Spss software version 23.0 for windows

To balance the confounding factors, we performed propensity score matching (PSM) using a logistic regression model for the following variables: sex and age. The two groups were matched 1:1 with a caliper of 0.001.
Normally distributed data are expressed as the means ± standard deviations (SDs). Categorical variables are expressed as frequencies and percentages. Continuous variables were compared using Student’s t test, and categorical variables were compared using the χ² test. Comparisons between multiple groups were made using ANOVA. The metabolism-related parameters, liver enzyme profiles, and FIB-4 scores of the study subjects were compared between SCr quartiles. We explored the correlates of HS through univariate and multivariate logistic regression analysis; that is, significant factors (p < 0.05) were included in the univariate regression and further validated in the multivariate regression. Covariables included sex, age, systolic blood pressure, diastolic blood pressure, lipid profile, and liver enzyme profile. All tests were two-tailed, and results p < 0.05 were considered statistically significant. Statistical analyses were performed with SPSS software version 23.0 for Windows (SPSS Inc., Chicago, IL, USA).

Normality of the data was tested using the Kolmogorov‐Smirnov test. The categorical variables are described as percentages and frequencies. Normally distributed data are presented as means ± SD unless noted otherwise. For non‐normally distributed data, median values with interquartile ranges (IQR) are shown. Data were analysed using Mann‐Whitney and Kruskal‐Wallis test for nonparametric variables and ANOVA and Student's t test for parametric variables. For categorical variables, chi‐square was used. Statistical analysis was performed using SPSS software version 23.0 for Windows (SPSS, Chicago, IL, USA). P values <0.05 were considered statistically significant.

The filled questionnaires were checked for completeness, and responses were coded and entered into the Statistical Package for the Social Sciences (SPSS) software version 23.0 for Windows (SPSSInc., Chicago, IL). Descriptive statistics of participants' sociodemographic, disease, and CAM use characteristics were expressed in frequencies and proportions. Comparisons between CAM users and nonusers characteristics were conducted using chi-square. The association of each of those characteristics with CAM use was assessed using simple logistic regression, with CAM use as outcome variable. In order to evaluate the correlates of CAM use, a multiple logistic regression model was used. In this model, variables were included if they were significantly associated with the outcome in the univariate analysis. Odds ratios and their respective 95% confidence intervals were computed. Statistical significance was detected by a p value less than 0.05.