Spss version 19.0 for windows

Continuous variables were expressed as the mean and standard deviation. Differences between the subgroups were compared by t-test or Mann-Whitney U test. Categorical variables were compared using χ² test with Yates’ correction or Fisher’s exact test. Factors with significant impact on PHLF upon univariate analysis were explored with multivariate forward logistic regression as hypothetical independent predictors of PHLF. A significance level of 0.05 was used in all analyses. The prognostic value of PHLF prediction model and the LSM only were assessed using receiver operating characteristic (ROC) curve analysis (MedCalc Software bvba, Ostend, Belgium). The area under the ROC curve (AUC), the sensitivity, the specificity, the positive and negative predictive values, and the positive and negative likelihood ratio for cutoff values were obtained.
Data analysis was performed using SPSS, version 19.0 for Windows (SPSS, Inc., Chicago, IL) and R software 2.10.1 (R Foundation for Statistical Computing, Vienna, Austria; www.r-project.org). All reported p values were two-sided, and p < 0.05 was considered to be statistically significant.

Continuous data are presented as either the mean ± standard deviation (SD) for normally distributed variables or the median (interquartile range) for non-normally distributed variables, and they were analysed between groups using the Wilcoxon rank-sum test. Categorical variables are expressed as group percentages and were compared among samples using either Pearson’s χ² or Fisher’s exact test. All statistical tests were 2-sided, with a significance level of P < 0.05. Receiver operating characteristic (ROC) curves were generated and areas under the curve (AUCs) were calculated to evaluate the ROC effects. Statistical analyses were conducted using SPSS version 19.0 for Windows (SPSS Inc., Chicago, IL, USA).

Statistical analyses were performed using SPSS version 19.0 for Windows (SPSS Inc., Chicago, IL, USA). The Kolmogorov–Smirnov test was used to check whether the data conformed to normal distribution. Continuous variables with normal distribution are expressed as the mean ± standard deviation (SD), and categorical variables are expressed as N (%). The measurement data from CCTA were compared with those from RCCT using the paired t test. Correlations and agreement of EAT measurements between RCCT and CCTA scans were evaluated using Pearson’s correlation test and Bland–Altman analysis. The difference in EAT according to the presence or absence of coronary atherosclerotic plaques was analyzed using Student’s t-test, Mann–Whitney U test, and the dichotomic variables were analyzed using chi-squared test. A P value < 0.05 was considered statistically significant.

Statistical analysis was performed using SPSS version 19.0 for Windows (SPSS Inc., Chicago, IL). Data were tested for normality by visual inspection and the use of Kolmogorov-Smirnov test. Continuous variables were expressed as median (interquartile range, IQR) and were compared by a nonparametric Mann-Whitney test. Results are expressed as mean ± SD or median (IQR). Proportions were compared by the χ² test. Variables were compared between groups of sessions (with hypotension vs without hypotension). The binary classification (“no intradialytic hypotension” vs “intradialytic hypotension”) was used as an outcome variable in a way that “no hypotension” and “hypotension” were coded as 0 and 1, respectively. Quantitative and qualitative variables associated with hypotension with a p value below 0.05 in univariate analysis were selected for inclusion in a multivariable logistic regression model. Logistic regression with both categorical and continuous independent variables was used to build predictive models for the occurrence of hypotension. A one way-ANOVA was used for comparison between groups of patients according to ultrasound profiles. The four “ultrasound profiles” were compared for association with hypotension, dialysis discontinuation, or mortality in 28 days, using χ² analysis. Statistical significance was assumed at the 5% level.

All statistical analyses were performed with the Statistical Package for the Social Sciences (SPSS) version 19.0 for Windows (SPSS Inc., Chicago, IL, USA). Group differences in demographic variables, BIS-11, and CIAS scores were analyzed using the independent t-test and chi-square test. The correlation between abnormal brain activations and behavioral and personality measures in subjects with IGA was assessed using Pearson’s correlation analysis. For the behavioral data, a repeated measures ANOVA was used to analyze the accuracy of the Go/No-Go Task, with group (IGA and control) as the between-participant factor, and Go/No-Go (Go and No-Go) as the within-participant factor. Statistical significances were defined at the 0.05 level, two-tailed.

The demographic factors in this study were sex; body mass index (BMI); caffeine consumption habits; nap habits; mean TST, SOL, and WASO in the week preceding the study; and MEQ score. Nap and caffeine consumption habits were categorized into two groups: zero or one or more times per week. Based on the overweight criteria used in Taiwan [29 (link)], the participants were classified as BMI ≥ 24 and BMI < 24. Differences in the baseline characteristics of PSG-recorded sleep parameters were analyzed using a t test or Pearson correlation. The SWS and REM stages in the PSG-recorded data reached 0 for several participants; therefore, we used Box–Cox transformations with λ₁ = 0 and λ₂ = 0.01 before conducting analyses [30 ].
To determine whether TST, SOL, and WASO mediate the relationship between the MEQ and napping, regression analyses were conducted based on the guidelines proposed by Shrout and Bolger [31 (link)]. Furthermore, to confirm the relevance of the indirect effects, a nonparametric bootstrapping procedure was performed [32 ]. A total of 1000 bootstrap resamples were created to obtain bias-corrected estimates. Analyses were performed using SPSS Version 19.0 for Windows (SPSS, Inc., Chicago, IL, USA). All statistical tests were two-tailed and p < 0.05 was considered statistically significant.

Intensities of bands in western blot and Areas under peaks in Wes were detected to present for the relative amount of special proteins. A linear regression fit was used to determine the linear dynamic ranges of special proteins. Pearson correlation analysis was used to estimate the correlation of results from the western blot and Wes assay. Using the fold results in rank ordinal numbers, Spearman rank correlation analysis was also used to measure the correlation of results from these two assays. A P-value of Correlation coefficient <0.05 was considered significant. Statistical tests were performed with SPSS version 19.0 for Windows (SPSS Inc., Chicago, IL, USA).