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<p> Many medical studies are performed to investigate the effectiveness of new treatments (such as new drugs, new surgery) versus traditional (or placebo) treatments. In many cases, researchers measure a continuous variable at baseline and again as an outcome assessed at follow up. The baseline measurement usually has strong relationship with post treatment measurement. Consequently, the ANCOVA model using baseline as covariate may provide more powerful and precise results than the ANOVA model.</p> <p> However, most epidemiologic studies will encounter the problem of missing covariate data. As a result, the patients with missing baseline measurements will be excluded from the data analysis. Hence, there exists a tradeoff between the ANOVA with full data set and the ANCOVA with partial data set.</p> <p> This study focuses on the variance of the estimator of treatment means difference. In practical situation, the standard error of the estimator obtained from the ANCOVA model with partially missing baseline relative to the standard error obtained form the ANOVA with full data relies on the correlation between baseline and follow-up outcome, the proportion of the missing baseline, and the difference of the group means on the baseline. In moderate sample size studies, it is also affected by the sample size.</p> <p> The theoretically required minimum correlations for the ANCOVA model were calculated to obtain the same precision with the ANOVA model assuming the missing proportion, sample size and difference of group means on covariate are available. The minimum correlation can be obtained through checking the reference table or figures.</p> <p> The figures of asymptotic relative efficiencies provide the asymptotic variance and the length of the confidence intervals of the estimated difference obtained from the ANCOVA model relative to the ANOVA model for all the range of the correlation between baseline and follow up.</p> / Thesis / Master of Science (MSc)

Ecological data is often fraught with many problems such as Missing Data and Spatial Correlation. In this dissertation we use a data set collected by the Ohio EPA as motivation for studying techniques to address these problems. The data set is concerned with the benthic health of Ohio's waterways. A new method for incorporating covariate structure and missing data mechanisms into missing data analysis is considered. This method allows us to detect relationships other popular methods do not allow. We then further extend this method into model selection. In the special case where the unobserved covariates are assumed normally distributed we use the Bayesian Model Averaging method to average the models, select the highest probability model and do variable assessment. Accuracy in calculating the posterior model probabilities using the Laplace approximation and an approximation based on the Bayesian Information Criterion (BIC) are explored. It is shown that the Laplace approximation is superior to the BIC based approximation using simulation. Finally, Hierarchical Spatial Linear Models are considered for the data and we show how to combine analysis which have spatial correlation within and between clusters. / Ph. D.

京都大学 / 新制・課程博士 / 博士(社会健康医学) / 甲第25207号 / 社医博第134号 / 京都大学大学院医学研究科社会健康医学系専攻 / (主査)教授 森田 智視, 教授 永井 洋士, 教授 中山 健夫 / 学位規則第4条第1項該当 / Doctor of Public Health / Kyoto University / DFAM

OBJECTIVE: To explore the effect of pet ownership (PO) on the oral health of older adults. METHODS: NHANES data 2005-06 was analyzed (logistic/linear regression), restricted to aged 65 years and older. The outcomes of interest were the presence/absence of untreated dental caries, the presence/absence of dental restorations, and mean number of teeth. The primary independent variable was PO status within the past year. Potential confounders included demographic data (age, gender, race, marital status, poverty income ratio, education), smoking status, depression, physical activities, and mean sugar intake, taking into account NHANES complex sampling. RESULTS: Individuals aged 65-69 were more likely to own pets than those older than 70 years, with dogs being the most popular pet (62%) followed by cats (31%). After adjusting for potential confounders, there was no association between pet ownership in seniors and the outcomes of interest. Non-Hispanic blacks reported the highest tooth loss (-6.42 teeth) among the racial groups and were 40% less likely to have a dental restoration. In the unadjusted model Mexican Americans have 2.83 times the odds of untreated dental caries compared to Non-Hispanic whites, while current smokers had 7 fewer teeth than those who never smoked. CONCLUSIONS: Pet ownership was not associated with improved oral health for older adults. Despite the lack of oral health protection, pet ownership provides companionship, reduces loneliness, and increases socialization among older adults.

Longitudinal studies of cognition are preferred to cross-sectional stud- ies, since they offer a direct assessment of age-related cognitive change (within-person change). Statistical methods for analyzing age-related change are widely available. There are, however, a number of challenges accompanying such analyzes, including cohort differences, ceiling- and floor effects, and attrition. These difficulties challenge the analyst and puts stringent requirements on the statistical method being used. The objective of Paper I is to develop a classifying method to study discrepancies in age-related cognitive change. The method needs to take into account the complex issues accompanying studies of cognitive aging, and specifically work out issues related to attrition. In a second step, we aim to identify predictors explaining stability or decline in cognitive performance in relation to demographic, life-style, health-related, and genetic factors. In the second paper, which is a continuation of Paper I, we investigate brain characteristics, structural and functional, that differ between suc- cessful aging elderly and elderly with an average cognitive performance over 15-20 years. In Paper III we develop a Bayesian model to estimate the causal effect of living arrangement (living alone versus living with someone) on cog- nitive decline. The model must balance confounding variables between the two living arrangement groups as well as account for non-ignorable attrition. This is achieved by combining propensity score matching with a pattern mixture model for longitudinal data. In paper IV, the objective is to adapt and implement available impu- tation methods to longitudinal fMRI data, where some subjects are lost to follow-up. We apply these missing data methods to a real dataset, and evaluate these methods in a simulation study.