Indiana University-Purdue University Indianapolis (IUPUI) / Joint models for longitudinal and time-to-event data has been introduced to study the association between repeatedly measured exposures and the risk of an event. The use of joint models allows a survival outcome to depend on some characteristic functions from the longitudinal measures. Current estimation methods include a two-stage approach, Bayesian and maximum likelihood estimation (MLEs) methods. The twostage method is computationally straightforward but often yields biased estimates. Bayesian and MLE methods rely on the joint likelihood of longitudinal and survival outcomes and can be computationally intensive. In this work, we propose a joint generalized estimating equation framework using an inverse intensity weighting approach for parameter estimation from joint models. The proposed method can be used to longitudinal outcomes from the exponential family of distributions and is computationally e cient. The performance of the proposed method is evaluated in simulation studies. The proposed method is used in an aging cohort to determine the relationship between longitudinal biomarkers and the risk of coronary artery disease.

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Kernel mixture models are proposed to cluster univariate, independent multivariate and dependent bivariate longitudinal data. The Gaussian distribution in finite mixture models is replaced by the Gaussian and gamma kernel functions, and the expectation-maximization algorithm is used to estimate bandwidths and compute log-likelihood scores. For dependent bivariate longitudinal data, the bivariate Gaussian copula is used to reveal the correlation between two attributes. After that, we use AIC, BIC and ICL to select the best model. In addition, we also introduce a kernel distance-based clustering method to compare with the kernel mixture models. A simulation is performed to illustrate the performance of this mixture model, and results show that the gamma kernel mixture model performs better than the kernel distance-based clustering method based on misclassification rates. Finally, these two models are applied to COVID-19 data, and sixty countries are classified into ten clusters based on growth rates and death rates. / Thesis / Master of Science (MSc)

Preterm birth is the leading cause of neonatal mortality and long-term morbidity. Neonatologists can adjust nutrition to preterm neonates to control their weight gain so that the possibility of long-term morbidity can be minimized. This optimization of growth trajectories of preterm infants can be achieved by studying a cohort of selected healthy preterm infants with weights observed during day 1 to day 21. However, missing values in such a data poses a big challenge in this case. In fact, missing data is a common problem faced by most applied researchers. Most statistical softwares deal with missing data by simply deleting subjects with missing items. Analyses carried out on such incomplete data result in biased estimates of the parameters of interest and consequently lead to misleading or invalid inference. Even though many statistical methods may provide robust analysis, it will be better to handle missing data by imputing them with plausible values and then carry out a suitable analysis on the full data. In this thesis, several imputation methods are first introduced and discussed. Once the data get completed by the use of any of these methods, the growth trajectories for this cohort of preterm infants can be presented in the form of percentile growth curves. These growth trajectories can now serve as references for the population of preterm babies. To find out the explicit growth rate, we are interested in establishing predictive models for weights at days 7, 14 and 21. I have used both univariate and multivariate linear models on the completed data. The resulting predictive models can then be used to calculate the target weight at days 7, 14 and 21 for any other infant given the information at birth. Then, neonatologists can adjust the amount of nutrition given in order to preterm infants to control their growth so that they will not grow too fast or too slow, thus avoiding later-life complications. / Thesis / Master of Science (MSc)

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Homelessness is an important social problem in Canada, and the needs and experiences of women may differ from those of other homeless people. Little research has looked beyond rehousing to examine community integration following homelessness. Predictive models of three distinct facets of community integration for women who have experienced homelessness are presented and tested in this thesis. The first model examines physical integration, which is defined in terms of attaining and retaining stable housing. The second model predicts economic integration, defined in terms of participation in work or education. The third model predicts psychological integration, defined as psychological sense of community in one’s neighbourhood. Data for this research comes from a two-year longitudinal study conducted in Ottawa. Participants were women aged 20 and over (N =101) who were homeless at the study’s outset. Family status was an important predictor of community integration. Women who were accompanied by dependent children were more likely than those unaccompanied by children to be physically, economically and psychologically integrated in their communities. Having access to subsidized housing predicted becoming rehoused and living in one’s current housing for longer. Greater perceived social support predicted living in one’s current housing for longer. Past work history and mental health functioning predicted economic integration. Lower levels of education predicted returns to full-time studies. Living in higher quality housing and having more positive contact with neighbours predicted psychological integration, while living in one’s current housing for longer predicted lower levels of psychological integration. Overall, participants achieved a moderate level of community integration. The majority had been housed for at least 90 days at follow-up. However, only a minority were participating in the workforce or education at follow-up. Participants achieved only a moderate level of psychological integration. Results are discussed in terms of implications for policy and service provision. Improvements in the availability and quality of affordable housing, as well as employment support are recommended. Special attention needs to be paid to providing adequate and effective services for women who are unaccompanied by dependent children. / Fonds québécois de la recherche sur la societé et la culture

The term dementia refers to a progressive decline in cognitive functioning resulting in a significant impairment in daily living. Given the devastating impacts of the disease and lack of a cure, it is reasonable to expect people fear developing a dementia. Alzheimer's disease ranks high among the most feared diseases in national samples of the American population. As a topic of study, little is known about the determinants of fear of Alzheimer's disease and how this fear may change as a function of aging, time, or experience. The current study sought to fill this gap by investigating the nature of changes in fear of Alzheimer's disease by following participants (N = 227) over the course of two years. Volunteers completed measures on fear of dementia, knowledge about Alzheimer's disease, knowledge about the aging process, personality traits, memory self-efficacy, anxiety about aging, and Alzheimer's-related experiences (i.e., family history, caregiving experience, number of people known with the disease, personal diagnosis, etc.). Results supported the notion that fear for becoming a burden to others, a component of fear of dementia, decreased over the two years. In addition, personality traits and memory self-efficacy mediated the two-year change in concerns about perceived symptoms of cognitive decline. In predicting fear for various aspects of Alzheimer's disease, anxiety about aging and experience/exposure to the disease emerged as prominent predictors. These results highlight dementia concerns and offer guidance for early interventions, such as an open communication with family and health care providers about fear of dementia.

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<p> In this report we explore how the effect of meteorological factors, allergens, and air pollution on respiratory conditions in children using longitudinal data. Our analysis makes use of a dataset from the DAVIS study in southern Ontario. The response variables are children's lower respiratory tract (URT) and upper respiratory tract (URT) scores. The explanatory variables are readings of various meteorological, allergen, and air pollution factors. First we make use of generalized estimating equations to find the main factors that are associated with certain respiratory conditions in children as measured by LRT and URT scores. Then we determine whether there are any interactions between the significant factors associated with LRT /URT scores. Comparisons between case and control groups are made to determine whether children with asthma are more sensitive to any of the changes in meteorological, allergen, and air pollution factors. The analysis results show that the significant factor that is associated with LRT scores for children with asthma is the two-day lag daily average changes in air pressure. On average an increase in air pressure will result in an increase in children's LRT scores. The interaction terms that remained in the final model show some degree of significance but without strong evidence to support it. Children in the case groups are more sensitive to meteorological factors, allergens, and air pollution than the children in control groups. </p> / Thesis / Master of Science (MSc)