Treatment survival outcomes including, OS, PFS, LRFS, and DMFS were calculated using nonparametric statistics, namely Kaplan–Meier survival analysis and compared with log-rank and Wilcoxon tests. We used the log-rank test to compare survival curves. Univariate analyses were performed using Kaplan–Meier survival analysis and then selected into multivariate analysis to determine the possible prognostic risk factors that were associated with treatment efficiency. Chi-square and/ or Fisher’s exact tests were used to examine whether T stage, N stage, clinical stage and groups were independent based on the patients’ data. In all the statistical tests, P values less than 0.05 between each group were deemed to show a statistically significant difference. All analyses were performed by using by SPSS Statistics 19.0 for Windows (SPSS, Chicago, IL).
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SPSS Statistics 19.0 for Windows is an industry-leading statistical software package designed for data analysis and management. It provides a comprehensive set of tools for data manipulation, statistical modeling, and reporting. The software is widely used across various industries and academic institutions for a broad range of applications.
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43 protocols using spss statistics 19.0 for windows
1
Survival Analysis of Treatment Outcomes
2
Golde Score Analysis in Clinical Data
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Continuous variables are expressed as median (interquartile range) and frequencies for categorical variables. The Golde score was analyzed by the Kruskal-Wallis One-Way analysis of variance, the Spearman's coefficient of correlation was used to assess inter-observer variation in the analysis of the Golde score. Clinical data was analyzed by the χ2 test for categorical and by the Kruskal-Wallis One-Way analysis for continuous data. The level of significance was defined as a two-tailed P value <0.05. All statistical analyses were performed with SPSS Statistics 19.0 for Windows (SPSS, Inc. 2010).
3
Statistical Analysis of Psychological Variables
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Continuous variables were compared using the Student’s t test, while discrete variables were compared using the chi-square test. Data are represented as percentages for nominal variables and mean (SDs) for continuous variables. To compare groups on psychological variables, ANOVAs (analysis of variance) were performed. If group differences were observed, the Tukey-Kramer post-hoc test for unequal group sizes was used to identify which groups differed significantly. Descriptive data were coded and analyzed using “frequencies”. A P<.05 indicated statistical significance. All tests were two-tailed. Data were analyzed using SPSS Statistics 19.0 for Windows.
4
Differentiating Recurrent Tumor and Post-Treatment Changes
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Statistical analysis was performed with SPSS Statistics 19.0 for Windows. For all statistical analyses, a 2-tailed P value of 0.05 was considered to indicate a statistically significant difference. The 2-tailed independent Student's t-test was used to compare mean ADC1000, ADC2000, and ADCratio values between the group with recurrent tumor and the group with post-treatment changes.
Student's t-test was also performed in order to compare mean ADC2000 values decreasing percentages versus ADC1000 ones between recurrent tumor group and posttreatment changes one.
A ROC (receiver operating characteristic) curve was drawn to investigate the optimal cutoff values for ADC1000, ADC2000, and ADCratio in order to obtain the best sensitivity, specificity, and accuracy in distinguishing between recurrent tumor and posttreatment changes.
Student's t-test was also performed in order to compare mean ADC2000 values decreasing percentages versus ADC1000 ones between recurrent tumor group and posttreatment changes one.
A ROC (receiver operating characteristic) curve was drawn to investigate the optimal cutoff values for ADC1000, ADC2000, and ADCratio in order to obtain the best sensitivity, specificity, and accuracy in distinguishing between recurrent tumor and posttreatment changes.
5
Cytokine Profiling in Fungal Sinusitis
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Statistical analyses were performed using SPSS Statistics 19.0 for Windows (SPSS Inc., Chicago, IL, USA). The chi-square test or t test was used for the comparison of clinical characteristics between FS patients and the controls. The Mann‒Whitney U test was used to compare serum cytokine levels and laboratory findings between FS patients and controls. Spearman’s rank correlation coefficient was calculated to detect significant correlations between cytokine levels. GraphPad Prism v.7.0 (GraphPad Software Inc., San Diego, CA, USA) was used to perform the above tests. Values are expressed as means. Statistical significance was defined as P < 0.05.
6
Comparative Analysis of CSTB Expression
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All analyses were performed with SPSS Statistics 19.0 for Windows (SPSS, Chicago, IL, USA). For comparison between two groups of positivity and correlation between CSTB expression and histological types or the clinicopathological characteristics, a Fisher’s exact test was performed. For comparison between the two scoring groups of immunostaining, a Wilcoxon rank-sum test was applied. For comparison between two groups in the treatment experiments, a Student’s t-test was used. Results are presented as the mean ± standard error of mean (SEM). Significant difference was considered at the value of P<0.05.
7
CSF Protein Concentration Norms Across Ages
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Statistical analysis was performed using the SPSS Statistics 19.0 for Windows (SPSS Inc, Chicago, IL) program. Categorical data were described using frequencies and percentages, and continuous variables were described by using mean, median, interquartile range (IQR), and 90th and 95th percentile values. Linear regression was used to determine the associations between age and CSF protein concentrations. Because the relationship between CSF protein values and age appeared to be nonlinear for children aged <36 months, exponential curve analysis was performed using logarithmically transformed CSF protein values as the dependent variable in group 2 (Table 1 ). Figures are created using SPSS. The distribution of residuals is normal. Kruskal-Wallis H tests were subsequently used to compare the distribution of CSF protein concentrations amongst the predefined age categories to facilitate implementation of our results into clinical practice: group 1 (0–14, 15–28, 29–42, and 43–56 days) and group 2 (2–<6, –<12, 12–<18, 18–<24, and 24–<36 months). Two months also include 57, 58, 59, and 60 days.
8
Evaluating Medicine Ball Throw Test
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All data were checked for normality and expressed as mean values and SDs. The sphericity was checked using the Mauchly test. A 1-way repeated-measures analysis of variance (ANOVA) and intraclass correlation coefficients (ICCs) were conducted to determine differences between medicine ball throws attempts and the reliability of the medicine ball throw test. Participants' times to 50-m freestyle were analyzed as a whole and divided into male and female groups, as the effect of PAP may be influenced by gender ( 20 ). A repeatedmeasures ANOVA was then used to identify potential differences in the 50-m freestyle test performance, for the participants under each of the warm-up protocols in both gender-and non-gender-specific groupings. Post hoc Bonferroni test determined which measures differed significantly pairwise. Significance was set at p ≤ 0.05 for all statistical tests and SPSS Statistics 19.0 for Windows was used to analyze data.
9
Comparative Analysis of Intellectual Disability
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Descriptive analysis, Pearson's chi-squared tests and Student's t-tests were used for comparison of dichotomic and continuous variables (sex, age, severity of IDD, presence of chronic diseases, diagnosis of diseases with medical certification, and self-reported health) between both groups (homes vs. residences).
All statistical analyses were performed using SPSS statistics 19.0 for Windows, and the significance level was considered at p<0.05.
All statistical analyses were performed using SPSS statistics 19.0 for Windows, and the significance level was considered at p<0.05.
10
Evaluation of Cell Viability Assay
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Data are the mean ± SEM (standard error of the mean) of three independent experiments (* p < 0.05) compared with control (Statistical analysis was performed using IBM SPSS Statistics 19.0 for Windows (Chicago, IL, USA). Differences were determined using Student’s t-test.
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