The Kolmogorov-Smirnov test was done to determine the normality of data. Differences in dietary intakes between treatment groups were detected with independent-sample t-tests. Multiple linear regression models were used to assess the treatment effects on study outcomes after adjusting for confounding parameters including; age, and BMI. Significance of the treatment effects was presented as the mean differences with 95% confidence interval. Bootstrapping was also used as a sensitivity analysis of confidence interval. P-values < 0.05 were considered statistically significant. All statistical analyses were done using the Statistical Package for Social Science version 18 (SPSS Inc., Chicago, Illinois, USA).
Statistical package for social science version 18
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SPSS Statistics version 18 is a software package used for statistical analysis. It provides a range of statistical procedures for data management, analysis, and presentation. The core function of the software is to enable users to perform statistical analyses on data collected from various sources.
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Lab products found in correlation
37 protocols using statistical package for social science version 18
1
Dietary Intake Effects on Study Outcomes
2
Effects of Vitamin D on Inflammation
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The Kolmogorov-Smirnov test was applied to control the normal distribution of variables. Independent sample t-test was used to establish changes in anthropometric measures and dietary intakes between the two groups. To determine the effects of vitamin D administration on inflammation and oxidative stress expression, we used independent sample t-test. P < 0.05 were considered statistically significant. All statistical analyses conducted using the Statistical Package for Social Science version 18 (SPSS Inc., Chicago, Illinois, USA).
3
Statistical Analysis
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Statistical analysis was performed using the Statistical Package for Social Science version 18 (SPSS, Chicago, IL, USA). The data were considered statistically significant when p was <0.05. One-way ANOVA was performed to assess significant differences in all quantitative data.
4
Statistical Analysis of Continuous and Categorical Measurements
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Results of continuous measurements were presented as mean ± SD and analyzed using Student's unpaired t-test. Results of categorical measurements were presented in number (%) and compared using χ2 test of significance. In the above tests, a P < 0.05 was accepted as indicating statistical significance. Data analysis was carried out using Statistical Package for Social Science Version 18 (SPSS Inc., Chicago, IL, USA) and Microsoft word and Excel were used to generate graphs and tables.
5
Statistical Analysis of Anthropometric and Dietary Outcomes
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The Kolmogorov-Smirnov test was done to determine the normality of data. To detect the differences in anthropometric measures, nutrient intakes and psychological parameters between treatment groups, we used independent-samples t-test. Multiple linear regression models were applied to determine treatment effects on study outcomes after adjusting for confounding parameters including; age, and BMI. The effect sizes were presented as the mean differences with 95% confidence intervals. Differences in proportions were evaluated by Chi square test. P-values < 0.05 were considered statistically significant. All statistical analyses were done using the Statistical Package for Social Science version 18 (SPSS Inc., Chicago, Illinois, USA).
6
Vitamin D Supplementation and Metabolic Profiles
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We used the Kolmogrov-Smirnov test to examine the normal distribution of variables. One-way analysis of variance (ANOVA) was used to detect differences in anthropometric characteristics and dietary intakes between the three groups. To determine the effects of vitamin D supplementation on metabolic profiles, we used ANOVA test. The changes across three groups were compared using Bonferoni post hoc pair-wise comparisons. To control the effect of confounders, we adjusted all analyses for baseline values, age and baseline BMI to avoid potential bias. These analyses were conducted by analysis of covariance (ANCOVA) using general linear models. We used Pearson correlations analysis to assess between changes of metabolic profiles and changes in the 25(OH)D3 concentrations. p < 0.05 was considered as statistically significant. All statistical analyses were done using the Statistical Package for Social Science version 18 (SPSS Inc., Chicago, IL, USA).
7
Ketamine Effects on Open Field Locomotion
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All the statistical analyses were conducted using GraphPad Prism (GraphPad Software, Inc., San Diego, CA, USA) version 5.01.336 and Statistical Package for Social Science version 18 (SPSS Inc., Chicago, IL, USA). The moving paths and travelled distances in the open field were tracked before and after ketamine injection. The measurement parameters were analyzed by analysis of variance (ANOVA), followed by Bonferroni’s post hoc tests. Probability (p) values under 0.05 were considered significant.
8
Synbiotic Supplementation Effects on Metabolic Parameters
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The Kolmogorov-Smirnov test was performed to determine the normality of data. Outcome log-transformation was used if model residual has non-normal distribution (hs-CRP, MDA, SHBG and FAI). To detect differences in anthropometric parameters as well as in macro- and micro-nutrient intakes between the two groups, we applied independent t-test. To assess the effects of synbiotic supplementation on metabolic parameters, we used one-way repeated measures analysis of variance. Adjustment for changes in baseline values of biochemical parameters, age and baseline BMI was performed by analysis of covariance (ANCOVA). P-values < 0.05 were considered statistically significant. All statistical analyses were done using the Statistical Package for Social Science version 18 (SPSS Inc., Chicago, Illinois, USA).
9
Exploring Predictors of Hepatocellular Carcinoma Prognosis
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The differences between the TACE group and the non‐TACE group in clinicopathological characteristics were tested using the Pearson chi‐squared test. The differences between patients with HMRE and with LMRE in adverse reactions related to pa‐TACE were tested using the Pearson chi‐square test or Fisher's probabilities test. The associations between microRNA‐4651 expression and clinicopathological characteristics of patients with hepatocellular carcinoma were analyzed with nonconditional logistic regression using ORs and 95% confidence intervals (CIs). The effects of pa‐TACE treatment and microRNA‐4651 expression on hepatocellular carcinoma prognosis were evaluated using Kaplan‐Meier survival analysis models (with the log‐rank test) and Cox regression models (including univariate and multivariate models). Hazard ratios (HRs) and 95% CIs were used to determine the prognostic potential of clinicopathological variables (including pa‐TACE and microRNA‐4651 expression). All statistical tests were performed using the Statistical Package for Social Science version 18 (SPSS Institute, Chicago, IL), and a P value of less than 0.05 was considered statistically significant.
10
Examining Metabolic and Psychological Outcomes
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p‐Values < .05 were considered statistically significant. The Kolmogorov–Smirnov test was done to assess the normality of data. Statistical analyses were done using the Statistical Package for Social Science version 18 (SPSS Inc., Chicago, IL, USA). Descriptive statistics were calculated for demographic variables. To detect the differences in anthropometric scales among two intervention groups, we used the independent‐samples Chi‐square and t‐test analyses. Multiple linear regression models were used to determine treatment effects on trial outcomes (psychological status and metabolic biomarkers) after adjusting for confounding variables. The effect sizes were presented as the mean differences with 95% confidence intervals.
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p‐Values < .05 were considered statistically significant. The Kolmogorov–Smirnov test was done to assess the normality of data. Statistical analyses were done using the Statistical Package for Social Science version 18 (SPSS Inc., Chicago, IL, USA). Descriptive statistics were calculated for demographic variables. To detect the differences in anthropometric scales among two intervention groups, we used the independent‐samples Chi‐square and t‐test analyses. Multiple linear regression models were used to determine treatment effects on trial outcomes (psychological status and metabolic biomarkers) after adjusting for confounding variables. The effect sizes were presented as the mean differences with 95% confidence intervals.
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