Spss version 22.0 statistical package

The differences of gender and age between 2 groups were analyzed by 2-sided Chi-squared test and independent samples t test, respectively. We performed an exact test to examine Hardy–Weinberg equilibrium (HWE) in case and control groups. Minor alleles of SNPs were seemed as risk alleles for AS susceptibility. The differences in frequency distributions of alleles were compared between cases and controls by Pearson Chi-squared test. Odds ratios (ORs), 95% confidence intervals (CIs), and P-value were used for logistic regression analysis and we performed the Wald test by unconditional logistic regression analysis so that the adjustment for age and sex were done for the dominant, recessive, codominant, and log-additive models. We used the Haploview software package (version 4.2) and the SHEsi software platform to analyze the linkage disequilibrium and SNP haplotypes.^{[30 (link),31 (link)]} A logistic regression analysis was performed to assess haplotype association with response. SPSS version 22.0 statistical package (SPSS, Chicago, IL) and Microsoft Excel were used for all statistical analyses. P < .05 was considered statistically significant.

Descriptive statistical analysis presented continuous variables in terms of mean and standard deviation (SD). Sample normality was evaluated using the Kolmogorov-Smirnov test. The parametric variables were analyzed using a t-test in the case of two groups and analysis of variance (ANOVA) for more than two. Meanwhile, non-parametric variables were analyzed using the Mann-Whitney U test for two groups and the Kruskal-Wallis test in the case of more than two groups. A confidence interval of 95% (p < 0.05) was considered to conclude significant differences between variables. Finally, the statistical analysis was performed using the SPSS version 22.0 statistical package (SPSS, Chicago, IL, United States).

Continuous variables were expressed as the mean (standard deviation). The Kolmogorov–Smirnov test was used to analyze the distribution of continuous variables. As for normally distributed variables, the analysis of variance t-test was applied. In the case of non-normally distributed variables, the groups were compared using the Mann–Whitney U-test (two groups) or the Kruskal–Wallis test (more than two groups). A p-value < 0.05 was considered significant. All analyses were performed using the SPSS version 22.0 statistical package (SPSS, Chicago, IL, USA).

A pilot study revealed a PI mean of 2.79 ± 1.93 for supine position. Using this value, and a 25% increase in this value for each position (accepting type I error of 0.05 and a power of 0.80), a total of 61 patients were required to find a statistically significant difference between each position.
The descriptive characteristics were expressed as numbers and percentages in the categorical variables and as means, standard deviations, and medians. The Kolmogorov-Smirnov test was employed to test the normality of the data’s distribution. Measurements were compared with repeated measures ANOVA test. The chi square test was used to compare categorical data. Data analysis was performed with the IBM SPSS version 22.0 statistical package (Chicago, IL, USA). A p value < 0.05 was considered statistically significant.

All neuropsychological and CE data were analyzed using the SPSS version 22.0 Statistical package (SPSS, IBM Inc., Chicago, IL, USA) with two-tailed tests and a 5% level of significance. Shapiro–Wilk tests were used to verify continuous variables for normal distribution, and Wilcoxon tests were used to compare the right and left hemispheres in the DAI group.
For inferential analysis, all subjects from DAI group were matched by age and gender to healthy subjects from a normative database of CE (17 (link)), and a Mann–Whitney U-test was performed. The Spearman test was performed to analyze correlation between neuropsychological and CE data results.