Data from continuous variables are presented as mean value ± standard deviation or median (interquartile range, IQR) in the case of nonparametric distribution. Categorical variables are presented as count with percentage in parentheses. Associations between categorical variables were analyzed using Fisher's exact test. Comparisons of quantitative data across groups were performed with Mann-Whitney/T-test or Kruskal-Wallis/ ANOVA tests depending on the number of groups and the normality of the data. Differences between study groups, e.g., diabetic vs. non-diabetic subjects, regarding the levels of eicosanoids were evaluated by logistic regression, including in the models relevant covariates, namely age, sex, weight, hypertension, hyperlipidemia and diabetes (the latter only in the analysis of associations with eGFR and proteinuria in the entire cohort). One outlier in Figure 2 and two outliers in Figures 4 and 5 were excluded from the figures in order to improve the visibility of the charts. The threshold for outliers was determined by multiplying the IQR by 1.5 and adding the result to the third quartile. Statistical analyses were carried out using IBM SPSS Statistics package v.22 (IBM Corporation, Armonk, NY, USA). A p-value <0.05 was considered statistically significant.
Spss statistics package v 22
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SPSS Statistics package v.22 is a comprehensive software suite for statistical analysis. It provides a wide range of data management, analysis, and reporting capabilities. The core function of SPSS Statistics is to enable users to analyze and interpret data, identify trends, and make informed decisions.
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Lab products found in correlation
6 protocols using spss statistics package v 22
1
Eicosanoid Levels in Diabetic Cohort
2
Epidemiological Analysis of Tinnitus
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General information on the sample size, age, sex, tinnitus prevalence, severe tinnitus distribution, and SNHL was recorded. Qualitative variables are presented as relative frequencies to compare them among studies and to obtain average values. Quantitative variables are expressed as mean ± standard deviations (SD). All statistical analyses were performed using SPSS Statistics package v22 (IBM, Armonk, NY).
3
Cognitive Characteristics by Gender and Occupation
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The statistical analysis was performed with the IBM SPSS Statistics Package, v.22. (SPSS Inc., Chicago, IL, USA). The descriptive statistics are shown according to the nature of each variable: mean (m) and standard deviation (SD) or the number of participants in each category (n) and the proportion of patients in relation to the total (%). The normality of the variables was verified by Kolmogorov–Smirnov test.
To analyze cognitive characteristics for each gender, the non-parametric Mann–Whitney U-test was used. To measure cognitive characteristics according to the mental occupation state, Kruskal–Wallis H-test was applied. The level of significance was set at 5%.
To analyze cognitive characteristics for each gender, the non-parametric Mann–Whitney U-test was used. To measure cognitive characteristics according to the mental occupation state, Kruskal–Wallis H-test was applied. The level of significance was set at 5%.
4
Statistical Analysis of Cognitive Measures
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The statistical analysis was performed with the IBM SPSS Statistics Package,
v.22. The descriptive statistics are shown according to the nature of each
variable. For the quantitative variables, the mean ( ), SD, and 95% confidence interval level
were used for the population mean. Due to the non-symmetry of some of these
variables, we also included the median (Me), the first (Q1) and third (Q3)
quartile and the extreme points (Table 2 ). For qualitative variables, the number of participants in
each category (n) and the proportion of patients over the total (%) were
considered. The Kolmogorov-Smirnov test was used to verify the normality of the
quantitative variables. Most of them are non-normal distributions.
The Pearson Chi-square test was used to determine associations between
qualitative variables. Differences between groups in the cognitive measurements
were evaluated using the non-parametric Mann-Whitney U test. In addition,
Spearman correlation coefficients were calculated between the cognitive
measurements and the ANOVA analysis was used for predictive multiple linear
regression models.
v.22. The descriptive statistics are shown according to the nature of each
variable. For the quantitative variables, the mean ( ), SD, and 95% confidence interval level
were used for the population mean. Due to the non-symmetry of some of these
variables, we also included the median (Me), the first (Q1) and third (Q3)
quartile and the extreme points (
each category (n) and the proportion of patients over the total (%) were
considered. The Kolmogorov-Smirnov test was used to verify the normality of the
quantitative variables. Most of them are non-normal distributions.
The Pearson Chi-square test was used to determine associations between
qualitative variables. Differences between groups in the cognitive measurements
were evaluated using the non-parametric Mann-Whitney U test. In addition,
Spearman correlation coefficients were calculated between the cognitive
measurements and the ANOVA analysis was used for predictive multiple linear
regression models.
5
Statistical Analysis of GIST Survival
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The frequency tables were analysed with the χ2 test or Fisher's exact test. The groups resulting from the TUNEL analysis were compared using the Mann–Whitney U-test. Cumulative survival was estimated with the Kaplan–Meier method, and survival between groups was compared using a univariable Cox proportional hazards model. The interaction between tumour SLUG expression and imatinib treatment in the SSGXVIII/AIO series was calculated using a Gaussian process model for conditional event probabilities. Gaussian process model can model nonlinear effects of the covariates and interactions between covariates, and thus allow inspection of whether SLUG has an interaction effect with imatinib treatment or other covariates (Joensuu et al, 2012b (link)). Overall survival was calculated from the date of surgery to the date of death, censoring patients who were alive on the last follow-up date. Recurrence-free survival (RFS) was calculated from the date of randomisation to the date of GIST recurrence or to death, whenever death preceded recurrence, censoring the patients alive on the date of last follow-up. The P values are two-sided. The statistical calculations were done using the IBM SPSS Statistics package v. 22.0, or with GPstuff 4.6 (Vanhatalo et al, 2013 ).
6
Survival Analysis of GIST Patients
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The frequency tables were analysed with the chi‐squared test. Continuous parameters were compared with the Mann–Whitney test or the Kruskal–Wallis test. Cumulative survival was estimated with the Kaplan–Meier method and the log‐rank test. Overall survival was calculated from the date of the GIST diagnosis to the date of death, censoring patients who were alive on the last date of follow‐up. GIST‐specific survival was computed from the date of the diagnosis to death deemed to result from GIST, censoring patients who died from another cause on the date of death and patients who were alive on the last date of follow‐up. All P‐values are two‐sided. The inter‐rater agreement in immunohistochemical scoring of ITGA4 between two independent raters (O.P.P. and O.T.), and the intra‐rater agreement were measured by computing Cohen's kappa coefficient. The statistical calculations were carried out using an SPSS Statistics package v. 22.0 (IBM, Armonk, NY, USA).
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