Sas statistical software 9

Data are presented as median and interquartile range. The whole population was divided in two groups according to the airway driving pressure lower and equal/higher than 15 cmH₂O [3 (link)]. Physiological variables were compared with unpaired t test or Mann-Whitney rank sum test according to the result of the Shapiro-Wilk normality test. Categorical variables were compared with the chi-square test. The role of baseline variables (gas exchange and lung mechanics) on patients outcome from the intensive care unit was assessed with logistic regression analysis (odds ratio [OR] and 95 % confidence intervals [CI]). The agreement between results was assessed using linear regression. A receiver operating characteristics (ROC) curve was used to assess airway and transpulmonary driving pressure ability to predict a lung stress greater than 24 or 26 cmH₂O [32 (link)]. Statistical analysis was performed with SigmaPlot 12.0 (Systat Software, San Jose, CA, USA), logistic regression with SAS statistical software 9.2 (SAS Institute, Cary, NC, USA).

Statistical analyses were performed using SAS statistical software 9.2 (SAS Institute, Inc., Cary, NC, USA). Normal distribution of the data was calculated with the Kolmogorov–Smirnov test. Continuous variables were expressed as mean ± standard deviation (SD). Variables that were not normally distributed were rank transformed before analysis. Differences on clinical characteristics and metabolic biomarkers were evaluated using t-test or a one-way analysis of variance (ANOVA) for normally distributed variables or non-parameter Wilcoxon test for non-normally distributed, with multiple comparisons performed using SNK (Student-Newman-Keuls) correction. Partial correlation analysis was used to study the relationships between brain–gut peptides and other metabolic parameters. A multiple stepwise regression analysis was employed to identify influence factors of brain–gut peptides, using a stepwise selection method with a P-value of 0.15 at entry and a P-value of 0.15 at stay. All independent variables in the multiple regression were tested for multicollinearity. Statistical tests were based on two tailed probability. A P-value less than 0.05 was considered statistically significant. Power calculation was performed using PASS software 11.

Statistical analyses were carried out using SAS statistical software 9.2 (SAS Institute Inc., Cary, NC, USA). Data were presented as mean ± stand error. For different treatments (antibiotics and starvation), the proportion of winged/wingless among treated and control aphids was compared and analyzed, respectively. An independent T-test and One-Way ANOVA for means were used to investigate the phenotypic impacts. The end point measurements included the survival rate and the percentage of winged morphs.

SAS statistical software 9.3 (SAS Institute, Care, NC) and IBM SPSS statistics 22 were used for the statistical analysis. Mann–Whitney U or Pearson Chi‐squared (χ²) tests were used in univariate analysis for patient characteristics depending on the data characteristics. Cox regression model was plotted with time‐dependent covariate (asparaginase treatment) and the risk score model for CVT by using FREQ procedure. All analyses were two sided and P < 0.05 was considered statistically significant.

Results were analyzed as concentrations of free thiamin, TMP, TPP, total thiamin, free riboflavin, FAD, and total riboflavin in milk. Participants were separated into two sub-groups based on their initial value being at either 2 or 6 wk postpartum (2 wk sub-set = 2 and 24 wk, 6 wk sub-set = 6 and 24 wk) and by treatment (LNS or control group). The distributions of concentrations were assessed for normality and transformations performed to normalize the variables: logarithmic transformations were performed on TPP, free thiamin, total thiamin, free riboflavin, and total riboflavin, while FAD was subjected to square root transformation. TMP had a normal distribution and was not transformed. Means were compared by paired t-test to evaluate changes over time within treatment group (2 to 24 wk; 6 to 24 wk); for evaluation of treatment effects at the time points of sample collection (2 or 6 wk, 24 wk) means were compared by t-test. P-values < 0.05 were considered to be statistically significant. SAS® statistical software 9.3 (SAS Institute, Cary, NC) was used for all statistical analyses.