All statistical analyses were performed with SPSS (Windows version 13.0, SPSS Inc., Chicago, IL, USA). The Spearman correlation test was used to determine correlations between the expression of biological markers in the primary tumor and the expression of these markers in the lymph nodes. The Chi-squared test or Fisher's exact probability test was used to compare the recurrence rates. Logistic regression analysis was conducted on the parameters found to be significantly associated with recurrence by the Chi-squared tests or Fisher's exact probability test (P < 0.05) to identify the independent factors of recurrence. Survival rates were calculated by the Kaplan-Meier method and compared by the log-rank test. Stepwise forward Cox regression model was conducted for parameters found to be significantly associated with survival by the log-rank test (P < 0.05) in order to identify the independent factors of survival. Values of P < 0.05 were considered significant in all analyses.
Spss windows version 13
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SPSS (Windows version 13.0) is a statistical software package designed for data analysis. The core function of SPSS is to provide users with tools for data manipulation, analysis, and visualization. The software supports a range of statistical techniques, including descriptive statistics, regression analysis, and multivariate analysis.
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12 protocols using spss windows version 13
1
Multivariate Analysis of Tumor Markers
2
Neurophysiological Effects of Treatment
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All statistical analyses were performed using SPSS (windows version 13.0). Experimental data (amplitude of mEPSCs and % spontaneous alternation performance were analyzed using one-way ANOVA and experimental data of water maze (swimming path length and swimming speed) were analyzed using two-way repeated ANOVA. As appropriate Tukey's post hoc analyses were utilized to determine differences between groups. All values were presented as mean ± standard error. Differences were considered significant at P < 0.05.
3
Comparative Statistical Analysis of Data
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All statistical analyses were made with SPSS (Windows version 13.0, SPSS Inc., Chicago, IL, USA). Continuous variables were compared by Mann-Whitney U tests, and categorical data were compared by χ2 tests. A p value of less than 0.05 was considered significant.
4
Association of Serum Vitamin D Levels in JIA
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Analyses were performed using the software program SPSS (Windows Version 13.0, SPSS Inc., Chicago, IL). Descriptive statistics were used to assess the demographic variables and characteristics of disease activity. A linear regression was used to analyze the association between serum 25(OH)D levels and any variables that could influence serum 25(OH)D levels, including age, BMI, duration of JIA, JIA subtype, CHAQ, Patient global health, Tender joints, Swollen joints, DAS28, ESR, and BASDAI and Medications used. DAS28 and its components (except swollen joints) were included in multivariate analysis. P values ≤ 0.05 were considered significant.
5
Statistical Analysis of Categorical and Continuous Parameters
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Chi-square test was used to compare categorical parameters between the groups, while t-test was used to compare continuous parameters. All the statistical analysis were performed using SPSS (Windows version 13.0; SPSS Inc., Chicago, IL, USA). The p-value<0.05 was considered statistically significant.
6
Statistical Analysis of Biological Data
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All data were analyzed using SPSS (Windows version 13.0; SPSS Inc., Chicago, IL, USA). Values are expressed as the mean ± SEM. Statistical differences among the groups were assessed by one-way ANOVA. All analyses were performed using GraphPad Prism six for Windows (GraphPad Software, Inc., La Jolla, CA, USA). In all tests, P-values of 0.05 or less were considered to indicate significance.
7
Robust Statistical Analysis of Biological Data
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All values were expressed as mean± standard error (SE) of mean and the statistical significance between treated and control groups was analyzed by means of a Student’s t-test. P < 0.05 was considered significant.
The data presented was expressed as means±SE. The data was analyzed for differences between means using one-way analysis of variance (ANOVA). Duncan’s multiple-range test was used as a post hoc comparison for statistical significance (p values < 0.05). All statistical analyses were performed using the SPSS windows version 13.0 (SPSS Inc. USA).
The data presented was expressed as means±SE. The data was analyzed for differences between means using one-way analysis of variance (ANOVA). Duncan’s multiple-range test was used as a post hoc comparison for statistical significance (p values < 0.05). All statistical analyses were performed using the SPSS windows version 13.0 (SPSS Inc. USA).
8
Correlating MHC-I and DRP-1 Phosphorylation
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All data are expressed as the mean ± standard error of mean (s.e.m.). All statistical analyses were performed in SPSS Windows version 13.0. Spearman correlation analysis was used to assess the relationship between MHC-I and pSer616 DRP-1 expression. Kaplan–Meier survival curves were plotted and log-rank tests were performed. All experiments were performed at least in triplicates and the exact numbers of independent experiments with similar results are indicated in the figure legends. All statistical analyses of experiments were performed with two-tailed Student’s t-tests unless otherwise stated. p < 0.05 was considered statistically significant.
9
Predicting Alcohol Craving Relapse
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Descriptive statistics were used to analyze characteristics of the sample (eg, age, sex, education, polysubstance dependence). The temptation to drink scale was dichotomized considering the median of the scores distribution, to obtain two separate groups of patients characterized by low (≤20) and medium–high (>20) temptation to drink levels. This method has been widely used in previous scientific studies.30 (link),31 (link)
Data were separately analyzed for each psychological variable by means of repeated measures analysis of variance (ANOVA), with time (T1, T2) as the within-subject factor and temptation to drink (low vs medium–high) as the between-subject factor. The dependent variables in each analysis were the stages of change, self-efficacy, readiness to change, depression, and anxiety.
Pearson’s correlation analysis was performed to examine bivariate correlations among pretreatment factors (eg, depression, readiness to change) and posttreatment temptation to drink. Variables which showed a significant correlation with posttreatment temptation to drink (p<0.05) were included simultaneously in a multivariate regression model to identify baseline predictors of posttreatment temptation to drink. A p-value <0.05 was considered statistically significant.
All statistical analyses were conducted using SPSS, Windows version 13.0 (SPSS Inc, Chicago, IL, USA).
Data were separately analyzed for each psychological variable by means of repeated measures analysis of variance (ANOVA), with time (T1, T2) as the within-subject factor and temptation to drink (low vs medium–high) as the between-subject factor. The dependent variables in each analysis were the stages of change, self-efficacy, readiness to change, depression, and anxiety.
Pearson’s correlation analysis was performed to examine bivariate correlations among pretreatment factors (eg, depression, readiness to change) and posttreatment temptation to drink. Variables which showed a significant correlation with posttreatment temptation to drink (p<0.05) were included simultaneously in a multivariate regression model to identify baseline predictors of posttreatment temptation to drink. A p-value <0.05 was considered statistically significant.
All statistical analyses were conducted using SPSS, Windows version 13.0 (SPSS Inc, Chicago, IL, USA).
10
Impact of Air Pollutants on Biologics Initiation
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Continuous variables are shown as mean ± standard deviation and categorical variables as number (percentage) of patients. Between groups, we compared continuous variables by the independent t-test and categorical variables by chi-square test or Fisher’s exact test.
Because the impact of air pollutants might have multicollinearity and correlation based on their chemical properties, a correlation table including variance inflation factors (VIF) of each air pollutant amongst all pollutants and Pearson correlation coefficients with p-values of all pairs of air pollutants was plotted. VIF ≥ 10 was considered significant multicollinearity necessitating being corrected.
We examined the association between biologics initiation and levels of exposed air pollutants using multivariable conditional logistic regression models for adjustment, shown as adjusted odds ratios (aORs) with 95% confidence intervals (CIs). A probability (p) of < 0.05 was considered statistically significant. Statistical calculations were performed using the Statistical Package for the Social Sciences (SPSS), Windows Version 13.0 (SPSS Inc., Chicago, IL, USA).
Because the impact of air pollutants might have multicollinearity and correlation based on their chemical properties, a correlation table including variance inflation factors (VIF) of each air pollutant amongst all pollutants and Pearson correlation coefficients with p-values of all pairs of air pollutants was plotted. VIF ≥ 10 was considered significant multicollinearity necessitating being corrected.
We examined the association between biologics initiation and levels of exposed air pollutants using multivariable conditional logistic regression models for adjustment, shown as adjusted odds ratios (aORs) with 95% confidence intervals (CIs). A probability (p) of < 0.05 was considered statistically significant. Statistical calculations were performed using the Statistical Package for the Social Sciences (SPSS), Windows Version 13.0 (SPSS Inc., Chicago, IL, USA).
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