Matching Items (225)
Description
Many longitudinal studies, especially in clinical trials, suffer from missing data issues. Most estimation procedures assume that the missing values are ignorable or missing at random (MAR). However, this assumption leads to unrealistic simplification and is implausible for many cases. For example, an investigator is examining the effect of treatment on depression. Subjects are scheduled with doctors on a regular basis and asked questions about recent emotional situations. Patients who are experiencing severe depression are more likely to miss an appointment and leave the data missing for that particular visit. Data that are not missing at random may produce bias in results if the missing mechanism is not taken into account. In other words, the missing mechanism is related to the unobserved responses. Data are said to be non-ignorable missing if the probabilities of missingness depend on quantities that might not be included in the model. Classical pattern-mixture models for non-ignorable missing values are widely used for longitudinal data analysis because they do not require explicit specification of the missing mechanism, with the data stratified according to a variety of missing patterns and a model specified for each stratum. However, this usually results in under-identifiability, because of the need to estimate many stratum-specific parameters even though the eventual interest is usually on the marginal parameters. Pattern mixture models have the drawback that a large sample is usually required. In this thesis, two studies are presented. The first study is motivated by an open problem from pattern mixture models. Simulation studies from this part show that information in the missing data indicators can be well summarized by a simple continuous latent structure, indicating that a large number of missing data patterns may be accounted by a simple latent factor. Simulation findings that are obtained in the first study lead to a novel model, a continuous latent factor model (CLFM). The second study develops CLFM which is utilized for modeling the joint distribution of missing values and longitudinal outcomes. The proposed CLFM model is feasible even for small sample size applications. The detailed estimation theory, including estimating techniques from both frequentist and Bayesian perspectives is presented. Model performance and evaluation are studied through designed simulations and three applications. Simulation and application settings change from correctly-specified missing data mechanism to mis-specified mechanism and include different sample sizes from longitudinal studies. Among three applications, an AIDS study includes non-ignorable missing values; the Peabody Picture Vocabulary Test data have no indication on missing data mechanism and it will be applied to a sensitivity analysis; the Growth of Language and Early Literacy Skills in Preschoolers with Developmental Speech and Language Impairment study, however, has full complete data and will be used to conduct a robust analysis. The CLFM model is shown to provide more precise estimators, specifically on intercept and slope related parameters, compared with Roy's latent class model and the classic linear mixed model. This advantage will be more obvious when a small sample size is the case, where Roy's model experiences challenges on estimation convergence. The proposed CLFM model is also robust when missing data are ignorable as demonstrated through a study on Growth of Language and Early Literacy Skills in Preschoolers.
ContributorsZhang, Jun (Author) / Reiser, Mark R. (Thesis advisor) / Barber, Jarrett (Thesis advisor) / Kao, Ming-Hung (Committee member) / Wilson, Jeffrey (Committee member) / St Louis, Robert D. (Committee member) / Arizona State University (Publisher)
Created2013
Description
It is common in the analysis of data to provide a goodness-of-fit test to assess the performance of a model. In the analysis of contingency tables, goodness-of-fit statistics are frequently employed when modeling social science, educational or psychological data where the interest is often directed at investigating the association among multi-categorical variables. Pearson's chi-squared statistic is well-known in goodness-of-fit testing, but it is sometimes considered to produce an omnibus test as it gives little guidance to the source of poor fit once the null hypothesis is rejected. However, its components can provide powerful directional tests. In this dissertation, orthogonal components are used to develop goodness-of-fit tests for models fit to the counts obtained from the cross-classification of multi-category dependent variables. Ordinal categories are assumed. Orthogonal components defined on marginals are obtained when analyzing multi-dimensional contingency tables through the use of the QR decomposition. A subset of these orthogonal components can be used to construct limited-information tests that allow one to identify the source of lack-of-fit and provide an increase in power compared to Pearson's test. These tests can address the adverse effects presented when data are sparse. The tests rely on the set of first- and second-order marginals jointly, the set of second-order marginals only, and the random forest method, a popular algorithm for modeling large complex data sets. The performance of these tests is compared to the likelihood ratio test as well as to tests based on orthogonal polynomial components. The derived goodness-of-fit tests are evaluated with studies for detecting two- and three-way associations that are not accounted for by a categorical variable factor model with a single latent variable. In addition the tests are used to investigate the case when the model misspecification involves parameter constraints for large and sparse contingency tables. The methodology proposed here is applied to data from the 38th round of the State Survey conducted by the Institute for Public Policy and Michigan State University Social Research (2005) . The results illustrate the use of the proposed techniques in the context of a sparse data set.
ContributorsMilovanovic, Jelena (Author) / Young, Dennis (Thesis advisor) / Reiser, Mark R. (Thesis advisor) / Wilson, Jeffrey (Committee member) / Eubank, Randall (Committee member) / Yang, Yan (Committee member) / Arizona State University (Publisher)
Created2011
Description
Designing a hazard intelligence platform enables public agencies to organize diversity and manage complexity in collaborative partnerships. To maintain the integrity of the platform while preserving the prosocial ethos, understanding the dynamics of “non-regulatory supplements” to central governance is crucial. In conceptualization, social responsiveness is shaped by communicative actions, in which coordination is attained through negotiated agreements by way of the evaluation of validity claims. The dynamic processes involve information processing and knowledge sharing. The access and the use of collaborative intelligence can be examined by notions of traceability and intelligence cohort. Empirical evidence indicates that social traceability is statistical significant and positively associated with the improvement of collaborative performance. Moreover, social traceability positively contributes to the efficacy of technical traceability, but not vice versa. Furthermore, technical traceability significantly contributes to both moderate and high performance improvement; while social traceability is only significant for moderate performance improvement. Therefore, the social effect is limited and contingent. The results further suggest strategic considerations. Social significance: social traceability is the fundamental consideration to high cohort performance. Cocktail therapy: high cohort performance involves an integrative strategy with high social traceability and high technical traceability. Servant leadership: public agencies should exercise limited authority and perform a supporting role in the provision of appropriate technical traceability, while actively promoting social traceability in the system.
ContributorsWang, Chao-shih (Author) / Van Fleet, David (Thesis advisor) / Grebitus, Carola (Committee member) / Wilson, Jeffrey (Committee member) / Shultz, Clifford (Committee member) / Arizona State University (Publisher)
Created2015
Description
The Pearson and likelihood ratio statistics are well-known in goodness-of-fit testing and are commonly used for models applied to multinomial count data. When data are from a table formed by the cross-classification of a large number of variables, these goodness-of-fit statistics may have lower power and inaccurate Type I error rate due to sparseness. Pearson's statistic can be decomposed into orthogonal components associated with the marginal distributions of observed variables, and an omnibus fit statistic can be obtained as a sum of these components. When the statistic is a sum of components for lower-order marginals, it has good performance for Type I error rate and statistical power even when applied to a sparse table. In this dissertation, goodness-of-fit statistics using orthogonal components based on second- third- and fourth-order marginals were examined. If lack-of-fit is present in higher-order marginals, then a test that incorporates the higher-order marginals may have a higher power than a test that incorporates only first- and/or second-order marginals. To this end, two new statistics based on the orthogonal components of Pearson's chi-square that incorporate third- and fourth-order marginals were developed, and the Type I error, empirical power, and asymptotic power under different sparseness conditions were investigated. Individual orthogonal components as test statistics to identify lack-of-fit were also studied. The performance of individual orthogonal components to other popular lack-of-fit statistics were also compared. When the number of manifest variables becomes larger than 20, most of the statistics based on marginal distributions have limitations in terms of computer resources and CPU time. Under this problem, when the number manifest variables is larger than or equal to 20, the performance of a bootstrap based method to obtain p-values for Pearson-Fisher statistic, fit to confirmatory dichotomous variable factor analysis model, and the performance of Tollenaar and Mooijaart (2003) statistic were investigated.
ContributorsDassanayake, Mudiyanselage Maduranga Kasun (Author) / Reiser, Mark R. (Thesis advisor) / Kao, Ming-Hung (Committee member) / Wilson, Jeffrey (Committee member) / St. Louis, Robert (Committee member) / Kamarianakis, Ioannis (Committee member) / Arizona State University (Publisher)
Created2018
Description
As the prevalence and awareness of Autism Spectrum Disorder (ASD) increases, so does the variety of treatment options for primary symptoms (social interaction, communication, behavior) and secondary symptoms (anxiety, hyperactivity, GI problems, and insomnia). Various treatments, from Adderall to Citalopram to Flax Seed Oil promise relief for these symptoms. However, very little research has actually been done on some of these treatments. Additionally, the research that has been done fails to compare these treatments against one another in terms of symptom relief. The Autism Treatment Effectiveness Survey, written by Dr. James Adams, director of the Autism/Asperger's Research Program at ASU, and graduate student/program coordinator Devon Coleman, aims to fill this gap. The survey numerically rates medications based on benefit and adverse effects, in addition to naming specific symptoms that are impacted by the treatments. However, the survey itself was retrospective in nature and requires further evidence to support its claims. Therefore, the purpose of this research paper is to evaluate evidence related to the results of the survey. After the performing an extensive literature review of over 70 different treatments, it appears that the findings of the Autism Treatment Effectiveness Survey are generally well supported. There were a few minor discrepancies regarding the primary benefitted symptom, but there was not enough of a conflict to discount the information from the survey. As research is still ongoing, conclusions cannot yet be drawn for Nutritional Supplements, although the current data looks promising.
ContributorsAnderson, Amy Lynn (Author) / Adams, James (Thesis director) / Coleman, Devon (Committee member) / School of Nutrition and Health Promotion (Contributor) / W.P. Carey School of Business (Contributor) / Barrett, The Honors College (Contributor)
Created2018-05
Description
Osteoporosis is a medical condition that leads to decreased bone mineral density, resulting in increased fracture risk.1 Research regarding the relationship between sleep and bone mass is limited and has primarily been studied in elderly adults. While this population is most affected by osteoporosis, adolescents are the most proactive population in terms of prevention. The purpose of this study was to evaluate the relationship between sleep efficiency and serum osteocalcin in college-aged individuals as a means of osteoporosis prevention. Thirty participants ages 18-25 years (22 females, 8 males) at Arizona State University were involved in this cross-sectional study. Data were collected during one week via self-recorded sleep diaries, quantitative ActiWatch, DEXA imaging, and serum blood draws to measure the bone biomarker osteocalcin. Three participants were excluded from the study as outliers. The median (IQR) for osteocalcin measured by ELISA was 11.6 (9.7, 14.5) ng/mL. The average sleep efficiency measured by actigraphy was 88.3% ± 3.0%. Regression models of sleep efficiency and osteocalcin concentration were not statistically significant. While the addition of covariates helped explain more of the variation in serum osteocalcin concentration, the results remained insignificant. There was a trend between osteocalcin and age, suggesting that as age increases, osteocalcin decreases. This was a limited study, and further investigation regarding the relationship between sleep efficiency and osteocalcin is warranted.
ContributorsMarsh, Courtney Nicole (Author) / Whisner, Corrie (Thesis director) / Mahmood, Tara (Committee member) / School of International Letters and Cultures (Contributor) / School of Nutrition and Health Promotion (Contributor) / Barrett, The Honors College (Contributor)
Created2018-05
Description
Adenosine triphosphate (ATP) is the driving force of the human body which allows individuals to move freely. Metabolism is responsible for its creation, and research has indicated that with training, metabolism can be modified to respond more efficiently to aerobic stimulus. During an acute bout of exercise, cardiac output increases to maintain oxygen supply to the body. Oxidative muscle fibers contract to move the body for prolonged periods of time, creating oxidative stress which is managed by the mitochondria which produce the ATP that supplies the muscle fiber, and as the body returns to its resting state, oxygen continues to be consumed in order to return to steady state. Following endurance training, changes in cardiac output, muscle fiber types, mitochondria, substrate utilization, and oxygen consumption following exercise make adaptations to make metabolism more efficient. Resting heart rate decreases and stroke volume increases. Fast twitch muscle fibers shift into more oxidative fibers, sometimes through mitochondrial biogenesis, and more fat is able to be utilized during exercise. The excess postexercise oxygen consumption following exercise bouts is reduced, and return to steady state becomes quicker. In conclusion, endurance training optimizes metabolic response during acute bouts of aerobic exercise.
ContributorsWarner, Erin (Author) / Nolan, Nicole (Thesis director) / Cataldo, Donna (Committee member) / School of Nutrition and Health Promotion (Contributor) / Barrett, The Honors College (Contributor)
Created2018-05
Description
University students currently lack sufficient knowledge and resources needed to support healthy eating patterns and nutrition. Comparison of the number of registered dietitians that are available to all students, along with the number of wellness events that are held at each university within the Pacific-12 conference will help determine which schools are best able to support their students' needs. Data was collected using a Google forms survey sent via email to wellness directors of each of the universities in the Pac-12 conference. Eight out of the twelve schools in the conference responded to the survey. The average number of dietitians available to all students (regardless of athlete status) was found to be 1.43 dietitians. Of the schools that responded, the University of Colorado, Boulder, has the most resources dedicated to student nutrition wellness with three dietitians available for all undergraduate students, free dietitian services, and approximately 150 wellness events each year. The success of available nutrition wellness resources was inconclusive as schools did not provide the information regarding student utilization and attendance. Future university promoted nutrition wellness programs should increase the number of affordable dietitians and total wellness events, as well as promote student health services through social media platforms to improve student nutrition knowledge and usage of resources.
ContributorsCurtin, Anne Clare (Author) / Dixon, Kathleen (Thesis director) / McCoy, Maureen (Committee member) / School of Nutrition and Health Promotion (Contributor) / Barrett, The Honors College (Contributor)
Created2018-05
Description
Forty collegiate gymnasts were recruited for a nutrition and health study. Participants must have been at least eighteen years old at Arizona State University (ASU) in the club or team gymnastics program. The Institutional Review Board (IRB) reviewed and accepted my survey in order to hand out to the gymnasts. The ASU club and team coach and the ASU study team also approved my survey. As soon as the survey was approved, it was emailed to all of the gymnasts. ASU gymnasts were surveyed on nutritional knowledge and personal health. Subjects answered a quiz on nutrient needs and serving sizes. Personal questions consisted of height, weight, injuries, body image, and typical meal plans. Gymnasts were given a $10 compensation to increase the participation. We found that only 16% of gymnasts surveyed scored a 70% or higher on their nutritional knowledge. Although these gymnasts do not have adequate knowledge, the majority consume a healthy diet. Diets included fruits, vegetables, protein-rich foods, and few high fat and sugary foods. Four of the gymnasts had one or fewer injuries in the past two years, although, four gymnasts also had three or more injuries. No correlation was found between diet and injuries. There was also no correlation between the gymnast's nutritional knowledge and their health.
ContributorsKugler, Natalie K. (Author) / Levinson, Simin (Thesis director) / Berger, Christopher (Committee member) / School of Nutrition and Health Promotion (Contributor) / Barrett, The Honors College (Contributor)
Created2017-12
Description
Building sustainable American Muslim institutions is critical for the development of an embedded, productive and contributing American Muslim community. The Muslim Students Association is a springboard for emerging young American Muslim leaders to learn how to develop American Muslim organizations, network and provide services for the community. This guidebook is designed to sustain the growth of this organization at ASU.
ContributorsSyed, Sarah (Author) / Mousa, Neimeh (Thesis director) / Hania, Hind (Committee member) / School of Nutrition and Health Promotion (Contributor) / Barrett, The Honors College (Contributor)
Created2016-05