Analysis of Time-to-event Data, Intermediate Phenotypes, and Sparse Factors in the OPPERA Study

Motivated by the Orofacial Pain: Prospective Evaluation and Risk Assessment (OPPERA) project, a large study of temporomandibular disorders (TMD), this dissertation develops statistical methods applicable to three facets of chronic pain. First, we propose a method for parameter estimation in survival models with missing censoring indicators. These result because conducting multiple invasive examinations for incidence on all participants in large prospective studies is infeasible. We estimate the probability of being an incident case for those lacking a gold standard examination using logistic regression. Multiple imputations of case status for each missing examination are generated using these estimated probabilities. Imputed and observed data are combined in Cox models to estimate the incidence rate and associations with putative risk factors. The variance is estimated using multiple imputation. Our method performs as well as or better than competing methods and highlighted new discoveries for OPPERA. Secondly, we propose a general method to analyze secondary phenotypes and apply it to the OPPERA baseline case-control study. Traditional case-control genetic association studies examine relationships between case-control status and one or more covariates. Investigators now commonly study additional phenotypes and their association with the original covariates as secondary aims. Assessing these associations is statistically challenging, as participants do not form a random sample from the population of interest. Standard methods may be biased and lack coverage and power. Utilizing inverse probability weighting and bootstrapping for standard error estimation, our method performs as well as competitors when they are applicable and provides promising results for outcomes to which other methods do not apply. Third, we propose a method for sparse factor analysis. Psychometric studies frequently measure numerous variables that may be noisy manifestations of a few underlying constructs. Aims include identifying these latent variables and their relationship to the observed variables and reducing the data to a few key variables that explain the majority of variance. While variable reduction methods exist for principal component analysis, none have been proposed to date for factor analysis. Our method retains predictive accuracy for many thresholds in simulations while providing sparse loadings. Competing methods had less predictive accuracy or less sparsity.Doctor of Philosoph

Thermal Comfort Performance of Active Cooling T-Shirt in Agricultural Protective Clothing

The purpose of this research aimed to investigate if a t-shirt with printed cooling technology can significantly improve the physiological and subjective thermal comfort of agricultural workers. Chest skin temperature, chest microclimate humidity, and the heart rate of twenty agricultural workers were measured. Daily exit surveys consisting of perceived exertion, comfort, and temperature sensations were gathered along with an additional exit survey consisting of acceptance information from the agricultural workers. Results from this study will determine if novel textile finishing technologies, such as the ones used in this study, can improve comfort for agricultural and industrial work applications

Tests of trend between disease outcomes and ordinal covariates discretized from underlying continuous variables: simulation studies and applications to NHANES 2007–2008

Abstract Background Many epidemiological studies test trends when investigating the association between a risk factor and a disease outcome. Continuous exposures are commonly discretized when the outcome is nonlinearly related to exposure as well as to facilitate interpretation and reduce measurement error. Guidance is needed regarding statistically valid trend tests for epidemiological data of this nature. Methods The association between a discretized variable and a disease is modeled through logistic regression or survival analysis. Linear regression is then conducted by regressing the odds ratio or relative risk on the midpoint of the exposure interval. The trend test is based on the slope of the regression line. In order to investigate the performance of this approach, we conducted simulation studies, considering ten different approaches for the linear regression based on the inclusion or exclusion of an intercept in the model and the form of the weights. The proposed methods are applied to the National Health and Nutrition Examination Survey (NHANES) 2007–2008 for illustration. Results The simulation studies show that eight of these methods are valid, and the relative efficiency depends on the underlying relationship between the covariate and the outcome. Conclusions The significance of the study is its potential to help practitioners select an appropriate method to test for trend in their future studies that utilize ordinal covariates

Modeling Secondary Phenotypes Conditional on Genotypes in Case–Control Studies

Traditional case–control genetic association studies examine relationships between case–control status and one or more covariates. It is becoming increasingly common to study secondary phenotypes and their association with the original covariates. The Orofacial Pain: Prospective Evaluation and Risk Assessment (OPPERA) project, a study of temporomandibular disorders (TMD), motivates this work. Numerous measures of interest are collected at enrollment, such as the number of comorbid pain conditions from which a participant suffers. Examining the potential genetic basis of these measures is of secondary interest. Assessing these associations is statistically challenging, as participants do not form a random sample from the population of interest. Standard methods may be biased and lack coverage and power. We propose a general method for the analysis of arbitrary phenotypes utilizing inverse probability weighting and bootstrapping for standard error estimation. The method may be applied to the complicated association tests used in next-generation sequencing studies, such as analyses of haplotypes with ambiguous phase. Simulation studies show that our method performs as well as competing methods when they are applicable and yield promising results for outcome types, such as time-to-event, to which other methods may not apply. The method is applied to the OPPERA baseline case–control genetic study

Non-Response in Wave IV of the National Longitudinal Study of Adolescent Health

Non-response is a potential threat to the accuracy of estimates obtained from sample surveys and can be particularly difficult to avoid in longitudinal studies. The objective of this report is to investigate non-response and consequent bias in estimates for Wave IV of the National Longitudinal Study of Adolescent Health (Add Health). The Survey Research Unit at the University of North Carolina at Chapel Hill previously analyzed the non-response rates for the first three waves of Add Health. As shown in Chantala, Kalsbeek and Andraca, 2005, the total bias in Waves I, II, and III for 13 measures of health and risk behaviors rarely exceed 1%, which is small relative to the 20% to 80% prevalence rates for most of these measures. Results are similar for Wave IV. In this paper, first, we outline the Wave IV sampling design and results of the field work. Second, we characterize the non-response rates overall and stratified by a number of demographic variables. Next, we use data on the health risk measures reported by Wave IV responders and non-responders during their Wave I In-home interview to estimate total and relative bias due to non-response in Wave IV. We conclude with a discussion of Wave IV bias due to non-response

An evaluation of metrics for assessing maternal exposure to agricultural pesticides

We evaluate the use of three different exposure metrics to estimate maternal agricultural pesticide exposure during pregnancy. Using a geographic information system-based method of pesticide exposure estimation, we combine data on crop density and specific pesticide application amounts/dates to create the three exposure metrics. For illustration purposes, we create each metric for a North Carolina cohort of pregnant women, 2003–2005, and analyze the risk of congenital anomaly development with a focus on metric comparisons. Based on the results, and the need to balance data collection efforts/computational efficiency with accuracy, the metric which estimates total chemical exposure using application dates based on crop-specific earliest planting and latest harvesting information is preferred. Benefits and drawbacks of each metric are discussed and recommendations for extending the analysis to other states are provided

Association Between Gynecological Characteristics and Temporomandibular Disorders: Insights from the OPPERA Study

• Several chronic pain conditions, such as temporomandibular disorders (TMD), are more common in women than in men although the biological mechanisms responsible for this gender disparity are poorly understood • Observational studies suggest that TMD pain is greatest during the late luteal phase of the menstrual cycle and during menses when estrogen levels quickly decline • Also, women with TMD who use hormonal contraception report greater levels of daily pain compared to women not taking hormonal contraception • The aim of this study is to evaluate gynecological characteristics that are putative risk factors for TMD: parity, use of hormonal contraception, and self-reported pain levels and psychological symptoms over the course of the menstrual cycl