Spss 24.0 for mac

We performed a secondary analysis of the 2-year diet intervention (18 (link)). The primary outcome of the study was the change in fat mass over a period of 2 years. Power calculation based on this objective indicated that 35 subjects were needed in each intervention arm to achieve a significant outcome (P < 0.05) with 80% power. In a previous study of 20 obese individuals, weight loss altered postprandial GLP-1 and GIP levels (14 (link)), indicating that we should have enough power for the secondary analysis described in this paper, despite the relatively high loss-to-follow-up rate. Randomisation of this single-centre, parallel group trial was performed by a statistician blinded to the study, using a block size of four and a 1:1 allocation ratio. Baseline values and treatment effects (change from baseline to 6 months and from baseline to 24 months) were compared between groups using the Mann–Whitney U test. Within-group changes over time from baseline to 6 months and from baseline to 24 months were analysed using the Wilcoxon signed-rank test. Correlation analyses were performed by calculating Spearman’s rho (r_S). A two-sided P value of <0.05 was considered statistically significant. Statistical analyses were performed using SPSS 24.0 for Mac (IBM Corp). Data are presented as mean ± s.e.m. unless otherwise stated.

Values are presented as mean values with standard deviation (SD) or total numbers or percentages (%). To compare mean serum albumin levels with clinico-pathological parameters Students’ T tests and one-way ANOVA tests were performed. p values of < 0.05 were considered statistically significant. To evaluate the independent risk factors for complication (CDC 1–5) binary logistic regression was performed, using all well-established parameters. To rule out a potential bias regarding prognostic value of hypoalbuminemia on OS patients, who died within 30 days after surgery, were excluded from this survival analysis. With respect to overall survival, differences between groups were tested using the log-rank test and presented as Kaplan–Meier survival curves. Multivariable analysis was performed using a Cox regression model including as independent variables serum albumin levels (dichotomized at 35.0 mg/dl), patients’ age (dichotomized at the median value of 68.2 years), tumor stage (FIGO III and IV vs. FIGO II vs. FIGO I), tumor grade (G3 vs. G2 vs. G1) and histology (squamous cell carcinoma vs. others) as independent variables. Statistical analyses were performed using the statistical software SPSS 24.0 for MAC (SPSS 24.0, IBM Inc., Armonk, NY).

All data are given either as the mean (SD) or median (range). Patients were classified according to the 2009 International Federation of Gynecology and Obstetrics (FIGO) classification system [28 (link)] and the 4th edition of the WHO Classification of Tumors of Female Reproductive Organs [19 ]. Chi-squared tests were performed to compare the stratification systems. A Sankey diagram was generated to illustrate shifts of patients between risk groups of different classification systems using power-user add-in for MS Excel (Power User Software, Paris, France). p-values of 0.05 were considered to be statistically significant. Statistical analysis was performed using the Statistical Package for the Social Sciences statistical software (SPSS 24.0 for MAC, IBMCorp., Armonk, NY, USA).

Statistical analysis was performed using SPSS 24.0 for Mac (SPSS Inc., Chicago, IL, USA). The data are presented as mean ± standard deviation or median and range. For comparison between groups, the Mann–Whitney test or Student’s t test were employed where appropriate. p < 0.05 was considered statistically significant.

SPSS 24.0 for Mac (SPSS, Inc.) was used for data analysis. Results were represented as mean ± SD and evaluated by using the two-tailed unpaired Student’s t-test or one-way analysis of variance. The p-value < 0.05 was considered to be significant.

Descriptive data were presented as the mean and standard deviations (SD), and as percentages with 95% confidence intervals (95% CI). Statistical methods applied were χ² statistics and the significance level was set to 5%. Injury incidences were calculated as injuries per 1000 h of training and injuries per 1000 h of game play. For the calculation of game injury incidence five games were considered at most in order to reduce recall bias. Following Knowles et al. [30 (link)], 95% CI were provided for injury incidences and were calculated as: $\mathrm{Incidence}\mathrm{rate}\pm 1.96\times \sqrt{\left(\mathrm{number}\mathrm{of}\mathrm{injuries}\right)}÷\left(\mathrm{person}\text{-}\mathrm{time}\mathrm{at}\mathrm{risk}\right)$
Moderate overlap between the bars of the 95% CI (no more than half of each bar) was the criterion for statistically significant differences of injury incidences at a p-value of 0.05 [31 ]. The statistical analysis was performed using SPSS 24.0 for Mac (SPSS, Chicago, Illinois, USA) and Excel 2001 for Mac (Microsoft, Redmond, WA, USA).