Spss statistics 22 software for windows

Continuous non-parametric variables are presented as the median, IQR, and range, whereas categorical variables are expressed as number and percentage. Mann–Whitney U test was used to compare continuous non-parametric variables. Kaplan–Meier analysis was run to determine OS and PFS [84 (link)]. OS was calculated from date of diagnosis until death from any cause. PFS was measured from date of diagnosis until radiological or clinical progression date. Specifically, for multiple lesions, the first radiological progression of one lesion was considered as the progression date, irrespective of the evolution of any others. For both OS and PFS analyses, patients were censored at last available follow-up time if no event occurred.
Factors that may influence time to progression (gender, age at tumor diagnosis, location of tumor, symptoms at diagnosis) were tested by comparing PFS curves with the log-rank test.
Exclusively for patients with histological or radiological diagnosis of LGG, log-rank test was used to compare PFS curves of patients with and without associated OPG, and those of children with single and multiple lesions. The Cox regression model was used to explore predictors of progression in patients with LGG. For all analyses, p values < 0.05 were considered statistically significant. IBM SPSS Statistics 22 Software for Windows was used for statistical analysis.

All statistical analyses were conducted using IBM SPSS Statistics 22 software for Windows (IBM Corp, New York, NY, USA), and means were compared for significance by Duncan’s multiple range method.

We compared baseline characteristics as mean with standard error or as frequency between groups using non-paired t-test or chi-squared test, respectively. Two-way analysis of variance (ANOVA), was used to determine whether differences existed between values (two levels for the time factor: pre- and post-trial) for each group (group factor: WBVT/MA and WBVT/P) according to all outcome measures. When an interaction was significant, we examined the simple effect of time and group. In addition, when interaction was not significant, we tested the main effect of time and group. We calculated 95% confidence intervals for all outcome measures pre- and post-trial. The effect sizes (Cohen’s d) between pre- and post-trial data were determined by using the average change and excluding the pre-test standard deviation. The calculated intraclass correlation coefficient (ICC) based on pre- and post-trial data was moderate or greater (0.41~0.87). The calculated coefficient of variation based on pre-test data was 0.11 to 0.36. Effect size (d) standards were 0.2 = small, 0.5 = moderate and 0.8 = large. IBM SPSS Statistics 22 software for Windows (IBM, Armonk, NY) performed all statistical processing. Statistical significance was set at P < 0.05.