Spss for windows v 27

Statistical analyses were performed using SPSS for Windows v. 27.0 (IBM Corp, Armonk, NY, USA). Correlations between continuous variables were evaluated by a nonparametric measure (Spearman rank sum analysis). Group comparisons were analyzed by a nonparametric test (Mann-Whitney U test). All tests of significance were two-sided and p values of ≤0.05 were considered significant. To evaluate the potential of a select group of miRNAs as prognostic markers, we calculated the ROC (Receiver Operating Characteristic) curve and the ROC AUC (Area Under the Curve) for a multivariate logistic regression model using normalized miRNA expression values individually and in combination.

Statistical analysis was performed using SPSS for Windows v.27.0 (IBM Corporation, Armonk, NY, USA). Survival analysis was performed on the entire cohort. Further subgroup analysis was performed using early tumor stage and low-grade tumor as covariates. MRE11 protein expression in samples from the cancer core and periphery was assessed in univariate and multivariate analyses using Kaplan–Meier curves and Cox’s proportional hazard ratio (HR) survival modeling. Sex, age, tumor-node-metastasis (TNM) stage, differentiation, lymph node (LN) involvement, metastasis stage at diagnosis, LVI/PNI, and adjuvant and neoadjuvant treatments were included as covariates. The statistical significance of results from univariate and multivariate analyses was determined using the Mann–Whitney U test. p < 0.05 was considered significant.

All statistical analyses were performed using SPSS for Windows v.27 (IBM Corporation, Somers, NY, USA). Mean (M) and standard deviation (SD) values were generated for all outcome measures. Paired samples t-tests were used to test for differences in post-occlusion, pre-exercise changes between the occlusion conditions, including arterial flow and heart rate. A standardized mean effect size (ES) calculated as (M_BTF − M_TRA/SD_BTF) for between-group comparisons, whereas within-conditions were calculated as (M_Post − M_Pre/SD_Pre), and qualitatively described as small, moderate, and large (ES = 0.2, 0.5, and 0.8, respectively) [24 ]. Normality for these variables was determined using the Shapiro Wilk test and visual inspection of residual histograms and QQ-plots. When the normality assumption was violated (‡), the Kruskal-Wallis test was used.
Repeated measures ANOVAs were used for the other continuous variables to compare raw values and change scores of measures between- and within- conditions. Sphericity was tested using Mauchly’s W. If sphericity was violated, the non-parametric Kruskal-Wallis independent samples test was used. If a main effect was found, a post-hoc analysis with Bonferroni correction was utilized to determine between-condition differences. Statistical significance was indicated using an α level of p < 0.05 and was used for all tests.

Descriptive statistics (mean, SD, median, and ranges) were used to describe the population characteristics. The Kruskal-Wallis test, followed by a Mann-Whitney U test, Student’s t-test, or Chi square test were used to compare differences between groups, as appropriate. Correlations between parameters were analyzed using Spearman rank correlation coefficients. The level of statistical significance was set at P ≤ 0.05. Statistical analysis, including sample size calculation was conducted using IBM SPSS for Windows v27. Using a 2-sided test, 5% significance level test (α = 0.05) with power 80% power (β = 0.2), the required sample size is approximate 198 to detect Spearman Rank order of 0.2 between cortisol and glucose.