Spss statistic software version 25

Data were analyzed using IBM SPSS statistic software version 25 (IBM, Armonk, NY, USA). Continuous variables are described as means ± SD. Sample size was calculated using G-Power^{59 (link)}. For a sample size of 83 individuals, if α  =  0.05 statistical power is 89%. All data underwent Kolmogorov-Smirnov analysis for normality of distribution. Parametric and nonparametric analyses of variance with corrected post hoc tests were used to evaluate the effect of experimental phase (T1/T2/T3/T4) and of group (PARO/Control group) on perceived happiness and on oxytocin levels and the effect of condition (Baseline/Touch/No-Touch in the PARO group and S1/S2/S3 in the control group) on pain ratings. Correlations between pairs of variables were calculated with Pearson’s r; p < 0.05 was considered significant. The Bonferroni correction was applied to multiple comparisons, where needed.

All the markers (EBV oncoproteins and immune markers) and the tumor volume were numeric variable. A correlation test was done for all variables using Spearman correlation test. For graphical presentation, a scatterplot was used to display the correlation data of all tested variables. All data was processed with IBM SPSS Statistic Software version 25.

All quantitative data are expressed as median (interquartile range) and were compared with the Mann–Whitney-U test. Differences between the study groups were assessed with the Chi-squared test of independence for nominal variables or the Fisher’s exact test as needed. Univariate logistic regression models were conducted to identify risk factors for venous thrombosis or limb ischemia. For limb ischemia, one model included parameters at the time of cannulation and the other model included parameters assessed during the ECMO therapy. The multivariat logistic regression model was adjusted for alle factors with a p values of less than 0.1 in the univariate analysis. In addition, a multivariate model for biochemistries according to limb ischemia was calculated. The cutoff points for NIRS were identified by receiver operating characteristic (ROC) analysis using the Youden index. All reported p-values were two-sided, and a p-value of ≤ 0.05 was considered statistically significant. Data entry and calculation were done using Microsoft EXCEL365 ProPlus (Microsoft, Redmond, WA, United States) and IBM SPSS Statistic software version 25.0 (SPSS Inc., Chicago, IL, United States).

Statistical differences among more than two groups were determined using one‐way ANOVA, or two‐way ANOVA, followed by the Bonferroni multiple comparison test. A value of P < 0.05 in two‐tailed test was considered statistically significant. All data are presented as the mean ± SEM. Single regression analysis and multivariable regression analysis were used to evaluate the relationships between SIR and the level of serum cytokines or EV‐miRNAs. SIR and either serum level of IL‐1β, IL‐6, and TNF‐α or EV‐miRNAs were included in variables. Regression analyses and Pearson's correlation test were performed using spss statistic software version 25.0 (SPSS Inc., Chicago, IL, USA) and matlab 2018a (MathWorks Inc.).

Descriptive statistics are presented as numbers (n), range, and fractions (%), and continuous data as median [interquartile range (IQR): 25%; 75%], as appropriate. Continuous data were compared with the Mann-Whitney U-test, and categorical data with the Chi² test. A multivariate linear regression model was calculated, including all independent variables with p < 0.1 in the univariate model. Multivariate linear regression analyses were conducted to identify risk factors for Rf, including known possible risk factors such as ECMO and cannula specifics as well as hemodynamic and respiratory parameters as published previously (13 (link)). Linear quadratic regression models were used to assess associations between the Rf , Q_EC, and Q_EFF. A two-sided p < 0.05 was considered statistically significant. Data entry and calculation were done with Microsoft EXCEL365 ProPlus (Microsoft, USA) and IBM SPSS Statistic software version 25.0 (SPSS Inc. Chicago, IL, USA).

Propensity-score matching was carried out by matching every patient who received Argatroban with a historical UFH patient to control for potential confounders such as age, sex, body mass index (BMI), sepsis-related organ failure assessment (SOFA), the acute respiratory distress syndrome (ARDS) subgroup (pulmonary, non-pulmonary, post-trauma, and miscellaneous), resuscitation and days of mechanical ventilation before ECMO, and days on ECMO to account for potential imbalances between the Argatroban and the UFH group. More details are presented in the supplements.
All non-normally distributed quantitative data are expressed as median (interquartile range) and were compared with the Mann–Whitney-U test. Differences between the two study groups were assessed with the Chi-squared test of independence for nominal variables. Linear regression models were calculated to assess the association between the Argatroban plasma concentration and aPTT or SOFA. All reported p values were two-sided, and a p value of ≤ 0.05 was considered statistically significant. Data entry and calculation were done using Microsoft EXCEL365 ProPlus (Microsoft, USA), IBM SPSS Statistic software version 25.0 (SPSS Inc. Chicago, IL, USA), and the software package R (v 3.6.1 R Foundation for Statistical Computing).