Spss 22.0 software for windows

SPSS 22.0 software for Windows was used for statistical analysis. Descriptive statistics included frequency, percentage, mean, and standard deviation (SD). The effect of the intervention was determined with a paired sample t test by analyzing the difference in pre-intervention and post-intervention scores for familiarity with ACP and its components, intent to engage in ACP, and participation in informal discussions about the seven EoL-care treatments. Consistency between PWCIs and FCGs for decisions regarding EoL-care pre-intervention and post-intervention was determined with the Kappa parameter test [29 (link)]. The McNemar test was used to determine significant differences in scores for each topic of informal discussion and to compare consistency of EoL-care between decisions pre-intervention and post-intervention. A level of p < .05 was considered significant.

Differences in titers between the control strain and other strains were evaluated using SPSS 22.0 software for Windows (SPSS, Chicago, IL, United States). One-way ANOVA analyses were carried out with a confidence interval of 95% and statistical significance between the groups and the relevant control was considered if p-value < 0.05.

Statistical analysis was performed using SPSS 22.0 software for Windows. The values of normally distributed variables were compared by the Kolmogorov–Smirnov test. Normally distributed continuous variables were expressed as means ± standard deviation. Variables not normally distributed were reported as medians and four quantiles. Categorical data were expressed as percentages and compared by the chi square test. Analysis of variance was used for multiple comparison of normally distributed data. The Kruskal-Wallis H test was used for multiple comparisons of nonnormally distributed data. The receiver operation characteristic (ROC) curve was obtained to evaluate the performance of BMIb and ΔBMI in identifying MetS prevalence in early adulthood. The optimal cutoff point was identified as the coordinate closest to the y intercept (0,1) of the ROC curve and, at this point, the sum of the sensitivity and the specificity was maximum. Diagnostic accuracy was assessed by the area under the curve (AUC). Partial correlation was used to analyze the relationships of BMI, MetS, and other cardiovascular risk factors after 5 years. Multiple logistic regression was used to analyze the association of BMIb and ΔBMI with MetS and MetS components at follow-up. Differences with p < 0.05 were considered statistically significant.

Statistical analysis was performed using SPSS 22.0 software for Windows (SPSS Inc., Chicago, IL, USA). Because of the asymmetric distribution between the two groups, differences of non-categorical data were evaluated with the Wilcoxon rank-sum test. The Fisher’s exact test was used for categorical data. Descriptive statistics are given either as median (range) and mean ± standard deviation for non-categorical data or as percentages of total for categorical data. CERAD total score, TMT and plasma levels of Aβ_1–40 and Aβ_1–42 have been evaluated for each time point adjusting for age, gender and educational levels (only cognition). For the CERAD subdomains, demographically adjusted z scores were used. Differences were considered significant at p < 0.05.