated the relationship between place of discharge, admission FIM scores or FIM difference scores, and patients' characteristics and found relationships between those variables. However, most of these studies fail to account for the multi-layered multidimensionality of the FIM and the measurement error associated with the FIM items. This study utilizes Generalized Linear Latent Mixed Models (GLLAMM) and Structural Equation Models (SEM) to assess which patient characteristics are associated with FIM difference scores and the structural relationship …

Analysis Of Binary Data Via Spatial-Temporal Autologistic Regression Models, Zilong Wang

Theses and Dissertations--Statistics

Spatial-temporal autologistic models are useful models for binary data that are measured repeatedly over time on a spatial lattice. They can account for effects of potential covariates and spatial-temporal statistical dependence among the data. However, the traditional parametrization of spatial-temporal autologistic model presents difficulties in interpreting model parameters across varying levels of statistical dependence, where its non-negative autocovariates could bias the realizations toward 1. In order to achieve interpretable parameters, a centered spatial-temporal autologistic regression model has been developed. Two efficient statistical inference approaches, expectation-maximization pseudo-likelihood approach (EMPL) and Monte Carlo expectation-maximization likelihood approach (MCEML), have been proposed. Also, Bayesian …

Evaluation Of Repeated Biomarkers: Non-Parametric Comparison Of Areas Under The Receiver Operating Curve Between Correlated Groups Using An Optimal Weighting Scheme, Ping Xu

USF Tampa Graduate Theses and Dissertations

Receiver Operating Characteristic (ROC) curves are often used to evaluate the prognostic performance of a continuous biomarker. In a previous research, a non-parametric ROC approach was introduced to compare two biomarkers with repeated measurements. An asymptotically normal statistic, which contains the subject-specific weights, was developed to estimate the areas under the ROC curve of biomarkers. Although two weighting schemes were suggested to be optimal when the within subject correlation is 1 or 0 by the previous study, the universal optimal weight was not determined. We modify this asymptotical statistic to compare AUCs between two correlated groups and propose a solution …

Linear Mixed-Effects Models: Applications To The Behavioral Sciences And Adolescent Community Health, Lizmarie Gabriela Maldonado

USF Tampa Graduate Theses and Dissertations

Linear mixed-effects (LME) modeling is a widely used statistical method for analyzing repeated measures or longitudinal data. Such longitudinal studies typically aim to investigate and describe the trajectory of a desired outcome. Longitudinal data have the advantage over cross-sectional data by providing more accuracy for the model. LME models allow researchers to account for random variation among individuals and between individuals.

In this project, adolescent health was chosen as a topic of research due to the many changes that occur during this crucial time period as a precursor to overall well-being in adult life. Understanding the factors that influence how …

Bayesian Inference On Mixed-Effects Models With Skewed Distributions For Hiv Longitudinal Data, Ren Chen

USF Tampa Graduate Theses and Dissertations

Statistical models have greatly improved our understanding of the pathogenesis of HIV-1 infection

and guided for the treatment of AIDS patients and evaluation of antiretroviral (ARV) therapies.

Although various statistical modeling and analysis methods have been applied for estimating the

parameters of HIV dynamics via mixed-effects models, a common assumption of distribution is

normal for random errors and random-effects. This assumption may lack the robustness against

departures from normality so may lead misleading or biased inference. Moreover, some covariates

such as CD4 cell count may be often measured with substantial errors. Bivariate clustered

(correlated) data are also commonly encountered in …

Statistical Estimation Of Physiologically-Based Pharmacokinetic Models: Identifiability, Variation, And Uncertainty With An Illustration Of Chronic Exposure To Dioxin And Dioxin-Like-Compounds., Zachary John Thompson

USF Tampa Graduate Theses and Dissertations

Assessment of human exposure to environmental chemicals is inherently subject to uncertainty and variability. There are data gaps concerning the inventory, source, duration, and intensity of exposure

as well as knowledge gaps regarding pharmacokinetics in general. These gaps result in uncertainties in exposure assessment.

The uncertainties compound further with variabilities due to population variations regarding stage of life, life style, and susceptibility,

etc. Use of physiologically-based pharmacokinetic (PBPK) models promises to reduce the uncertainties and enhance extrapolation between species, between routes, from high to low dose, and from acute to chronic exposure. However, fitting PBPK models is challenging because of …

Alternatives To Mixture Model Analysis Of Correlated Binomial Data, N. Rao Chaganty, Roy Sabo, Yihao Deng

Mathematics & Statistics Faculty Publications

While univariate instances of binomial data are readily handled with generalized linear models, cases of multivariate or repeated measure binomial data are complicated by the possibility of correlated responses. Likelihood-based estimation can be applied by using mixture distribution models, though this approach can present computational challenges. The logistic transformation can be used to bypass these concerns and allow for alternative estimating procedures. One popular alternative is the generalized estimating equation (GEE) method, though systematic errors can lead to infeasible correlation estimates or nonconvergence problems. Our approach is the coupling of quasileast squares (QLSs) method with a rarely used matrix factorization, …

The Cumulative Impact Of Unemployment On Risks Of Acute Myocardial Infarction, Guangya Liu, Matthew E. Dupree, Linda K. George, Eric D. Peterson

Faculty Scholarship

Background: Employment instability is a major source of strain affecting an increasing number of adults in the United States. Little is known about the cumulative effect of multiple job losses and unemployment on the risks for acute myocardial infarction (AMI).

Methods: We investigated the associations between different dimensions of unemployment and the risks for AMI in US adults in a prospective cohort study of adults (N=13 451) aged 51 to 75 years in the Health and Retirement Study with biennial follow-up interviews from 1992 to 2010. Unadjusted rates of age-specific AMI were used to demonstrate observed differences by employment status, …

Association Between Chemical Constituents Of Particulate Matter And Cardiovascular And Respiratory Morbidities In Nys, Rena Jones

Legacy Theses & Dissertations (2009 - 2024)

Improved understanding of health risks from short- and long-term exposure to fine particulate matter (PM_2.5) constituents may explain seasonal and geographic heterogeneity in PM_{2.5-health associations and inform control efforts targeting PM sources. Few studies have examined PM species health effects; most have been limited by their exposure assessments and modeling approaches. The goals of this project were to improve the PM exposure assessment and explore relationships between PM2.5 species and health in acute and chronic contexts.}

Incorporating Network Structure In Integrative Analysis Of Cancer Prognosis Data, Shuangge Ma

Shuangge Ma

In high-throughput cancer genomic studies, markers identified from the analysis of single datasets may have unsatisfactory properties because of low sample sizes. Integrative analysis pools and analyzes raw data from multiple studies, and can effectively increase sample size and lead to improved marker identification results. In this study, we consider the integrative analysis of multiple high-throughput cancer prognosis studies. In the existing integrative analysis studies, the interplay among genes, which can be described using the network structure, has not been effectively accounted for. In network analysis, tightly-connected nodes (genes) are more likely to have related biological functions and similar regression …

Risk Factors Of Follicular Lymphoma, Shuangge Ma

Shuangge Ma

No abstract provided.

Health Insurance Coverage And Impact: A Survey In Three Cities In China, Shuangge Ma

Shuangge Ma

No abstract provided.

Integrative Analysis Of Multiple Cancer Genomic Datasets Under The Heterogeneity Model, Shuangge Ma

Shuangge Ma

No abstract provided.

Health Insurance Coverage, Medical Expenditure And Coping Strategy: Evidence From Taiwan, Shuangge Ma

Shuangge Ma

No abstract provided.

Impact Of Illness And Medical Expenditure On Household Consumptions: A Survey In Western China, Shuangge Ma

Shuangge Ma

No abstract provided.

Identification Of Gene-Environment Interactions In Cancer Prognosis Studies Using Penalization, Shuangge Ma

Shuangge Ma

High-throughput cancer studies have been extensively conducted, searching for genetic risk factors independently associated with prognosis beyond clinical and environmental risk factors. Many studies have shown that the gene-environment interactions may have important implications. Some of the existing methods, such as the commonly adopted single-marker analysis, may be limited in that they cannot accommodate the joint effects of a large number of genetic markers or use ineffective marker identification techniques. In this study, we analyze cancer prognosis studies, and adopt the AFT (accelerated failure time) model to describe survival. A weighted least squares approach, which has the lowest computational cost, …

Adaptive Matching In Randomized Trials And Observational Studies, M. Van Der Laan, Laura Balzer, M. Petersen

Laura B. Balzer

Resampling-Based Information Criteria For Best-Subset Regression, Philip T. Reiss, Lei Huang, Joseph E. Cavanaugh, Amy Krain Roy

Philip T. Reiss

When a linear model is chosen by searching for the best subset among a set of candidate predictors, a fixed penalty such as that imposed by the Akaike information criterion may penalize model complexity inadequately, leading to biased model selection. We study resampling-based information criteria that aim to overcome this problem through improved estimation of the effective model dimension. The first proposed approach builds upon previous work on bootstrap-based model selection. We then propose a more novel approach based on cross-validation. Simulations and analyses of a functional neuroimaging data set illustrate the strong performance of our resampling-based methods, which are …

Flexible Distributed Lag Models Using Random Functions With Application To Estimating Mortality Displacement From Heat-Related Deaths, Roger D. Peng

Johns Hopkins University, Dept. of Biostatistics Working Papers

No abstract provided.