The Fisher’s exact test was used to examine the associations among categorical variables. The Mantel–Haenszel test was used for ordinal variables, and the independent 2 sample t test was used for the non-categorical variables. A P-value < 0.05 was considered statistically significant. The Statistical Package for the Social Sciences version 12.0 (IBM Corp, Armonk, NY, USA) and Statistical Analysis System version 9.4 (SAS Institute, Cary, NC, USA) were used for all analyses.
Statistical package for the social sciences version 12
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Statistical Package for the Social Sciences (SPSS) version 12.0 is a comprehensive software package for statistical analysis. It provides a wide range of tools for data manipulation, analysis, and visualization. SPSS 12.0 is designed to handle a variety of data types and supports a range of statistical techniques, including descriptive statistics, correlations, regression analysis, and more.
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28 protocols using statistical package for the social sciences version 12
1
Statistical Analysis Techniques for Categorical and Continuous Variables
2
MAP1B Expression Analysis in Urinary Tract Cancers
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The Statistical Package for the Social Sciences version 12.0 software program (IBM Corp., Armonk, NY, USA) was used for all statistical analyses. Differences between categorical parameters were assessed using the chi-squared or Fisher’s exact test. The median H scores of MAP1B immunoreactivity were used as cutoff values to separate UTUC and UBUC into two subgroups of high and low MAP1B expression. Pearson’s chi-squared test was used to compare the association between MAP1B expression and clinicopathological parameters. The Kaplan–Meier method was applied to estimate the effect of MAP1B expression on DSS and MFS. The survival curves were compared using the log-rank test. We used a Cox proportional-hazards model to identify independent predictors for DSS and MFS. In all figure legend, continuous parameters (such as MAP1B transcript expression in Figure 1 , mitotic activity in Figure 2 , MAP1B mRNA expression, relative proliferation, migration and invasion in Figure 4 , apoptosis rate in Figure 6 ) were assessed using a t-test or Mann–Whitney–Wilcoxon test. Survival analysis (DSS and MFS) were performed using Kaplan-Meier plots and compared by the log-rank test. Statistical significance was set at p < 0.05.
3
Genetic Factors in Liver Cirrhosis
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The obtained data were analyzed using the Statistical Package for the Social Sciences version 12 software (SPSS Inc., Chicago, IL, USA). Mean ± standard error (SE) was used to describe continuous variables, while percentages and frequencies were used to describe categorical variables. One way repeated measure analysis of variance (ANOVA) followed by post Hoc test Scheffe’s method were used to compare the continuous variables of the groups. The differences in the frequency of A1AT and IL-6 genotypes were analyzed by the c2 test. The unpaired t-test was used to compare the continuous variables between different A1AT and IL6 genotypes.
Multiple logistic regression analysis was performed to evaluate the independent associations between liver cirrhosis and the polymorphic variants of the studied SNPs at both A1AT and IL-6 genes in addition to the other variables that may affect liver cirrhosis such as serum level of ALT, AST, ALP, total and direct bilirubin, albumin, prothrombin time and AFP. For this analysis, patients were divided as cases with absence of cirrhosis vs. cases with presence of cirrhosis (fibrosis score F0-F3 vs. F4 respectively). Odds ratio (OR), 95% confidence interval (95% CI) and p values were calculated with the SPSS Inc. software. A p value of <0.05 was considered statistically significant.
Multiple logistic regression analysis was performed to evaluate the independent associations between liver cirrhosis and the polymorphic variants of the studied SNPs at both A1AT and IL-6 genes in addition to the other variables that may affect liver cirrhosis such as serum level of ALT, AST, ALP, total and direct bilirubin, albumin, prothrombin time and AFP. For this analysis, patients were divided as cases with absence of cirrhosis vs. cases with presence of cirrhosis (fibrosis score F0-F3 vs. F4 respectively). Odds ratio (OR), 95% confidence interval (95% CI) and p values were calculated with the SPSS Inc. software. A p value of <0.05 was considered statistically significant.
4
BDNF Levels and Cognitive Function
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The data are presented as the mean ± standard deviation. Baseline characteristics were compared across groups using a two-samples t-test (or the Mann–Whitney U test for data with a non-normal distribution). The sex distribution was compared across the groups using Pearson’s χ2 test. Correlations between serum BDNF levels and clinical variables were assessed using bivariate Pearson and Spearman correlation coefficients. Changes in MMSE scores, TPC scores, Digit Span Test scores, VFT scores, and serum BDNF levels were compared between the two groups using the two-samples t-test (or the Mann-Whitney U test for data with a non-normal distribution). The statistical significance of the results was set at P<0.05. All statistical analyses were carried out using Statistical Package for the Social Sciences version 12 software (SPSS Inc., Chicago, IL, USA).
5
Comparative Cost Analysis of Interventions
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Accepting an alpha risk of 0.05 and a beta risk of 0.20 in a bilateral contrast, 36 patients would be required in each group to detect a difference of 1,000 € or more. The variability of cost, according to our institution’s economic research department, was 1,423 €. A loss to follow-up of 10% was assumed.
Data were expressed as the mean ± standard deviation for continuous variables and as percentages and frequencies for categorical variables. The Student’s t-test was performed to assess differences between two means. The chi-squared or Fisher’s exact tests were used to evaluate the degree of association of categorical variables. A P-value <0.05 was considered to be statistically significant. The statistical analysis was done with Statistical Package for the Social Sciences version 12 software (SPSS Inc., Chicago, IL, USA).
Data were expressed as the mean ± standard deviation for continuous variables and as percentages and frequencies for categorical variables. The Student’s t-test was performed to assess differences between two means. The chi-squared or Fisher’s exact tests were used to evaluate the degree of association of categorical variables. A P-value <0.05 was considered to be statistically significant. The statistical analysis was done with Statistical Package for the Social Sciences version 12 software (SPSS Inc., Chicago, IL, USA).
6
Multivariate Analysis for Diagnostic Discrimination
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The Kolmogorov Smirnov test was used to evaluate the normality of the groups. The unpaired student T test was applied to compare between each two groups. The p values were then adjusted for the multiple hypotheses problem. A multivariate logistic regression analysis was applied in a stepwise forward mode in order to single out independent morphometric variables that were significantly associated with the diagnostic groups. Using the coefficients of regression obtained from the multivariate analysis and the independent variable values, discriminant scores (DS) were calculated. Best cutoff point in the DS for differentiating between the groups, were found using a Receiver Operating Curve (ROC) analysis. Two-tailed p values ≤ 0.05 will consider to be statistically significant. A leave one out method was also used for cross-validation of the multivariate model. The statistical analysis was performed using the Statistical Package for the Social Sciences version 12 (SPSS, Inc., Chicago, Illinois, U.S.A.).
7
Calculating Sample Size for Study
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The sample size was calculated to be 190 participants with a power of 80% and a level of significance of 0.05. Statistical Package for the Social Sciences Version 12 (SPSS, Chicago, IL, USA) was used for data analysis. The chi square and t-test were performed. The significance level was set at α=0.05.
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The sample size was calculated to be 190 participants with a power of 80% and a level of significance of 0.05. Statistical Package for the Social Sciences Version 12 (SPSS, Chicago, IL, USA) was used for data analysis. The chi square and t-test were performed. The significance level was set at α=0.05.
8
Nesfatin-1 Regulation in Depression
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All of the statistical analyses were performed using Statistical Package for the Social Sciences version 12.0.1 (SPSS Inc., Chicago, IL, USA). Data are expressed as the mean ± standard error of the mean, and P<0.05 was considered statistically significant. A one-sample Kolmogorov–Smirnov test showed a normal distribution of the continuous variables (age, BMI, Hamilton Depression Rating Scale [HAM-D] scores, and the concentrations of Nesfatin-1, corticosterone, TSH, fT3, and fT4) in both the patient group and the control group. A Student’s t-test was used to evaluate differences between the groups (age, BMI, HAM-D scores, and the concentrations of Nesfatin-1, corticosterone, TSH, fT3, and fT4). To analyze the sex difference between groups, the χ2 test was used. The correlation analyses were performed using a Pearson correlation test.
9
Statistical Analysis of Treatment Outcomes
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The data were analyzed using Statistical Package for the Social Sciences version 12 software (SPSS Inc, Chicago, IL, USA). Paired quantitative variables were compared before and after using the paired-samples t-test. The three independent treatment groups were compared by one-way analysis of variance, Tukey’s test for post hoc analysis, and analysis of covariance for adjustment for covariates. The Chi-squared/Fisher’s Exact tests were used for analysis of categorical variables.
10
Comparative Statistical Analysis of Variables
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All data have been presented as means ± standard deviations (SD). The Student's t-test was employed for comparison, and all analyses were performed using the Statistical Package for the Social Sciences version 12.0 (SPSS, Inc.). Differences between the variables were considered significant for p-values of > 0.05.
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