Statistical package for social science version 19

All statistical analyses were performed using Statistical Package for Social Science version 19.0 (SPSS, Inc., Chicago, IL). Normally distributed data were expressed as means ± SD, and skewed data were expressed as medians with the interquartile range. Intergroup comparisons of clinical values were performed using unpaired Student’s t test, and skewed data were log-transformed before analyses. The chi-square test was performed for intergroup comparisons of categorical variables. Partial correlation analysis was used to examine the correlation between FGF21 levels and clinical parameters, and multivariate logistic regression analysis was used to examine the independent factors of CAD occurrence. The variables input into the multiple logistic regression were variables that differed significantly between patients with and without AMI, and the diagnostic parameters were not included. All P values were two-tailed and P < 0.05 was considered statistically significant.

All data are shown as odds ratios (OR) and corresponding 95% confidence intervals (CIs). Statistical analyses were carried out using the Statistical Package for Social Science version 19.0 (SPSS Inc., Chicago, Illinois, USA) and two-tailed P-values <0.05 were considered to be statistically significant.
Differences of IQ scores at various time points within each group and among the 4 groups were analyzed by Friedman and Kruskal-Wallis tests, respectively. Multiple comparisons within each group and between the 4 groups were performed using a Dunn test. Multivariable logistic regression analysis was used to study the association between independent variables (confounding variables such as gender, gestational age, age of exposure to general anesthesia and a mother’s education level) and the dependent variable (IQ scores of children).

Processed images were analyzed within the framework of the General Linear Model. Several t-test analyses were performed to investigate GMV differences between healthy controls and both insight psychosis patients groups using pairwise contrasts. Age at scan, gender and total intracranial volume were entered as covariates of no interest in the statistical design in order to regress out possible effects of these parameters on between-group volume differences. First, a primary cluster-forming voxel-level threshold of p<0.01 (uncorrected) was applied. Then, a cluster-level inference strategy was employed by evaluating obtained clusters at a cluster-extent threshold of p<0.05 family-wise error (FWE) corrected. All clusters sizes were adjusted for smoothness non-uniformity by means of the VBM5 toolbox[39 (link)]. Anatomical regions covered by significant clusters were identified using automated anatomical labeling[40 (link)].Pearson’s chi-square for categorical data and Student’s t-tests for continuous variables were used to evaluate differences in sociodemographic characteristics between controls and patients. The Statistical Package for Social Science, version 19.0 (SPSS Inc.,Chicago, IL, USA), was used for these analysis.

Data were statistically analyzed using the Statistical Package for Social Science version 19.0 (SPSS Inc., IL, USA). Continuous variables with a normal distribution were expressed as mean ± standard deviation, and the difference in data between the two groups was analyzed using the independent-samples t test. Continuous variables with skewed distribution were presented as median (25th to 75th percentiles), and the difference in data between the two groups was analyzed using the Mann–Whitney U rank-sum test. Categorical variables were presented as a percentage and analyzed using the chi-square test.
The associations of APO with each of the metabolic parameters, inflammatory markers, anthropometrics, and lifestyle habits (quantitative data expressed as quartiles and qualitative variables expressed as “yes” or “no”) were evaluated by the univariate logistic regression analysis. They were expressed as the odds ratio (OR) and its 95% confidence interval (95% CI). Variables with a P value < 0.25 in the univariate analysis were retained for further multivariate logistic regression analysis. The adjusted odds ratio (aOR) and its 95% CI were calculated by the multivariate logistic regression analysis (stepwise forward Wald method) to select the variables independently associated with APO. All P values were two-tailed, and a P value < 0.05 was considered statistically significant.

Mean, median, and interquartile range (IQR) were generated for continuously coded variables. Frequencies and proportions were generated for categorical variables. The categorical data between the two groups were compared with chi-square test and Fisher exact test as appropriate. Comparisons between continuous variables were done by Student's t-test. One-way ANOVA rated the difference of age between years and disease occurrences between seasons. Subsequently, binary unconditional logistic regression models were fitted to test the effect of age, gender, and CCI on the mortality. Statistical significance was inferred at a 2-sided P value of <0.05. All statistical analyses were carried out using the Statistical Package for Social Science, version 19.0 (SPSS, Chicago, IL).

All the data are presented as the mean ± standard deviation. The F-test was used for significance testing, and P<0.05 was considered to be statistically significant. All tests were performed using the Statistical Package for Social Science version 19.0 (SPSS Inc., Chicago, IL, USA).