Spss 26.0 for windows

We applied Sankey Diagrams by RAWGraghs 2.0 (https://www.rawgraphs.io/) to visualize the source of prescriptions. We analyzed the consumption and expenditure of probiotics as a whole and separately according to different formulations and strains. The average cost per visit was the yearly probiotic expenditure divided by the number of probiotic prescriptions. The statistical significance of trends for the usage and cost of total probiotics was analyzed using the Mann-Kendall test. Other probiotic trends were estimated using the Cochran-Armitage trend test R V.3.3.0 (http://www.R-project.org). We demonstrate the mixture of different probiotic strains by Gephi software (version 0.9.4). Backward stepwise multivariable logistic regression with SPSS 26.0 for Windows (SPSS, Inc, Chicago, Ill) investigated risk factors for antitumor agents co-prescribed with probiotics. Variables with p < 0.2 in single-variable comparisons were included in a multivariable logistic regression model. A two-tailed p < 0.05 was considered to be statistically significant. Relative risk was estimated using odds ratios (ORs) with corresponding 95% confidence intervals (CIs). The Spearman’s rank correlation coefficient with SPSS 26.0 for Windows (SPSS, Inc, Chicago, Ill) calculated correlations between probiotic strain and antitumor drug usage. A p-value less than 0.05 is statistically significant.

Statistical analysis was performed using SPSS 26.0 for Windows (SPSS Inc. Chicago, USA). Data are presented as number (percentage), mean (± standard deviation), or median (25^th-75^th percentile) for TGs. Differences between groups were analyzed using the independent t-test for continuous variables and chi-square test for categorical variables. Multiple logistic regression analysis was used to identify factors associated with MetS. Covariates in the multivariable model included age, systolic and diastolic blood pressures, total cholesterol, LDL-C, eGFR and medications use. An interaction p in Logistic analysis: Model disease (y)= x1 +x2 + x1*x2 +covariates x1*x2 was interaction term. In this study, y = MetS; x1 = sex; x2 = each obesity-related index; covariates = age, systolic and diastolic blood pressures, total cholesterol, LDL-C, eGFR and medications use. Receiver operating characteristic (ROC) curves and areas under the ROC curves (AUCs) were used to assess the performance and predictive ability, respectively, of the obesity-related indices in identifying MetS. A difference was considered significant if the p value was less than 0.05.