Spss 22.0 program

The Kolmogorov-Smirnov test was used to examine the normality of the quantitative variables that were expressed as the mean ± standard deviation. The differences between groups were analyzed by variance analysis adjusted for age and sex. Non-parametric tests (Mann-Whitney) were used when the variables to be analyzed did not follow a normal distribution. The qualitative variables were expressed as proportions, whose differences between groups were analyzed using the Chi-square test. The associations of variables were evaluated by estimating the Pearson or Spearman correlation coefficient (according to normality). For all calculations, a p probability of less than 0.05 significant for two tails was considered significant. The data analysis was performed with the SPSS 22.0 program (SPSS Inc., Chicago, IL, 2013).

Mean, standard deviation, median lowest, highest, frequency and ratio values were used in descriptive statistics of the data. The distribution of the variables was measured with the Kolmogorov-Smirnov test. The Mann-Whitney test was used to analyze quantitative independent data. The Chi-square test was used for the analysis of qualitative independent data and the Fisher test was used when the chi-square test conditions were not met. SPSS 22.0 program was used in the analysis.

Factorial analysis of variance was performed to investigate the effects of additives, ensiling duration, and their interactions on the fermentation quality, microbial counts, bacterial community indices, nitrate, and N₂O content of silage in the General Line Model of SPSS (SPSS 22.0 program, SPSS Inc., Chicago, Illinois, United States). Significant differences were compared using Tukey multiple range tests, and p < 0.05 indicated statistical significance.

Fisher’s exact or chi-squared tests were used to assess the associations between clinical parameters and afatinib efficacy. The Kaplan–Meier method and the log-rank test were used to analyze the association of clinical parameters with PFS, and the associated 95% CIs were calculated. Multivariate analysis was performed using logistic regression models and Cox proportional hazards models to assess the simultaneous effects of prognostic factors on efficacy and survival. The analyses were performed with SPSS 22.0 program (SPSS Inc, Chicago, IL, USA), a two-sided p-value less than 0.05 was considered statistical significance.

The selection of cut-off values of baseline PET/CT parameters was determined using receiver operating characteristics (ROC) curve analysis. Analysis of the AUCs of the ROC curves was done using Delong's test to compare the performance of PET/CT parameters for predictive response. Univariate and multivariate logistic regression analyses were used to determine the clinical tumor response predictors. OS was defined as the interval between the treatment and death or the last follow-up. The Kaplan-Meier method and the log-rank test were used to analyze the association of each marker with OS, and the associated 95% CIs were calculated. Cox's proportional hazards models were used to perform multivariate analysis defining the independent prognostic factors for OS, and hazard ratios were reported as relative risks with corresponding 95% confidence intervals. The analyses were performed with the SPSS 22.0 program (SPSS Inc., Chicago, IL, USA) and the MedCalc program (Version 18.11). A two-sided p-value < 0.05 was considered statistically significant.