Spss 25.0 statistical package

Statistical analysis was performed using dedicated statistical software (SPSS 25.0 statistical package; SPSS Inc., Armonk, NY, USA: IBM Corp.) and the Deepwise research platform (https://keyan.deepwise.com, V1.6.2). Categorical variables are reported as percentages, and continuous variables as means (SDs) or medians and interquartile ranges (IQRs). Student’s t-test and the non-parametric Mann–Whitney test were used to assess if there were significant differences between patients with and without ISR. Interobserver agreements were calculated by ICC. Features with ICC > 0.75 were considered to be consistent. Cox regression was performed to determine which baseline characteristics were independently associated with ISR. Receiver operating characteristic (ROC) curves were constructed, and ISR prediction models were developed using clinical, conventional plaque features and selected radiomics features to calculate the area under the ROC curve (AUC) and sensitivity and specificity. The DeLong test was used to compare the radiomics model with the traditional and combined models.

Data is given as numbers and percentages or means and standard errors. Statistical comparisons between two groups were made using the χ² test (categorical variables) or Student’s t test (quantitative variables). Statistical significance was set at P ≤ 0.05. All calculations were made using the SPSS 25.0 statistical package (SPSS Inc., Chicago, IL, USA).

Based on the values of the primary outcome variable (24-h ambulatory SBP) obtained in previous studies, at least 36 patients (18 per group) were required to identify a difference of 6 mmHg in the primary efficacy variable between altitude exposure conditions.
Descriptive statistics are presented as means and standard deviations and absolute and relative frequencies overall and separately for non-HT and HT subjects. Shapiro–Wilk test was used for assessing normality in group data. Log transformation was applied to data without normal distribution. Levene's test was used for assessing variance homogeneity. To determine the effects of altitude (3: sea level, SL; high altitude day 1, HA-D1; and high-altitude day 7, HA-D7) and group (2: NT and HT) effects on the primary outcome (24-h ambulatory SBP) and on other variables of interest (other ambulatory BP variables, clinic HR, SBP, DBP, and SpO2), a mixed model multivariate analysis of variance (MANOVA) was applied. The follow-up was carried out by means of univariate analyses contrasts. Finally, in the case of significant effects, pairwise comparisons with Tukey correction were performed. The data were analyzed using the SPSS 25.0 statistical package (SPSS Inc, Chicago, IL, USA). For all statistical tests, an alpha level of 0.05 was used.

Data are shown as means and standard deviations or as numbers and percentages. Statistical comparisons between two groups were carried out with the Student’s t test (quantitative variables) or the χ-square test (categorical variables). Logistic regression models were fitted to investigate the combined effect of selected variables on mortality. The diagnostic accuracy of the McCabe and Charlson indices in predicting mortality was assessed by receiver operating characteristics (ROC) analysis [18 (link)]. Statistical significance was set at p ≤0.05. All calculations were made using the SPSS 25.0 statistical package (SPSS Inc., Chicago, IL, USA).

Descriptive statistics were calculated for all variables in the study, including means and standard deviations (Table 1). An analysis of bivariate correlations was performed for the study variables (Table 2). The subsequent focus was upon identifying distinct motivational profiles within the overall sample. A hierarchical cluster analysis using Ward’s method was conducted with the motivational variables from the BRSQ to help identify these motivational profiles. Subsequently, a confirmatory solution was attempted using a K means agglomerative method with a second sample. Finally, a hierarchical cluster analysis using Ward’s method was performed with the entire sample. To examine the characteristics of each motivational profile in relation to teacher controlling behaviors, the basic needs satisfaction variables, the perceived importance of physical education, the intention to be physical active and actual physical activity, a multivariate analysis of variance (MANOVA) was conducted. All analyses were conducted using the SPSS 25.0 statistical package (SPSS Inc., Chicago, IL, USA).