Spss software for windows version 27

Data were evaluated by SPSS software for Windows version 27.0 (SPSS Inc., Chicago, IL, USA). Univariate statistics included t test for independent groups to compare between the two studies. One-way ANOVA was used to compare between the three groups of anti-Xa (LOW, NORMAL, HIGH) for continuous variables. Pearson’s Chi-square test was used to compare categorical variables. Values were reported as mean ± SEM. Pearson’s correlation test was used to assess correlations. Significance level was set at p<0.05. Multiple regression was used as multivariate analysis. The dependent variable was the level of anti-Xa and the independent variables were the significant variables from the univariate analysis.

Statistical analysis was performed using the SPSS software for Windows, version 27.0 (Statistical Package for Social Sciences Inc., Chicago, IL, USA). Data are expressed as the mean ± standard error of the mean (SEM). After assessing the normality of data by Kolmogorov–Smirnov test, a one-way ANOVA with post-hoc Tukey’s test or unpaired Student’s t-test was used for statistical analyses, as appropriate. Values of p < 0.05 were considered statistically significant. Sample size was calculated with a priori power analysis (G*Power Version 3.1.9.2 for Windows; www.gpower.hhu.de, accessed on 12 May 2014), considering dermal thickening as endpoint. A sample size of six mice per group was determined sufficient to have a power > 0.80 at a significance level of p < 0.05.

Continuous data with normal distribution are presented as mean ± standard deviation (SD), and non-normally distributed data are presented as median and interquartile range (IQR). All dichotomous data are presented as numbers and percentages [n (%)]. For group comparisons, Student’s t-test or one-way analysis of variance (ANOVA) was used for normally distributed continuous variables, Mann-Whitney U-test or Kruskal-Wallis H-test was used for non-normally distributed continuous variables, and the Chi-square test or Fisher’s exact test was used for dichotomous variables. To test the correlations between two continuous variables, we used Pearson’s correlation for normally distributed data and Spearman’s rank correlation for non-normally distributed data. To investigate the risk factors, independent variables were included in a binary logistic regression model using the enter method. The odd ratio (OR) with 95% confidence intervals (CI) obtained in the regression analysis were calculated. The optimal cut-off points were determined using receiver operating characteristic (ROC) analysis. Comparing the area under the curve (AUC) of ROC was performed using the Delong test. Data were analysed using SPSS software for Windows (version 27) and the R programming language, and two-tailed p-values < 0.05 were considered statistically significant.