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Table ?Table11 summarizes the available data. Table 1 Number of study participants with available data at each visit by scale Statistical Analysis Descriptive statistical methods (i.e., means, percentages, etc.) were used to characterize the longitudinal CDR stage, CDR-sb, CERAD-NB, and the MMSE scores. An independent-samples t test or nonparametric Mann-Whitney U test was used to compare participants with very mild (CDR = 0.5) and those with mild AD (CDR = 1) at baseline, males and females, and participants who completed the 3-year follow-up and those who dropped out. The Spearman rank correlation coefficient was used to analyze correlations selleck screening library between age, education, CERAD-NB subtests, MMSE scores, CERAD-NB total scores, and CDR-sb scores during the 3-year follow-up. Due to relatively high attrition rates, patterns in possibly missing data were analyzed. Generalized estimating equation (GEE) analyses were conducted step by step, and the GEE models used were specified with gaussian distribution, identity link function, and unstructured correlation matrix. First, to meet aim 1, associations between AD severity (CDR-sb at the annual visits over the 3-year follow-up) and the repeated measures of MMSE, CERAD-NB total score, and each CERAD-NB subtest were analyzed separately with and without the selected covariates of age at baseline, gender, education, and time. Variables were then added selleckchem and removed using repeated measures of these variables to find the best subtest combination to explain the variance of the repeated measures of CDR-sb. We evaluated the goodness of the combination in terms of goodness-of-fit diagnostics and pseudo-R2 statistics [42], which measured how much of the outcome variable's variation was explained by the model at hand. Finally, to meet study aim 2, the same variables that were included in the best subtest combination, but only the baseline values, were used to predict disease progression over time. With the GEE S6 Kinase models, the relationship between the variables of the model at different time points could be analyzed simultaneously to reflect the longitudinal relationship between the outcome variable and the time-independent and/or time-dependent covariates using all available longitudinal data. Statistical significance was defined as p