Statistical software version 11

A number of patients in each group was decided as per statistician's advice with pilot study results. Both these groups were compared and statistically analyzed using Student's t-test and significance evaluated by P value. Repeated – measures analysis of variance were used to compare measurements of BP and pulse over time. P < 0.05 considered as statistically significant. Statistical analysis carried out using MedCalc Statistical software version 11.0 (MedCalc software, Buba, Belgium).

All continuous variables and patient characteristics were expressed as medians (interquartile range (IQR)) or n (%), as appropriate. Chi‐square or Fisher's exact tests were used to compare categorical data. The Mann‐Whitney U test and Kruskal–Wallis test was used to compare two or more groups. Correlations between CAP and continuous variables were assessed using Spearman correlation coefficients (ρ). Parameters that were significantly associated with CAP were subsequently entered into a multiple linear regression models (Stepwise method).
Receiver operating characteristic (ROC) curves were plotted, and areas under the curves (AUROCs) with 95% confidence interval (CI) were calculated to determine the diagnostic efficacy. The accuracy of CAP at optimal thresholds was defined by the maximum Youden index. For each optimal cut‐off value, the sensitivity, specificity, positive predictive values (PPVs) and negative predictive values (NPVs), positive likelihood ratio (PLR) and negative likelihood ratio (NLR) were calculated. The general coincidence rate was calculated as (true positive + true negative)/overall patients.
All statistical analyses were performed using spss version 16.0 for windows (SPSS, Chicago, IL, USA) and medcalc statistical software version 11.0 (MedCalc, Mariakerke, Belgium). Two‐sided P‐values <0.05 were considered statistically significant.

The MedCalc statistical software version 11.3.0.0, combined with the subject operating characteristic (ROC) curve, were used to determine the optimal cut-off value for the assessment of HCC survival outcomes with serum iron. SPSS 24.0 software was used for statistical data analysis. Continuous variables conforming to the normal distribution were expressed as mean ± standard deviation (SD) and assessed with Student’s t-test. The classified data were compared by Pearson chi-square test or Fish exact test. Correlation analysis was used to evaluate the relationship between preoperative serum iron levels and other clinicopathological indicators in HCC patients. Kaplan–Meier method and log rank test were used to analyze the OS, DFS and recurrence rate of patients. Univariate analysis and multivariate COX hazards regression model were used to determine the prognostic factors related to OS and DFS. P < 0.05 was considered statistically significant.

SPSS13.0 (SPSS Inc, Chicago, IL) and MedCalc statistical software version 11.3.0.0 (MedCalc Software, Broekstraat 52 Mariakerke, Belgium) were used in analyzing the data. The Pearson χ² test was used to compare qualitative variables. Univariate analysis was performed to determine the significance of variables using the logistic regression model for the response rate and the Cox regression model for DFS and OS. Survival curve was estimated by Kaplan-Meier analysis, and the log-rank test was used to examine the difference of survival distributions between groups. Subsequently, the variables with P < .05 were subjected to multivariate analysis. Cox proportional hazards regression model was used to determine the independent prognostic factors. A value of P < .05 was considered significant.

SPSS13.0 (SPSS Inc., Chicago, IL) and MedCalc statistical software version 11.3.0.0 (MedCalc Software, Broekstraat 52 Mariakerke, Belgium) were used for data analysis. The Pearson χ² test was used to compare qualitative variables. Univariate analysis was first performed to determine the significance of variables using the logistic regression model for the response rate and the Cox regression model for DFS and OS. Survival curves were estimated by Kaplan–Meier analysis, and the log-rank test was used to examine differences in survival distributions between groups. Subsequently, the candidate variables from the univariate analysis with P < 0.05 were subjected to multivariate analysis. Cox proportional hazards regression model was used to determine the independent prognostic factors. A value of P < 0.05 was considered statistically significant.