Statistical analysis was performed using IBM SPSS Statistics for Windows software version 26.0 (IBM, New York, NY, USA). Because peptide data did not pass the Shapiro-Wilk Normality test, either log transformation or nonparametric evaluation was performed. Pearson’s correlation tests were used to determine relationships between Ang-(1–12) and other clinical and biochemical variables after log transformation. To access the association of PH to plasma renin activity, multiple linear regression was performed with all other independent variables as fixed effects. Two-way ANOVA was employed to assess sex differences among variables, and the results are presented as mean ± the standard deviation (SD). Additionally, we created a multivariate linear regression model to ultimately assess the effect size associated with each covariate on PRA. Collinearity was assessed using a variance inflation factor and condition index, with values <10 indicating acceptability. Additionally, sensitivity analyses were performed to investigate effect of covariates on range of plasma Ang-(1–12), plasma Ang II and NT-proBNP. A two-tailed P-value <0.05 was considered statistically significant.
Spss statistics software for windows version 26
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SPSS Statistics software for Windows, version 26.0, is a comprehensive and widely-used data analysis tool. It provides a robust set of features for statistical analysis, data management, and visualization. The software is designed to handle a wide range of data types and supports advanced statistical techniques, making it a valuable resource for researchers, analysts, and professionals across various industries.
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Lab products found in correlation
33 protocols using spss statistics software for windows version 26
1
Angiotensin-(1–12) Biomarker Analysis
2
Multivariate Analysis of Survival and Recurrence
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Data were analyzed using the IBM SPSS Statistics for Windows software, version 26.0 (IBM Corp.). Continuous variables were expressed as median (IQR, interquartile range) and intergroup differences were compared using the t‐test or the Mann–Whitney U test. Categorical variables were expressed as frequencies (percentages) and the intergroup differences were compared using the chi‐squared test or the Fisher's exact test. The Kaplan–Meier method was used to draw the survival curves of OS and tumor recurrence. The independent risk factors of OS and tumor recurrence were analyzed by Cox multivariate analysis. P < 0.05 was considered statistically different.
3
Statistical Analysis of Experimental Data
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Statistical analyses were carried out using IBM SPSS Statistics for Windows, software version 26.0 (Armonk, NY, USA). The data were graphed as the mean ± SEM. Comparisons between groups were made using a Mann–Whitney U test or Kruskal–Wallis test followed by pair-wise comparison with Bonferroni correction, depending on the number of experiments being compared.
4
Statistical Analysis of Clinical Outcomes
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Quantitative data are expressed as mean ± SD, and individual t-tests were used to compare differences between the groups. The chi-square test was used to compare categorical data between the groups. Clinical recurrence was analyzed by the Kaplan-Meier method with intergroup log rank significance testing. Statistical analysis and PSM were performed using SPSS Statistics for Windows software version 26.0 (IBM Corporation, Armonk, NY, United States), and P < 0.05 was considered to indicate statistical significance. All tests were two-sided.
5
Statistical Analyses Protocol for Research
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Statistical analyses were performed using the IBM SPSS Statistics for Windows software, version 26 (IBM Corp., Armonk, NY, USA). Categorical variables were presented as numbers (percentage). Continuous variables were presented as medians (range). The compliance of the numerical values to the normal distribution was examined using histograms, in addition to Kolmogorov–Smirnov and Shapiro–Wilk tests. Since the quantitative variables did not display normal distributions, two independent groups were compared using the Mann–Whitney U-test. The chi-square test was used to compare the proportions in different groups. Time-to-event data were derived using Kaplan–Meier methodology. Factors that affect outcomes were analyzed using the log-rank test. Statistically significant factors in log-rank tests were evaluated by multivariate Cox regression analysis. An overall p-value of < 0.05 was considered to be statistically significant.
6
Multilevel Analysis of School-Based Factors
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Descriptive data were presented as percentages for categorical variables and means with standard deviations for continuous variables. Differences between participants and drop-outs at baseline were tested using Chi2 tests and independent sample t-tests. As the clusters (schools) explained a much of the variation in outcome variables, multilevel linear regression analyses were performed. The models were applied on two levels: the school and the individual, and a random intercept was modelled for each school. Unstandardised betas (β) are reported with 95% confidence intervals (CI). Residuals were plotted and checked for normality. A bootstrapping estimation was performed to produce sensible estimates for standard errors in the multi-level model, as suboptimal fitness of model or heteroscedasticity was detected. Each exposure was explored separately for association with each outcome. The models for anthropometrics and fitness variables were adjusted for gender and parental education. Crude models are reported in supplementary tables 1 –2 . No imputations for missing data or loss to follow-up were performed. The level of significance was set to p < 0.05. The IBM SPSS Statistics for Windows software, version 26 was used for the statistical analyses.
7
Exploring Psychological Well-being: Stress, Coping, and Moderators
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We analyzed the data using the IBM SPSS Statistics for Windows, software version 26 (IBM Corp, Armonk, NY, USA). The descriptive statistics of the frequencies, means and standard deviation were reported for all the variables. A Pearson Product-Moment correlation was used to investigate the relationships between the stressors, coping styles, and psychological well-being. A hierarchical multiple regression was conducted to establish the moderators and mediators. In model 1, we entered the sources of stress relating to the primary appraisal and the measures on gender, age, and religious belief. In model 2, we entered the secondary appraisal relating to the coping styles. For the final model, we entered the moderators that had been identified, after testing for the interaction effects between each predictor and the potential moderator variable. We conducted separate regressions to establish the moderators, according to the guidelines proposed by Baron and Kenny [32 (link)]. Those interaction variables that explained a statistically significant amount of variance in the psychological well-being were entered into the final model of the hierarchical multiple regression.
For all the statistical tests, the variables were considered significant at a significance level of 0.05.
For all the statistical tests, the variables were considered significant at a significance level of 0.05.
8
Factors Associated with FIM Improvements
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To investigate whether changes in FIM scores observed after the rehabilitation program (T1) may be associated with participants’ cognitive and psychological functioning at admission, linear regression analysis was performed.34 As a preliminary analysis, the correlation and directionality of the data were assessed to formulate the statistical model, using Pearson’s correlation coefficient to obtain Pearson’s r for continuous factors; and Spearman’s rank correlation coefficient to obtain Spearman’s ρ for categorical factors. Those factors significantly associated with the main outcome score (P ≤ 0.05) were further investigated with the linear regression model, for which the R2 value was reported as a goodness-of-fit measure. In addition, the significance of the model was evaluated through the F-value and the P-value. Finally, the relative contribution of factors included in the statistical model was verified with the independent variable (i.e., the outcome score). For each factor, the variance inflation factor (VIF) was reported as a measure of multicollinearity.
Data are presented as n (%) prevalence or mean ± SD, and range, and were analyzed using SPSS Statistics software for Windows, version 26.0 (IBM, Armonk, NY, USA). A P value ≤0.05 was considered to be statistically significant.
Data are presented as n (%) prevalence or mean ± SD, and range, and were analyzed using SPSS Statistics software for Windows, version 26.0 (IBM, Armonk, NY, USA). A P value ≤0.05 was considered to be statistically significant.
9
Comparative Analysis of 18F-FDG and 68Ga-FAPI Uptake
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All statistical analyses were performed using IBM SPSS Statistics software for Windows version 26.0 (IBM, Chicago, USA). We performed descriptive analyses of all patients including demographic and tumor-specific characteristics. The median and SD were used for the determination of SUVmax and TBR. The uptakes of 18F-FDG and 68Ga-FAPI were compared by using the Wilcoxon signed-rank test. The 68Ga-FAPI SUVmax between inflammatory and Ovarian malignancies was compared using the Mann–Whitney U test. A two-tailed P < 0.05 was considered to indicate a statistically significant difference.
10
NGAL Prognostic Biomarker in ICU Mortality
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All statistical analyses were performed using SPSS Statistics Software for Windows, Version 26.0. (IBM Corp., Armonk, NY, USA), or MedCalc Statistical Software version 18 (MedCalc Software bvba, Ostend, Belgium). Numerical results are expressed as mean ± standard deviation (SD), or median (interquartile range), as appropriate. Continuous variables in the survivor and non-survivor groups were compared using the independent t-test (parametric values), or Mann–Whitney U test (non-parametric values), as appropriate. The chi-squared test or Fisher’s exact test was used to compare categorical variables. In all analyses, a p-value < 0.05 was considered statistically significant. A receiver operating characteristic (ROC) curve was used to assess the prognostic function of baseline NGAL levels for predicting ICU mortality. Cox proportional hazards regression analysis was used to determine ICU mortality. The ROC curve analysis was used to identify the optimal cut-off values of NGAL in BAL and serum according to the Youden index. ICU mortality was analyzed using a Kaplan–Meier curve according to baseline NGAL levels in serum and BAL. In Supplementary Fig. 1 , BAL (A) and serum (B) NGAL levels were expressed as fold increases in NGAL levels (each value/mean of control).
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