Spss version 21.0 statistical software

Statistical analyses were performed using SPSS version 21.0 statistical software (SPSS, Chicago, IL, USA). To test the differences between groups, the Mann–Whitney U test, the Kruskal–Wallis test and the Wilcoxon signed-rank test for paired analysis were used for continuous variables, and the χ² test was used for proportions. When an overall significance was obtained by the Kruskal–Wallis test, pairwise post hoc analyses were calculated using SPSS software (nonparametric tests of two or more independent samples). Spearman’s rank correlation coefficient was used to assess the relationships between two continuous variables. All significance tests were two-tailed and conducted at the 0.05 level of significance. Univariate analyses of the association of survivin and other baseline clinical and demographic variables with radiological and clinical outcomes were performed. The predictive performance of survivin was analysed by 2 × 2 tables and odds ratios (ORs), sensitivity, specificity, positive predictive value (PPV) and negative predictive value were calculated.

The data were checked and categorised. SPSS version 21.0 statistical software was used for statistical calculations. Two-sided p-values of 0.05 were considered to indicate statistical significance. Categorical variables are presented as frequencies and percentages, while continuous variables are presented as means and standard deviations (±SD). The percentage differences in behavioural compliance across categorical variables were examined using chi-squared tests. An unconditional logistic regression method was adopted to determine the factors influencing respondents’ compliance after controlling for covariates. We used the unconditional logistic analysis method, as the dependent variable is dichotomous and matching design data is not used in this study. The dependent variable was the level (higher/lower) of respondents’ compliance with preventive behaviours (1 = no, 0 = yes). Education level, occupation, COVID-19 knowledge, anxiety, and quarantining were set as independent variables, while gender, age, household registration, and years of employment were set as covariates. Multiple categorical variables (age, occupation, education, and years of employment) were set as dummy variables.
The adjusted odds ratios (OR) for the variables and their 95% confidence intervals (95% CIs) were calculated using the unconditional logistic regression model.

The categorical variables were analyzed using absolute and relative frequencies with X² test. Quantitative variables were analyzed using mean and standard deviation. Differences between the means were analyzed using a Student’s t-test or Mann–Whitney U test. Patient survival was analyzed using Kaplan–Meier estimator and log-rank tests. And the relative risks of patient survival according to their surgery or waiting were analyzed by Cox-regression test. The confounding factors were adjusted. Statistical calculations were performed using SPSS version 21.0 statistical software (SPSS, Chicago, IL) and p < 0.05 was considered statistically significant.

The statistical analysis was conducted using SPSS version 21.0 statistical software (SPSS Inc., Chicago, IL, USA). Clinical differences between cTACE and DEB-TACE were assessed using non-parametric Mann–Whitney U-Test and Pearson’s chi-square test, with significance set at p < 0.05. Differences in tumor blood flow before and after treatment were evaluated using a non-parametric Mann–Whitney U-Test. Pearson’s correlation test was employed to calculate correlation coefficients. All reported p-values are two-sided, with statistical significance defined as p < 0.05. The relationship between the ABF change ratio and the degree of tumor enhancement on DSA was examined using Pearson’s chi-square test, with significance set at p < 0.05. Median PFS was calculated using the Kaplan–Meier method and compared using the log-rank test. PFS rates at 6 and 12 months were similarly determined.

All data were expressed as mean ± SEM. Statistical analyses were performed using SPSS (version 21.0) statistical software (SPSS Inc., Chicago, IL, USA). Student’s t-tests were used for the comparison between two groups. In respect of multiple group designs, subsequent to the confirmation of normal distribution of data, one-way ANOVA analysis was put to use followed by LSD post-hoc test. P < 0.05 was considered to be statistically significant.

The SPSS version 21.0 statistical software (Chicago, IL, United States) was used for clinical data analysis. After testing for data distribution and variance homogeneity, we calculated the means ± standard deviations for all measurement data, which included age, the GDDS average DQ, the ADOS social + communication score, the CARS total score, and the scores of the PCDI. Comparisons between the two groups were conducted using independent-sample t-tests. Classification data, such as sex, RJA, and IJA item scores, are expressed as numbers and were compared using χ² tests. A p-value <0.05 was considered significant.