Spss statistic version 22

Results are expressed as means ± standard deviation (SD). Comparisons between groups were performed by Student’s t-test for two samples. Correlations between body composition parameters and scores on the CGA domains or between blood biomarkers and scores on the CGA domains were analyzed by calculation of Pearson correlation coefficients. Multiple linear regression analysis using CGA as the dependent variable was performed to identify the determinants of scores on the CGA domains among potential factors. Independent variables with a variance inflation factor of less than 2 were adopted to avoid the multicollinearity problem [33 ,34 ]. We also confirmed normality of residuals for each dependent variable through histograms [33 ,34 ]. P values of less than 0.05 were considered as being indicative of statistical significance. Data were analyzed using SPSS version 22 (IBM SPSS Statistic Version 22).

All data were analyzed statistically via a t test, one-way ANOVA or Pearson Correlation Analysis by using IBM SPSS Statistic (version 22) or Graph Prism7, with P < 0.05 as significant level. Results were expressed as the mean ± SD or frequency from three or more repetitions assays.

All statistical analysis was performed using IBM® SPSS® Statistic version 22. The Kolmogorov-Smirnov test was used to evaluate the probability distribution of the samples with the reference data. Categorical data were described as median and percentages. A Receiver Operating Characteristic (ROC) curve analysis was performed to identify the nodal volume's optimal cut-off value with the highest possible sensitivity and specificity (Fig. 2). For survival analysis, the Kaplan-Meier method and the log-rank test were used. The Cox proportional hazard model and the univariate and multivariate analysis were performed to evaluate the prognostic factors. Overall survival, disease-free survival, and distant metastasis-free survival were considered to determine the prognostic implications. The p-value of <0.05 was considered statistically significant.Figure 2

A Receiver Operating Characteristic (ROC) curve analysis of the optimal cut-off value to predict the mortality.

Figure 2

The sample size was calculated (53 individual in each group), based on Matel D study data [17 (link)]. All data was recorded in the questionnaires. To compare quantitative variables, independent t-test was applied. Chi-square test was applied for qualitative variables. A simple regression analysis was applied to determine the relationship between QT interval indices with each of the quantitative parameters of CAN. Variables normality were tested by Shapiro–Wilk test. Age and BMI had a normal distribution between the two groups and were compared with parametric tests (independent t-test). Duration of DM, fasting blood sugar (FBS), blood sugar 2 h post prandial (BS2hrPP), glycosylated hemoglobin A1C (HbA1C), triglyceride (TG), total cholesterol (TC), high density lipoprotein-cholesterol (HDL-C), low density lipoprotein-cholesterol (LDL-C), 24-h urine protein, QTc, QT min, QT max, QT mean, QT dispersion don't have normal distribution between the two groups and non-parametric tests (Mann–Whitney test) was used. In order to choose best cut-off, we employed Receiver Operating Characteristic (ROC) curve for both QTC and QTd. For statistical analysis, IBM SPSS Statistic version 22 was used and P value less than 0.05 are considered significant.

The experimental results were given as mean ± standard deviation (SD) of the three independent experiments. The data were analyzed via one-way ANOVA, followed by Tukey’s test (IBM, SPSS statistic version 22, USA). A significant difference is accepted when the p-value is less than 0.05.