Biostatistics Commons^™

Statistical Methods For Detecting Causal Rare Variants And Analyzing Multiple Phenotypes, Xinlan Yang

Dissertations, Master's Theses and Master's Reports

This dissertation includes two papers with each distributed in one chapter. To date, genome-wide association studies (GWAS) have identified a large number of common variants that are associated with complex diseases successfully. However, the common variants identified by GWAS only account for a small proportion of trait heritability. Many studies showed that rare variants could explain parts of the missing heritability. Since the well-developed common variant detecting methods are underpowered for rare variant association tests unless sample sizes or effect sizes are very large, investigation the roles of rare variants in complex diseases presents substantial challenges. In chapter 1, we …

Geographic Variations In Antenatal Care Services In Sierra Leone, Eunice Nyambura Chege

Walden Dissertations and Doctoral Studies

Despite antenatal care presenting opportunities to identify and monitor women at risk, use of recommended antenatal care services remains. Barriers preventing use of antenatal services vary between countries, and limited knowledge exists about the link between geographical settings and antenatal service use. The objective of this cross-sectional quantitative study was to explore geographical variations and investigate how social demographic characteristics affect use of antenatal care for women in Sierra Leone using the Andersen behavioral model. The data used were from the 2016 maternal death surveillance report of the whole counrty (N =706). Logistic regression analysis was used to determine the …

Statistical Methods For Analyzing Multivariate Phenotypes And Detecting Rare Variant Associations, Huanhuan Zhu

Dissertations, Master's Theses and Master's Reports

This dissertation includes four papers with each distributed in one chapter.

In chapter 1, I compared the performance of eight multivariate phenotype association tests. The motivation to conduct this power comparison paper is as follows. For nearly 15 years, genome-wide association studies (GWAS) have been widely used to identify genetic variants associated with human diseases and traits. GWAS typically investigate genetic variants for a predefined phenotype, thus fail to identify weak but important effects. In recent years, many multivariate association tests have been developed. However, there is a lack of comprehensive summary of such kinds of approaches. To fill this …

Adjusting For Mis-Reporting In Count Data, Gelareh Rahimighazikalayeh

Theses and Dissertations

Any counting system is prone to recording errors including underreporting and overreporting. Ignoring the misreporting pattern in count data can give rise to bias in the estimation of model parameters. Accordingly, Poisson, negative binomial and generalized Poisson regression have been expanded in some instances to capture reporting biases. However, to our knowledge, no program has been developed to allow users to apply all of these models when needed. In the first part of the dissertation, we review the available models for underreported counts and develop a Stata command to estimate Poisson, negative binomial and generalized Poisson regression models for underreported …

Using Prescription Drug Monitoring Data To Inform Population Level Analysis Of Opioid Analgesic Utilization, Huong T. T. Luu

Theses and Dissertations--Epidemiology and Biostatistics

Increased opioid analgesic (OA) prescribing has been associated with increased risk of prescription opioid diversion, misuse, and abuse. States established prescription drug monitoring programs (PDMPs) to collect and analyze electronic records for dispensed controlled substances to reduce prescription drug abuse and diversion. PDMP data can be used by prescribers for tracking patient’s history of controlled substance prescribing to inform clinical decisions.

The studies in this dissertation are focused on the less utilized potential of the PDMP data to enhance public health surveillance to monitor OA prescribing and co-prescribing and association with opioid overdose mortality and morbidity. Longitudinal analysis of OA …

The Importance Of Friends And Family To Recreational Gambling, At-Risk Gambling, And Problem Gambling, Alissa Mazar, Robert J. Williams, Edward J. Stanek Iii, Martha Zorn, Rachel A. Volberg

Biostatistics and Epidemiology Faculty Publications Series

Background

The variables correlated with problem gambling are routinely assessed and fairly well established. However, problem gamblers were all ‘at-risk’ and ‘recreational’ gamblers at some point. Thus, it is instructive from a prevention perspective to also understand the variables which discriminate between recreational gambling and at-risk gambling and whether they are similar or different to the ones correlated with problem gambling. This is the purpose of the present study.

Method

Between September 2013 to May 2014, a representative sample of 9,523 Massachusetts adults was administered a comprehensive survey of their past year gambling behavior and problem gambling symptomatology. Based on …

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

Legacy Theses & Dissertations (2009 - 2024)

Stress Strength model P(X

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.

Estimating The Respiratory Lung Motion Model Using Tensor Decomposition On Displacement Vector Field, Kingston Kang

Theses and Dissertations

Modern big data often emerge as tensors. Standard statistical methods are inadequate to deal with datasets of large volume, high dimensionality, and complex structure. Therefore, it is important to develop algorithms such as low-rank tensor decomposition for data compression, dimensionality reduction, and approximation.

With the advancement in technology, high-dimensional images are becoming ubiquitous in the medical field. In lung radiation therapy, the respiratory motion of the lung introduces variabilities during treatment as the tumor inside the lung is moving, which brings challenges to the precise delivery of radiation to the tumor. Several approaches to quantifying this uncertainty propose using a …

Penalized Mixed-Effects Ordinal Response Models For High-Dimensional Genomic Data In Twins And Families, Amanda E. Gentry

Theses and Dissertations

The Brisbane Longitudinal Twin Study (BLTS) was being conducted in Australia and was funded by the US National Institute on Drug Abuse (NIDA). Adolescent twins were sampled as a part of this study and surveyed about their substance use as part of the Pathways to Cannabis Use, Abuse and Dependence project. The methods developed in this dissertation were designed for the purpose of analyzing a subset of the Pathways data that includes demographics, cannabis use metrics, personality measures, and imputed genotypes (SNPs) for 493 complete twin pairs (986 subjects.) The primary goal was to determine what combination of SNPs and …

Joint Analysis Of Multiple Phenotypes In Association Studies, Xiaoyu Liang

Dissertations, Master's Theses and Master's Reports

Genome-wide association studies (GWAS) have become a very effective research tool to identify genetic variants of underlying various complex diseases. In spite of the success of GWAS in identifying thousands of reproducible associations between genetic variants and complex disease, in general, the association between genetic variants and a single phenotype is usually weak. It is increasingly recognized that joint analysis of multiple phenotypes can be potentially more powerful than the univariate analysis, and can shed new light on underlying biological mechanisms of complex diseases. Therefore, developing statistical methods to test for genetic association with multiple phenotypes has become increasingly important. …

Improved Standard Error Estimation For Maintaining The Validities Of Inference In Small-Sample Cluster Randomized Trials And Longitudinal Studies, Whitney Ford Tanner

Theses and Dissertations--Epidemiology and Biostatistics

Data arising from Cluster Randomized Trials (CRTs) and longitudinal studies are correlated and generalized estimating equations (GEE) are a popular analysis method for correlated data. Previous research has shown that analyses using GEE could result in liberal inference due to the use of the empirical sandwich covariance matrix estimator, which can yield negatively biased standard error estimates when the number of clusters or subjects is not large. Many techniques have been presented to correct this negative bias; However, use of these corrections can still result in biased standard error estimates and thus test sizes that are not consistently at their …

Estimation Procedures For Complex Survival Models And Their Applications In Epidemiology Studies, Jie Zhou

Theses and Dissertations

In this dissertation, we aim to address three important questions in practice, which can be solved through complex survival models. The first project focuses on studying the longitudinal fitness effect on cardiovascular disease (CVD) mortality. In the second project, we study the disease-death relation between CVD and all-cause mortality and evaluate important covariate effects on the disease or death transitions. In the third project, we compare antiretroviral treatment (ART) for HIV patients and consider both treatment effect and side effect of the drugs. The first two projects are motivated by the Aerobics Center Longitudinal Study (ACLS) datasets and the third …

Comparison Of The Performance Of Simple Linear Regression And Quantile Regression With Non-Normal Data: A Simulation Study, Marjorie Howard

Theses and Dissertations

Linear regression is a widely used method for analysis that is well understood across a wide variety of disciplines. In order to use linear regression, a number of assumptions must be met. These assumptions, specifically normality and homoscedasticity of the error distribution can at best be met only approximately with real data. Quantile regression requires fewer assumptions, which offers a potential advantage over linear regression. In this simulation study, we compare the performance of linear (least squares) regression to quantile regression when these assumptions are violated, in order to investigate under what conditions quantile regression becomes the more advantageous method …

Examining The Confirmatory Tetrad Analysis (Cta) As A Solution Of The Inadequacy Of Traditional Structural Equation Modeling (Sem) Fit Indices, Hangcheng Liu

Theses and Dissertations

Structural Equation Modeling (SEM) is a framework of statistical methods that allows us to represent complex relationships between variables. SEM is widely used in economics, genetics and the behavioral sciences (e.g. psychology, psychobiology, sociology and medicine). Model complexity is defined as a model’s ability to fit different data patterns and it plays an important role in model selection when applying SEM. As in linear regression, the number of free model parameters is typically used in traditional SEM model fit indices as a measure of the model complexity. However, only using number of free model parameters to indicate SEM model complexity …

Characterization Of Plasmodium Falciparum And Plasmodium Vivax Recent Exposure In An Area Of Significantly Decreased Transmission Intensity In Central Vietnam, Johanna Helena Kattenberg, Annette Erhart, Minh Hieu Truong, Eduard Rovira-Vallbona, Khac Anh Dung Vu, Thi Hong Ngoc Nguyen, Van Hong Nguyen, Van Van Nguyen, Melanie Bannister-Tyrrell, Michael Theisen, Adam Bennet, Andrew A. Lover, Thanh Duong Tran, Xuan Xa Nguyen, Anna Rosanas-Urgell

Andrew Lover

Malaria Elimination: Time To Target All Species, Andrew A. Lover, J. Kevin Baird, Roly Gosling, Ric N. Price

Andrew Lover