Spss software package version 18

All data were expressed as the mean ± SE and analyzed
using the SPSS software package version 18 (Inc.
Chicago, IL, USA). The difference among the groups was
also analyzed by one-way analysis of variance (ANOVA),
followed by Duncan post hoc test. The P<0.05 were
considered statistically significant.

Statistical data analysis was performed using the SPSS software package, version 18 (SPSS Inc., Chicago, IL, USA). The methods of descriptive statistics were used to show the basic characteristics of the newborns. Mean values ± standard deviation with range (minimum–maximum) were used for continuous variables, and relative frequency was used for categorical variables. The distribution of numerical data was tested for normality using the Kolmogorov–Smirnov and Shapiro–Wilk tests. Student’s t-test for independent samples determined the existence of a statistically significant difference between the compared groups in the values of continuous variables, provided that the distribution is normal; otherwise, the nonparametric alternative Mann–Whitney U test was used. Comparisons between categorical variables were made using the χ² test (or Fisher’s exact probability test for the low frequency of certain categories). If the probability of the null hypothesis was lower than 5% (p = 0.05), the difference was considered statistically significant. The influence of a large number of variables on the examined dichotomous outcome, as well as the mutual interaction of potential predictor variables, was examined by binary logistic regression, and the results are presented in the form of a raw and adjusted odds ratio (OR) with the associated 95% confidence interval.

Data were analyzed using the SPSS software package, version 18 (SPSS Inc., Chicago, IL, USA). Basic descriptive indicators were calculated, and the groups were compared according to their characteristics using the χ² test and one-way ANOVA. The main analysis was performed using a 3 × 4 mixed-model ANOVA, with intervention type as an independent factor and time-point as a dependent factor. For the independent component of the analysis, Levene’s test was used to test the assumption of variance homogeneity among groups. For the dependent component of the analysis, sphericity was assessed by Mauchly’s test. In violation of Mauchly’s test, the Greenhouse–Geisser correction was used.
With the assumption of sphericity and homogeneity of variances, the main analysis—testing the effects (main effects of group and time-point, and their interaction)—was performed. According to Cohen [23 ], eta squared (η²) was used to classify the effects (i.e., small (0.01–0.05), medium (0.06–0.13), and large (>0.14)). In cases where significant main effects were determined, individual comparisons between variables were performed using the Fischer’s post-hoc LSD test. The same test was used for the analysis of simple effects, in case of a significant interaction. The level of statistical significance was set at p ≤ 0.05.

The SPSS software package, version 18 (SPSS Inc, Chicago, IL, U.S.A.) and STATA 14.0 (Stata Corporation, College Station, TX, U.S.A.) were used for statistical analyses. Data were analyzed by sample-paired t-test to compare the differential expression levels of NDRG3 mRNA between tumor and adjacent non-tumor liver tissues of HCC patients.
Expression of NDRG3 protein was estimated using the Wilcoxon signed rank nonparametric test because the data were not normally distributed. The associations between NDRG3 expression and clinicopathologic parameters were calculated by chi-square tests. Kaplan–Meier method was used to generate survival curves and the differences were analyzed using the log-rank test. Cox regression model was established for the multivariate survival analysis to determine prognostic factors that were signifcant on univariate analysis for either DFS and OS. Significance was established when P<0.05.

The SPSS software package, version 18.0 (SPSS Inc., Chicago, IL, USA) was used for statistical analysis. Univariate and multivariate logistic analysis were used to identify predictors of PVTT. Receiver operating characteristic (ROC) curve analysis was used to detect the most appropriate cut-off value. For the evaluation of the new index to predict PVTT, the area under the ROC curve (AUROC), sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), and hazard ratio (HR) were calculated. Overall survival was calculated using the Kaplan–Meier method and compared with the log-rank test. Continuous variables were assessed by Student’s t test or nonparametric tests. Pearson’s χ² test was performed to compare the categorical variables. In all tests, P values were statistically significant if less than 0.05.

All statistical analyses were performed using the SPSS software package version 18.0 (SPSS Inc., Chicago, IL, USA). Adequacy of all parameters to normal distribution, was tested by using Kolmogorov–Smirnov Test. Parametric tests were applied to data with normal distribution, while non-parametric tests were used to data without normal distribution. Descriptive statistics are presented as percentages for discrete variables and mean ± SD for continuous normally distributed variables. Chi-squared test and analysis of variance (ANOVA) were performed to check the significance of binary and continuous variables respectively between different clusters. Then, binary logistic regression was carried out to investigate the association between CAD and other risk factors. A calculated difference of P < 0.05 was considered to be statistically significant.
All of the examinations were carried out for diagnosis of hypertension and assessment of cardiovascular risk in adults in according to guidelines from the European Society of Cardiology25 (link). The study protocol was approved by the ethics committee of the Second Affiliated Hospital, Xi’an Jiaotong University, in compliance with the Declaration of Helsinki. All the participants were informed of the nature and purpose of the study and signed written informed consent.