The recent call to reconstruct a detailed picture of the tree of life for all organisms has forever changed the field of molecular phylogenetics. Sequencing technology has improved to the point that scientics can now routinely sequence complete plastid/mitochondrial genomes and thus, vast amounts of data can be used to reconstruct phylogenies. These data are accumulating in DNA sequence repositories, such as GenBank, where everyone can benefit from the vast growth of information. The trend of generating genomic-region rich datasets has far outpaced the expasion of datasets by sampling a broader array of taxa. We show here that expanding a dataset both by increasing genomic regions and species sampled using GenBank data, despite the inherent missing DNA that comes with GenBank data, can provide a robust phylogeny for the plant order Caryophyllales (angiosperms). We also investigate the utility of trnK intron in phylogeny reconstruction at relativley deep evolutionary history (the caryophyllid order) by comparing it with rapidly evolving matK. We show that trnK intron is comparable to matK in terms of the proportion of variable sites, parsimony informative sites, the distribution of those sites among rate classes, and phylogenetic informativness across the history of the order. This is especailly useful since trnK intron is often sequenced concurrently with matK which saves on time and resources by increasing the phylogenetic utility of a single genomic region (rapidly evolving matK/trnK). Finally, we show that the inclusion of RNA edited sites in datasets for phylogeny reconstruction did not appear to impact resolution or support in the Gnetales indicating that edited sites in such low proportions do not need to be a consideration when building datasets. We also propose an alternate start codon for matK in Ephedra based on the presense of a 38 base pair indel in several species that otherwise result in pre-mature stop codons, and present 20 RNA edited sites in two Zamiaceae and three Pinaceae species. / Ph. D.

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.

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.

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BACKGROUND:The extant squamates (>9400 known species of lizards and snakes) are one of the most diverse and conspicuous radiations of terrestrial vertebrates, but no studies have attempted to reconstruct a phylogeny for the group with large-scale taxon sampling. Such an estimate is invaluable for comparative evolutionary studies, and to address their classification. Here, we present the first large-scale phylogenetic estimate for Squamata.RESULTS:The estimated phylogeny contains 4161 species, representing all currently recognized families and subfamilies. The analysis is based on up to 12896 base pairs of sequence data per species (average = 2497 bp) from 12 genes, including seven nuclear loci (BDNF, c-mos, NT3, PDC, R35, RAG-1, and RAG-2), and five mitochondrial genes (12S, 16S, cytochrome b, ND2, and ND4). The tree provides important confirmation for recent estimates of higher-level squamate phylogeny based on molecular data (but with more limited taxon sampling), estimates that are very different from previous morphology-based hypotheses. The tree also includes many relationships that differ from previous molecular estimates and many that differ from traditional taxonomy.CONCLUSIONS:We present a new large-scale phylogeny of squamate reptiles that should be a valuable resource for future comparative studies. We also present a revised classification of squamates at the family and subfamily level to bring the taxonomy more in line with the new phylogenetic hypothesis. This classification includes new, resurrected, and modified subfamilies within gymnophthalmid and scincid lizards, and boid, colubrid, and lamprophiid snakes.

In cluster randomized trials (CRTs), groups of participants are randomized as opposed to individual participants. This design is often chosen to minimize treatment arm contamination or to enhance compliance among participants. In CRTs, we cannot assume independence among individuals within the same cluster because of their similarity, which leads to decreased statistical power compared to individually randomized trials. The intracluster correlation coefficient (ICC) is crucial in the design and analysis of CRTs, and measures the proportion of total variance due to clustering. Missing data is a common problem in CRTs and should be accommodated with appropriate statistical techniques because they can compromise the advantages created by randomization and are a potential source of bias. In three papers, I investigate statistical approaches for handling missing data in CRTs. In the first paper, I carry out a systematic review evaluating current practice of handling missing data in CRTs. The results show high rates of missing data in the majority of CRTs, yet handling of missing data remains suboptimal. Fourteen (16%) of the 86 reviewed trials reported carrying out a sensitivity analysis for missing data. Despite suggestions to weaken the missing data assumption from the primary analysis, only five of the trials weakened the assumption. None of the trials reported using missing not at random (MNAR) models. Due to the low proportion of CRTs reporting an appropriate sensitivity analysis for missing data, the second paper aims to facilitate performing a sensitivity analysis for missing data in CRTs by extending the pattern mixture approach for missing clustered data under the MNAR assumption. I implement multilevel multiple imputation (MI) in order to account for the hierarchical structure found in CRTs, and multiply imputed values by a sensitivity parameter, k, to examine parameters of interest under different missing data assumptions. The simulation results show that estimates of parameters of interest in CRTs can vary widely under different missing data assumptions. A high proportion of missing data can occur among CRTs because missing data can be found at the individual level as well as the cluster level. In the third paper, I use a simulation study to compare missing data strategies to handle missing cluster level covariates, including the linear mixed effects model, single imputation, single level MI ignoring clustering, MI incorporating clusters as fixed effects, and MI at the cluster level using aggregated data. The results show that when the ICC is small (ICC ≤ 0.1) and the proportion of missing data is low (≤ 25\%), the mixed model generates unbiased estimates of regression coefficients and ICC. When the ICC is higher (ICC > 0.1), MI at the cluster level using aggregated data performs well for missing cluster level covariates, though caution should be taken if the percentage of missing data is high.