Spss windows version 23

The statistical analysis was performed using SPSS Windows version 23.0 (IBM Inc., Chicago, IL, USA). The categorical data were described with frequency distributions and percentages and compared using the two-sided Fisher’s exact test or Pearson’s χ2 test. The normalization of the distributed data for the continuous variables was analyzed using the Kolmogorov–Smirnov test. Analysis of variance was used with Bonferroni post hoc correction to analyze continuous data with a normal distribution. The results were expressed as mean ± standard deviation (SD). The non-normally distributed continuous data were analyzed using the Mann–Whitney U-test. p values < 0.05 indicated statistical significance.

All basic variables (age, sex, and educational level) were compared between groups using SPSS (Windows version 23.0, IBM). Continuous variables are expressed as mean ± standard deviation and categorical values as numbers (percentages). Categorical variables were compared between groups by the chi-square test, continuous variables by the Student’s t-test and ranked data by the rank-sum test. A P < 0.05 was considered significant for all SPSS tests.
Network measures were compared between groups using GAT. Non-parametric permutation tests, each with 1,000 repetitions, were performed to test the statistical significance of between-group differences in global and regional network measures, with P < 0.05 (two-tailed) considered significant. Permutation analysis was also performed to assess between-group differences in network resilience against random failure and targeted attack, with statistical significance set at P < 0.05. To reduce the impact of thresholding, we also compared the areas under the curves (AUCs) generated from density variation between groups. In addition, the false discovery rate (FDR) was applied to correct for multiple comparisons in the regional BC analysis, with P < 0.05 (FDR-corrected) considered statistically significant.

The questionnaires were checked for completeness, coded and entered into EPI Data version 3.1software and then exported to SPSS windows version 23 for analysis. Bivariate analysis was used primarily to check variables which have an association with the dependent variable individually. Hosmer and Lemeshow goodness of fit test was checked, and it was found to be 0.989 on the final model. Variables which were found to have an association with the dependent variable at a P-value of < 0.2 were then entered into multivariable logistic regression for controlling the possible effect of confounders and finally the variables which have significant association were identified based on AOR, with 95%Confidence Interval (CI) and P-value < 0.05.

The data entry was performed in Microsoft Excel and 20% entries were randomly selected for cross-validation by a reviewer. Cleaning, coding, and analysis of the data were performed using SPSS Windows version 23. Cronbach’s alpha was calculated to measure the internal consistency of the scales and subscales of the HM-PRO, which is the average inter-item correlation (Cronbach, 1951 (link); Frost et al., 2007 (link); Tavakol and Dennick, 2011 (link); Devellis, 2016 ). The alpha value reflects the extents to which the instrument measures the concept consistently. Cronbach’s alpha value greater than 0.7 was taken as reliable (Tavakol and Dennick, 2011 (link)). Spearman’s rank correlation was also calculated to assess the inter-item and item-total partial correlation. A moderate correlation (r_s = 0.2) is expected between items (Streiner and Norman, 2003 ). Intra-class correlation was calculated to assess the level of agreement between scores from the first assessment (test) and after 7 days (re-test). An ICC value of 1 means that 100% variability is because of difference between patients, and the ICC value of 0 means that all variability is due to within-patient variability and error (Studenic et al., 2016 (link)). A 2-way mixed-effects, absolute agreement, multiple raters measurement type of ICC was chosen as per McGraw and Wong convention (Mcgraw and Wong, 1996 (link)).