Missing data continues to be one of the main problems in data analysis as it reduces sample representativeness and consequently, causes biased estimates. Multiple imputation methods have been established as an effective method of handling missing data. In this study, we examined multiple imputation methods for quantitative variables on twelve data sets with varied sizes and variability that were pseudo generated from an original data. The multiple imputation methods examined are the predictive mean matching, Bayesian linear regression and linear regression, non-Bayesian in the MICE (Multiple Imputation Chain Equation) package in the statistical software, R. The parameter estimates generated from the linear regression on the imputed data were compared to the closest parameter estimates from the complete data across all twelve data sets.

The Conover Solution is a nonparametric method used to analyze relative growth in students' achievement on state tests administered on two or more occasions. However, there has been very little research assessing the robustness of this method in the presence of missing data. Using vertically scaled and non-vertically scaled data from the math portion of a statewide assessment for grades 4-7, I compare results from listwise deletion and multiple imputation across the residual gain score model, the simple gain score model, and the HLM-NPAR model. In these approaches, I study differences by gender and race in two-level models and then extend the modeling to a three-level model that incorporates school-level random effects. The results are similar across missing data and the modeling approaches for both gender and race. These results hold across multiple cohorts. In addition, there are school-level effects. The results do not vary across missing data or modeling approaches. I discuss implications for these findings and guidelines for practitioners.

<p>Background and Objectives: There are some methodological and clinical challenges in conducting HIV related research. A subset of such challenges include: non-availability of a universally accepted method to quantify subclinical atherosclerosis in HIV patients; ultrasound imaging techniques aimed at quantifying atheroma burden and endothelial dysfunction have been proposed, however there is no universally accepted ultrasound protocol; conflicting inferences on the nature ofthe relationship between anti-retroviral therapy (ART) and cardiovascular disease (CVD) due to small sample sizes; and missing data from longitudinal studies and ultrasound data. The objective of this thesis is to investigate selected aspects of the afore-mentioned issues, and to provide recommendations for future research.</p> <p>Methods:</p> <p>Project 1: We compared the construct validity of carotid artery intima media thickness (IMT) and brachial artery flow mediated vasodilation (FMD); two non-invasive ultrasound techniques used in measuring the extent of sub-clinical atherosclerosis. Baseline and one-year follow-up data were obtained for a sample of 257 subjects aged 35 years or older, recruited into an ongoing study of cardiovascular risk in HIV. An ultrasound technique having statistically significantly strong association with known CVD risk factors was adjudged to have good construct validity. The relationship between baseline IMT or FMD and known CVD risk factors was studied using multiple regression analysis. We modelled the relationship between progression of IMT or FMD and risk factors using fixed-effects models.</p> <p>Project 2: To more precisely investigate the relationship between ARTs and IMT (as a surrogate for CVD), we pooled cross-sectional baseline, record-level data for 1,032 patients recruited across three cohort studies in Canada, France and USA in a metaanalysis. We investigated the association between exposure to ARTs and CVD using hierarchical linear models.</p> <p>Project 3: On missing data, we studied the impact ofan inclusive strategy for conducting multiple imputation (MI) on the efficiency ofregression parameter estimates using Monte-Carlo simulation. In an inclusive strategy, all final analysis variables are included in a multivariate normal model to impute plausible values for missing data. This issue is not well studied for longitudinal HIV data.</p> <p>Results and Conclusions:</p> <p>Project 1: Baseline IMT was significantly associated with age (p < 0.001), male gender (p = 0.034), current smoking status (p < 0.001), systolic blood pressure (p < 0.001) and total:HDL cholesterol ratio (p = 0.004). IMT progression was significantly associated with age (p < 0.001), male gender (p = 0.0051) and current smoking status (p = 0.011). Neither extent nor progression ofFMD was significantly associated with any of the examined vascular risk factors. IMT was adjudged to have better construct validity than FMD.</p> <p>Project 2: Similar to some (but not all) previous studies, AR Ts do not appear to lead to CVD independent of traditional risk factors. However, exploratory analysis of two-way interactions suggests statistically significant moderating effects between ARTs and traditional risk factors. These results warrant further investigation into potential moderating effects between ARTs and known CVD risk factors.</p> <p>Project 3: In conducting MI, simulation results show that a strategy that includes all final analysis model variables in the imputation model provides the least combined variability and bias for final regression estimates. This is important to note because final regression estimates are used in making clinically relevant inferences in practice.</p> / Thesis / Doctor of Philosophy (PhD)

Background and Objectives: Missing participant data are likely to bias the results of randomized control trials (RCTs) when the reason for missingness is associated with status on the outcome of interest. Unlike dichotomous MPD in RCTs, which have been thoroughly investigated, knowledge regarding continuous MPD in RCTs is much more limited. Our objectives were 1) using an adapted checklist, to assess the reporting quality of simulation studies comparing methods to deal with continuous MPD; 2) identify optimal methods proposed by biostatisticians and tested in simulations studies for continuous MPD in RCTs; 3) evaluate how authors report MPD, and how they plan and conduct analyses to deal with MPD in RCTs. Methods: We conducted two systematic surveys. The first identified methods papers published till 2015 January that compared statistical approaches to deal with continuous MPD in RCTs using at least one simulation. In this sample, we considered both the quality of reporting and the results. The second survey identified a representative sample of individual RCTs published in 2014 in core journals reporting the results of at least one continuous variable addressing a patient-important outcome. Results and conclusion: Our survey identified important limitations in reporting quality of simulation studies that compared statistical approaches to deal with continuous MPD, particularly in the reporting of simulation procedures. Only one of 60 studies reported the random number generator used and none reported starting seeds or failures during simulation. Less then half reported software used to perform simulation (41.7%) or analysis (48.3%), and only 4 (5%) reported justification of number of simulations. When facing continuous MPD in RCTs, results of simulation studies demonstrate that trialists seeking optimal approaches may choose robust regression or mixed models and avoid using last observation caring forward. Continuous MPD frequently occurs in RCTs and the extent is typically substantial (median greater than 10%). Methods sections in trial reports typically do not provide adequate detail on how they dealt with MPD in their primary analysis. Among methods actually implemented to deal with MPD, most authors use only available data, thus excluding MPD from the analysis. Seldom do investigators apply statistical approaches to impute or taking into account of MPD nor conduct sensitivity analysis to address the impact of it. A comprehensive knowledge synthesis summarizing current available statistical approaches and its relative merits, as well as the current used methods in RCTs provide clear implications on how the practise of using methods to handle continuous MPD should shift in individual RCTs. Trialists should use mixed models and robust regressions and avoid using last observation caring forward method. / Thesis / Doctor of Philosophy (PhD)

A globular cluster is a collection of stars that orbits the center of its galaxy as a single satellite. Understanding what influences the formations of these clusters provides understanding of galaxy structure and insight into their early development. We continue the work of Harris et al. (2013), who identified a set of predictors that accurately determined the number of clusters Ngc, through analysis of an incomplete dataset. We aimed to improve upon these results through imputation of the missing data. A small amount of precision was gained for the slope of Ngc~ R_e*sigma_ e, while the intercept suffered a small loss of precision. Estimates of intrinsic variance also increased with the addition of imputed data. We also found galaxy morphological type to be a significant predictor of Ngc in a model with R_e*sigma_ e. Although it increased precision of the slope and reduced the residual variance, its overall contribution was negligible. / Thesis / Master of Science (MSc)

PhD Thesis / Older adults are a rapidly growing segment of the population with unique healthcare needs. As people age, they are more likely to become susceptible to diseases and develop complex health conditions that require tailored strategies to address. These vulnerabilities could also impact different stages of the research process to generate evidence that promote healthy aging and better quality of life for this population. Attrition and missing data are some of the common methodological challenges in research with older adults. These issues could affect the quality of evidence generated if not properly addressed. There is also limited evidence to guide the development of interventions in specific populations of older adults with frailty, who have reduced function and are at higher risk for poor health outcomes. Across six chapters, this thesis addresses these methodological and interventional gaps in research with older adults. Using different research methodologies including a systematic literature survey, secondary data analysis of a cohort study, and two randomized feasibility trials, this thesis provides some important considerations for practice. In particular, we (i) evaluated the magnitude, pattern, and factors associated with attrition in the Global Longitudinal Study of Osteoporosis in Women (GLOW) Hamilton cohort of older adults; (ii) performed a systematic survey of the reporting and handling of missing data in longitudinal observational studies of older adults; (iii) conducted a randomized controlled feasibility trial of the Geras virtual frailty rehabilitation program to build resilience in vulnerable older adults during the COVID-19 pandemic; and (iv) evaluated the feasibility of the FitJoints randomized controlled trial of a multimodal intervention in frail older patients with osteoarthritis awaiting hip and knee replacement. / Thesis / Doctor of Philosophy (PhD) / The number of people who are old is increasing by the day and so is the need to understand how to ensure they are aging well. Old age makes people more prone to diseases. The risks of becoming ill could make the efforts to generate knowledge that can help them thrive challenging. They could drop out of a study making it difficult to collect enough information for data analysis. For some older adults who are frail and have higher risk for diseases, there is little known about how to design programs that will enable them stay active and healthier during the COVID-19 pandemic or before they have hip or knee replacement surgery. This thesis contributes to the knowledge on how to improve the quality of research involving older adults and bridge the gap in the knowledge about how to support those who are frail among them.

Most previous research on online social networks (OSNs) has focused on the adoption and continuation of OSN as it is a newer form of social media the usage of which has increased over time. However, very little research has explored the discontinuation of users from OSN usage. Using disengagement theory, this study examines the roles of fear of missing out and addiction along with other factors such as victimization, well-being, privacy concerns, alternative attractiveness, and social influence in the disengagement process from OSN usage. The proposed conceptual model is evaluated using survey design. A preliminary investigation consisting of expert panel review, pretest, and pilot test is conducted to ensure measurement validity. A primary investigation consisting of reliability and validity testing, model fit test (i.e. goodness of fit), common method bias test, and t-test is conducted to ensure validity of structural model. The data are analyzed to recommend the findings. The study found that intention to disengage from OSN leads to actual disengagement, thus, bridging the gap between intention and actual behavior. Attractive alternatives to existing OSN, privacy concerns, and negative psychosocial wellbeing were found to positively influence intention to disengage from a specific OSN. Perceived enjoyment and social influence were found to negatively affect intention disengage from OSN. The findings also indicated that the influence of alternative attractiveness on intention to disengage from an OSN will be moderated by the fear of missing out, such that the influence will be weaker. Similarly, the influence of negative psychosocial well-being on intention to disengage from an OSN will be moderated by the fear of missing out, such that the influence will be weaker. These findings contribute to the information systems and OSN research literature by introducing several theories to expand the concepts of fear of missing out and addiction in studying disengagement process from OSN usage. Besides, there are several implications of this research on practice such as understanding the impact of dark sides of OSNs in a user’s disengagement process from OSN usage.

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