Spss corp

Drug utilisation metrics used in the study included the number of patients stratified by age, gender and district (Tables 1 and 2), and the total and average number of yearly prescriptions per patient, stratified by age and gender (Table 3 and Figure 1).
Data were analysed using Statistical Analysis System^® (SAS Institute Inc. 2002–2012 ). The number of patients in the study population and prescriptions claimed during the course of the study period were explained by means of descriptive statistics that included frequencies, means, standard deviations and 95% confidence intervals. Tests of association (chi-square test and Fishers’ exact test) were used to determine the association between totals of categorical data. The results were considered statistically significant if p ≤ 0.05. Cohen’s d-value was used to determine the effect size of the difference between the average number of prescriptions per patient by age group and gender with d ≥ 0.8 defined as a large effect with practical significance.
Changes in the annual number of methylphenidate and atomoxetine prescriptions per patient per year were modelled over time by fitting a repeated measures Poisson regression model using the generalised estimating equations procedure in SPSS IBM Corp. (2013 ). Pairwise comparisons were adjusted for multiple comparisons using the Bonferroni correction.

The data was analyzed using IBM SPSS Corp.’s software, which was released in 2013. We used IBM SPSS Statistics for Windows. IBM Corporation (Version 22.0). Qualitative data was defined by number and percentage. Quantitative data was represented by median (minimum and maximum). Mean and standard deviation were used for parametric data. The significance of the obtained results was determined at the (0.05) level of significance.

Data were fed to the computer and analyzed using IBM SPSS Corp. Released 2013. IBM SPSS Statistics for Windows, Version 22.0. Armonk, NY: IBM Corp. Qualitative data were described using number and percent. Significance of the obtained results was judged at the (0.05) level. Graph pad prism version 6.01 was used for figure design. Chi-Square test for comparison of 2 or more groups.

Statistical analyses were performed via SPSS Corp (IBM Corp). Number and percent were used to describe qualitative data while, after testing normality using Kolmogrov–Smirnov test, quantitative data required use of median (minimum and maximum) for non-parametric variables and mean ± SD for parametric data. Comparing between two groups was done with either t test for parametric variables or Mann–Whitney test for non-parametric data, with comparison between two or more groups required use of χ² test or Monte Carlo tests.
Receiver operator characteristics (ROC) curve allowed choosing the cutoff point with the highest sensitivity and specificity rates while stepwise logistic regression was used for multivariable regression. In addition, adjusted Odds ratio with 95% confidence interval were calculated and linear regression analysis used for prediction of independent variables of continuous parametric outcome. Kaplan–Meier curve was used to demonstrate time to event. A p value < 0.05 was considered statistically significant.

Data were fed to the computer and analyzed using IBM SPSS Corp. Released 2013. IBM SPSS Statistics for Windows, Version 22.0. Armonk, NY: IBM Corp. Qualitative data were described using number and percent. Quantitative data were described using mean and standard deviation for parametric data after testing normality using Kolmogrov-Smirnov test. Significance of the obtained results was judged at the (0.05) level.

Data were fed to the computer and analyzed using IBM SPSS Corp. Released in 2013. IBM SPSS Statistics for Windows, Version 22.0. Qualitative data were described using numbers and percentages. Quantitative data were described using median (minimum and maximum) for non-parametric data and mean ± standard deviation for parametric data after testing normality using the Kolmogorov–Smirnov test. For qualitative data Chi-Square test was done for comparing 2 groups, the Student t test was used to compare parametric data of independent variables of the 2 groups, and Mann–Whitney U test was used to compare non-parametric data of independent variables of the 2 groups. Spearman’s rank-order correlation is used to determine the strength and direction of a linear relationship between two non-normally distributed continuous variables and /or ordinal variables. Multivariable linear regression analysis was done to determine the predictors of endothelial dysfunction in CKD patients. A P value less than 0.05 was considered statistically significant.