Spss statistics software program

Data are expressed as the mean ± standard deviation (SD) or as the median (25th, 75th percentile) for significantly skewed variables. Correlations between the clinical parameters and the TKB levels were assessed using Spearman’s rank correlation coefficient. All statistical analyses were performed using the SPSS Statistics software program (version 27.0, SPSS Inc., Chicago, IL, USA). P values of < 0.05 were considered to indicate statistical significance.
A path model based on a covariance structure analysis was proposed to investigate the relationships among clinical factors and specifically to identify probable causal effects of the TKB levels. The causality model defined some hierarchical regression models between clinical factors and the blood TKB levels. A path analysis was performed using the IBM SPSS AMOS software program (version 27; Amos Development Corporation, Meadville, PA, USA). The structural equation models that were obtained were tested and confirmed at a significance level of P < 0.05.
Furthermore, we applied Bayesian structural equation modeling using a program embedded in IBM SPSS AMOS, version 27 (Amos Development Corporation). The frequency polygon was described with the marginal posterior distributions of the estimands. The selected 2-D contour line was used in this study because it was easily visualized^{22 (link),34 (link),44 (link)}.

Kruskal Wallis tests were used to compare the control group to the optic nerve diseases groups for patient background factors, PhNRs, and OCT. We used the Steel–Dwass method for multiple comparisons, and the significance levels were adjusted using the Bonferroni method. The receiver operating characteristic (ROC) curves were created for the W_ratio, cpRNFLT, and GCCT in the patients with optic nerve disorders to investigate the ability of these parameters to differentiate eyes with optic nerve disorders from normal control eyes. An ROC curve is a plot of the true-positive rate versus the false-positive rate for all possible cutoff points. The area under the ROC curve (AUC) was used to determine the diagnostic accuracy of each parameter.
All of the statistical analyses were performed using the IBM SPSS Statistics software program (version 23.0, SPSS Japan, Inc, Tokyo, Japan). Differences in diagnostic ability (AUC) was tested for statistical significance using MedCalc statistical software version 18.6 (MedCalc Software Inc, Mariakerke, Belgium). The data are presented as the mean ± SD, and P values of < 0.05 were taken to be statistically significant.

Data were expressed as the mean ± standard deviation (SD) or the median (25th, 75th percentile) for significantly skewed variables. The blood sample data and hemodynamic data were compared among three time points using a repeated measure analysis of variance followed by Bonferroni’s test. However, when the variable was significantly skewed, the Friedman test was performed. The Wilcoxon signed rank test was used when the same patients were evaluated at two different points. The differences in continuous variables between two groups were evaluated by the unpaired Student t-test or Man-Whitney rank-sum test. The correlation between the ANP and BNP levels was investigated by a simple regression analysis and expressed as Spearman’s correlation coefficient. When we assessed the correlation between the change in BNP and the change in hemodynamic variables (such as diastolic PAP and SVI), BNP was logarithmically transformed for inclusion in linear models because of its exponential change. All statistical analyses were performed using the STATA statistic software program (version 14.0, STATA Corp., College Station, TX, USA) or the SPSS statistics software program (version 24.0, SPSS Inc., Chicago, IL, USA). P values of < 0.05 were considered to indicate statistical significance.