Spss statistics for windows ver 22

All statistical analyses were performed using IBM SPSS Statistics for Windows ver. 22.0 (IBM Co., Armonk, NY, USA). Mean values of the clinical characteristics were compared between groups (fatigued and non-fatigued). Continuous data were expressed as means and standard deviations, and Student t-tests were used to compare differences. Categorical data were reported as proportions, and chi-square or Fisher's exact tests were used for comparisons. Univariate and multivariate logistic regressions were used for association analyses. All variables with P-values less than 0.05 in the univariate analyses as well as clinically significant variables were included in the multivariate logistic regression. P-values less than 0.05 were considered statistically significant.

IBM SPSS Statistics for Windows ver. 22.0 (IBM Corp, Armonk, NY, USA) was used for statistical analysis. The Mann-Whitney U-test was used to compare measured variables between groups. The chi-square and Fisher exact tests were used to analyze the relationships between categorical variables or differences between groups. Qualitative variables are presented as frequencies (percentages), while quantitative variables are presented as means (±standard deviations) or medians (minimum-to-maximum). The relationship between VAS pain scores and QoL was examined using the Spearman rank correlation test. Univariate logistic regression analysis was performed to identify risk factors for recurrence. Accordingly, variables with a significance level of 0.25 or lower were subjected to multivariate logistic regression analysis, in which the associated odds ratio, 95% confidence intervals, and P-values were calculated. A P-value of <0.05 was considered statistically significant.

Statistical analyses were carried out using IBM SPSS Statistics for Windows ver. 22.0 (IBM Corp., Armonk, NY, USA) and R software ver. 3.1.2 (R Foundation for Statistical Computing, Vienna, Austria; https://www.r-project.org). Categorical variables were compared using the chi-square test or linear by linear association. A propensity score matched analysis was performed to minimize confounding bias for oncologic outcomes between the 3 groups. Survival rates were analyzed using the Kaplan-Meier method and the log-rank test. Multivariate analyses for prognostic factors were performed using a Cox proportional hazard model. Variables that were significant P-values in univariate analysis were entered into the multivariate model. The P-values were derived from two-tailed tests and P ≤ 0.05 was considered statistically significant.

In order to assess how well the model matched the observed data, the root mean square error of approximation (RMSEA) was used. First, the model fit was tested assuming there were no covariances between unique factors. After that, the modification indices suggested by the software were used to add covariance between factors (double-headed arrows in Fig 1) one at a time, each time testing the RMSEA closeness to the value of <0.05, or at least <0.08—the threshold for accepting the model fit.^{32 ,33 (link)} Every insertion was considered plausible if it made logical sense and did not violate the assumption that the common and the unique factors are uncorrelated. After achieving the RMSEA value of <0.05, no further covariances were imputed and the goodness of fit was assessed by χ² test. As the sample was relatively small considering the requirements of CFA, in an attempt to reduce dependence on sample size, the choice was the relative (or “normed”) χ² test. Relative χ² is a χ² estimate divided by the degrees of freedom. A relative χ² value <5.0 was considered an indication of a good fit.³⁴ All analyses were conducted using IBM^® SPSS^® Statistics for Windows^®, ver 22.0 (IBM Corp. Released 2013, Armonk, NY, USA); IBM^® SPSS^® Amos™, ver 23.0 (IBM^® Corp. Released 2013, PA, USA); and Stata/IC Statistical Software, release 14 (StataCorp LP, TX, USA).