Spss software 27

The statistical analysis was plotted using the GraphPad Prism 8.3 software (San Diego, CA, USA). All the data are shown as mean ± standard error (SE). Analysis of variance (one-way ANOVA) was chosen, followed by a least significant difference (LSD) post hoc test in SPSS software 27.0 (SPSS Inc., Chicago, IL, USA) when appropriate. A statistical significance was defined when p < 0.05.

Statistical analysis was performed using SPSS software 27.0 (Statistical Package for the Social Sciences, Chicago, US) for Windows. The Kolmogorov-Smirnov’s test was used to verify normal distribution of data. Results are expressed as mean ± SE, unless otherwise stated. One-way analysis of variance (ANOVA) followed by the Dunnett’s post hoc test was applied for multiple comparison, whereas Student’s t-test was used for comparison of two classes of data. A P value <0.05 was considered statistically significant.
Correlation analyses were carried out Pearson’s/Spearman’s test for parametric/nonparametric continuous variables, respectively. Overall survival (OS) and disease-free survival (DFS) are defined as the probability (ranging from 0 to 1) that a patient diagnosed with the disease is still alive (OS) or is free from the disease (DFS) at a time point from surgery. Survival analysis was estimated through the Kaplan-Meier method, and differences between groups were assessed by Log rank test. Univariate and multivariate Cox regression analyses of DFS in patients stratified for the indicated factors in high and low classes for PreS FSCN1 levels and PostS FSCN1 levels were performed by SPSS.

All data were presented as means and SD, and statistically analyzed using SPSS software 27.0 (SPSS Inc., Chicago, IL, USA). After the normality and homoscedasticity assumptions has been confirmed, one-way ANOVA and Duncan’s multiple range test were used to assess the differences among groups at p < 0.05. GraphPad Prism 8.0 (GraphPad Inc., La Jolla, CA, USA) was used for data visualization.

The mean RLX plasma concentrations (± S.D) of both treatments were plotted versus time. The pharmacokinetic parameters; peak RLX plasma concentration (C_max, ng/mL), the time to reach C_max (T_max, h), the mean residence time (MRT_0–∞, h), the elimination half-life (t_1/2), the area under the curve from zero to the last sampling point (AUC_{0–48 h}, ng.h/mL) and from zero to infinity (AUC_0–∞ ng∙h/mL) were estimated by application of non-compartmental analysis using WinNonlin software Ver. 8.3 (New Jersey, NJ, USA), and multivariate ANOVA using general linear model in SPSS software 27.0 (SPSS Inc., Chicago, IL, USA) and a non-parametric Wilcoxon signed rank test for Tmax. The AUC_{(0–48 h)} and AUC_(0–∞) values were used for calculating the relative bioavailability of the test treatment. Results were expressed as the mean (± S.D) except for T_max which was expressed as the median value.

Statistical analysis was performed with the SPSS software 27.0. The normality of the data was checked with the Shapiro–Wilk test. A one-way ANOVA with Dunnett correction was used to compare the means of multiple groups to one control group. If normality could not be assumed, a Kruskal–Wallis one-way ANOVA test was used to compare multiple groups. When comparing the mean of two groups, we used independent samples T-tests.

All analysis was conducted using IBM SPSS Software 27. All data were assessed for normality using Shapiro–Wilks test for normality. Data violated normal distribution, therefore, comparisons between total match-play and total training were assessed using Mann–Whitney U test.
Total match-play and training (ball-in-play) are reported as median (interquartile range). Relative running demands were calculated by dividing the total value by the time spent on each training component. Relative comparisons between training components and that of competitive match-play were assessed using Kruskal–Wallis H test, with pairwise comparisons and Bonferroni adjustments. Relative descriptive statistics are reported as median (interquartile range). Significance was set at the accepted alpha level of p < 0.05. To calculate the magnitude of effect, non-parametric standardised effect sizes (r) were calculated by dividing the standardised test statistic by the square root of the sample size [24 ]. Effect sizes were interpreted using Cohen’s d estimate of effect size interpretations: trivial (r < 0.20), small (r = 0.20–0.49), moderate (r = 0.50–0.79), large (r = 0.80–1.00) [25 ]. Furthermore, total training and training components were expressed as a percentage of match-play data.