Data are presented as the mean ± standard deviation unless otherwise specified in the text. Differences were analyzed using the SPSS ver. 14.0 (SPSS Inc., Chicago, IL, USA) and Medcalc ver. 14.8.1 (Medcalc, Ostend, Belgium). Correlations between continuous variables were analyzed using the Pearson correlation test and linear regression. The ratio of males and females according to age group was compared using the chi-square test. The paired t-test was used to compare pairs of dependent groups, and Student's t-test for two independent groups. Two-sided p-values <0.05 were considered statistically significant.
Ver 14
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MedCalc ver. 14.8.1 is a software application designed for statistical analysis and data management. It provides a range of tools for performing various types of analyses, including descriptive statistics, hypothesis testing, and regression modeling. The software is compatible with multiple operating systems and data file formats.
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Lab products found in correlation
4 protocols using ver 14
1
Comparative Statistical Analysis in Research
2
Evaluating CSLO Imaging for RNFL Defects
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To determine the discrimination power of blue and green CSLO imaging for detecting RNFL defects, areas under the receiver operating characteristic curve (AUCs) were calculated. Significant differences between AUCs were assessed using the method described by DeLong et al. [9 (link)]. Reproducibility of CSLO imaging interpretation was assessed by calculating the kappa value. Kappa values were calculated between each of two graders. Statistical significance between groups was tested using the Kruskal-Wallis test for continuous variables and chi-square test for categorical variables. Post-hoc analysis was performed using a Mann-Whitney U-test for continuous variables and chisquare test for categorical variables. A p-value of <0.05 was considered statistically significant in all analyses. We used PASW Statistics ver. 18.0 (SPSS Inc., Chicago, IL, USA) and Medcalc ver. 14.8.1 (Medcalc, Mariakerke, Belgium) to perform statistical analyses.
3
Liver Fibrosis Staging Evaluation
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Differences in mean and regional LSN scores between fibrosis groups (F0‐F4) as well as serum biochemistry were analyzed using one‐way analysis of variance (ANOVA) with Tukey's post hoc test using the statistical package for the social sciences program (SPSS ver. 20; Chicago, IL, USA). The coefficient of variance (CV) in each ROI group was calculated to assess the variability of measurements.14 Pearson correlation between the five regional LSN measurements and the mean LSN scores was performed. Agreement between the LSN measurements of the two methods was evaluated by calculating Pearson correlation coefficients (r) and generating Bland–Altman plots. Pearson correlation and Bland–Altman analysis were performed using MedCalc ver.14.8.1 (Medcalc, Mariakerke, Belgium).15 Intraclass correlation coefficients (ICC) values were evaluated to assess interobserver agreement in LSN scores and were classified as16 : poor (<0.40), moderate (0.40 to <0.60), good (0.60 to <0.80), and excellent (0.80–1.00).
To evaluate the diagnostic performance of mean and regional LSN scores according to fibrosis stages, receiver operating characteristics (ROC) curve analysis was performed to calculate the area under the ROC curve (AUROC), sensitivity, specificity, and diagnostic accuracy (DA). Two‐sided P values less than 0.05 were considered to indicate statistical significance.
To evaluate the diagnostic performance of mean and regional LSN scores according to fibrosis stages, receiver operating characteristics (ROC) curve analysis was performed to calculate the area under the ROC curve (AUROC), sensitivity, specificity, and diagnostic accuracy (DA). Two‐sided P values less than 0.05 were considered to indicate statistical significance.
4
Predictive Factors for Septic Shock
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Continuous variables are expressed by mean±standard deviation or median (interquartile range, IQR). Continuous variables were analyzed using the Student t-test or the Mann-Whitney U-test, and categorical variables were analyzed using chi-square or Fisher exact tests. A multivariable logistic regression was performed to identify predictive factors for septic shock using variables that had previously been reported to be significantly associated with septic shock.
We generated receiver operating characteristic (ROC) curves and determined the area under the ROC curve (AUC) for individual measures (KMEWS, KTAS, MEWS, and MEDS) that was associated with septic shock, ICU admission, and in-hospital mortality. The AUCs of the models were calculated and tested mutually for significance using DeLong equality tests. In addition, the cutoff value was calculated using the Youden index (Youden’s J statistic). All statistical analyses were performed using the IBM SPSS ver. 19.0 (IBM Corp., Armonk, NY, USA) and MedCalc ver. 14.8.1 (MedCalc software, Ostend, Belgium). P-values less than 0.05 were considered statistically significant.
We generated receiver operating characteristic (ROC) curves and determined the area under the ROC curve (AUC) for individual measures (KMEWS, KTAS, MEWS, and MEDS) that was associated with septic shock, ICU admission, and in-hospital mortality. The AUCs of the models were calculated and tested mutually for significance using DeLong equality tests. In addition, the cutoff value was calculated using the Youden index (Youden’s J statistic). All statistical analyses were performed using the IBM SPSS ver. 19.0 (IBM Corp., Armonk, NY, USA) and MedCalc ver. 14.8.1 (MedCalc software, Ostend, Belgium). P-values less than 0.05 were considered statistically significant.
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