4.10. Statistical Analysis
Continuous variables are expressed as mean ± standard deviation or median with the minimum and maximum values. Categorical variables, such as frequencies and percentages, are reported. Normality distribution was evaluated using the Shapiro–France test. Non-parametric tests (Mann–Whitney) or Student’s tests, according to the Gaussian distribution, were performed to detect significant independent variables.Some variables were standardized and Bonferroni correction was adjusted for multiple comparisons. For the paired analyses (before–after), the Friedman or Wilcoxon test was used with the signed-rank test according to the distribution of the data. Pearson’s Chi-square (χWe perform a canonical correlation analysis (CC) to analyze the correlation of two subsets of variables. In the first set, we include the x expressed as U (p) (X1, X2, …. XP) in which the variables that they made up were (IL-1β, IL-2, IL4, IL-6, IL-8, IL-10, IL-12p70, IL-17A, TNF-α, INFγ, IP-10, MCP-1, and TGFβ-1). In the second set of variables, expressed by V(q) (Y1, Y2, …. Yq). We included the SOFA score, C-reactive protein, procalcitonin, lipoperoxidation, selenium, thiols, vitamin C, carbonylation, glutathione, Gpx, GST, GR, SOD, and antioxidant capacity (CAT). We include more than two variables on both sides of the equation in this type of analysis. The main objective of CC is to identify the linear combinations and the value of the response of that correlation.
And with the predictive variable, it is expressed as follows:The largest correlation was identified between U1 and V1; the second largest correlation was between U2 and V2, as long as there was no correlation between U1 and U2, nor between V1 and V2; and the third largest correlation was between U3 and V3 and, as described above. There could be no alteration between U3, and U1 and U2, nor between V3, and V1 and V2. These combinations are known as canonical variables. Differences were considered statistically significant when the 