Data are expressed as mean ± SEM. Comparison between the NORel and polymer control groups were analyzed by the one-way ANOVA with the multiple comparison of means using Student’s t-test. All statistical analyses were performed using nonparametric statistics in SAS JMP version 13 (SAS Institute Cary, NC). Values of P<0.05 were considered statistically significant.
Sas jmp version 13
Manufactured by SAS Institute
Sourced in United States
SAS JMP version 13 is a statistical discovery software that enables users to explore data, uncover key insights, and make data-driven decisions. It provides a range of analytical and visualization tools to support statistical modeling, design of experiments, and quality improvement initiatives.
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Lab products found in correlation
7 protocols using sas jmp version 13
1
Statistical Analysis of NORel Effects
2
Predictive Biomarkers for Serum EPO
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Unless indicated otherwise, all data result from at least three biologically independent experiments. A p value of 0.05 in Student’s t test or ANOVA (SAS JMP, version 13) was considered an indicator for statistical significance.
For analyzing the clinical data, descriptive statistics such as mean and standard deviation were used to summarize continuous variables, while count and percentage were used for categorical variables. Student’s t test or univariate logistic regression was performed to evaluate the association between serum Epo levels vs PSMD1 expression and Epo vs ALDH1A3 expression. A multivariable linear regression model for Epo was constructed by including PSMD1 (yes (score 2 or 3) vs no (score 0 or 1)), and ALDH1A3 (yes (score 2 or 3) vs no (score 0 or 1)), age, cancer stage, Hb, triple-negative (yes vs no), chemo (yes vs no), receptor (positive vs negative), and histology. Model diagnostics were performed to assess the final multivariate regression model. For all statistical investigations, tests for significance were two-tailed, with a statistically significant p value threshold of 0.05. Statistical analyses were carried out using SAS version 9.2 (SAS Institute Inc., Cary, NC).
For analyzing the clinical data, descriptive statistics such as mean and standard deviation were used to summarize continuous variables, while count and percentage were used for categorical variables. Student’s t test or univariate logistic regression was performed to evaluate the association between serum Epo levels vs PSMD1 expression and Epo vs ALDH1A3 expression. A multivariable linear regression model for Epo was constructed by including PSMD1 (yes (score 2 or 3) vs no (score 0 or 1)), and ALDH1A3 (yes (score 2 or 3) vs no (score 0 or 1)), age, cancer stage, Hb, triple-negative (yes vs no), chemo (yes vs no), receptor (positive vs negative), and histology. Model diagnostics were performed to assess the final multivariate regression model. For all statistical investigations, tests for significance were two-tailed, with a statistically significant p value threshold of 0.05. Statistical analyses were carried out using SAS version 9.2 (SAS Institute Inc., Cary, NC).
3
Survival Analysis of GT Gene Expression
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The normalized expression data of the GT genes were correlated with the appropriate clinicopathological parameters and analyzed with the Kruskal-Wallis-Test. The relationship between GT gene expression and patients' probability of survival was predicted using the log-rank test and presented as Kaplan-Meier plots for overall survival (OS), cancer-specific survival (CSS), and disease-free survival (DFS). Cut-offs were determined by partition, with a minimum number of ≥48 patients and a minimum group distribution of ≥25%. To identify the independence and predictable impact of each GT gene expression, uni- and stepwise multivariable Cox regressions were applied. All of the statistical tests were calculated with SAS JMP version 13, the plots having been designed using GraphPad prism version 8. All p-values were two-sided; p-values of <0.05 were considered statistically significant.
4
Characterization of Cuticular Wax Composition
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Quantitative data on cuticular wax composition was subjected to two-way analysis of variance (ANOVA) to evaluate the differences between the analyzed berries; post-hoc Tukeys HSD was used to distinguish significantly different groups. Principal components analysis (PCA) on the correlation matrix and hierarchical cluster analysis using Ward’s method with the standardized data was performed to evaluate the relationships among various tested berries. Statistical analysis and data visualization was done using SAS JMP®, Version 13 (SAS Institute Inc., Cary, NC, USA).
5
Statistical Analysis of Experimental Data
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One-way analysis of variance (ANOVA) with Duncan's multiple range test at p < 0.05 was performed using SPSS statistical program version 25.0 (IBM, Chicago, USA). Principal component analysis was performed by using SAS JMP®, Version 13 (SAS Institute Inc., Cary, NC, USA).
6
Macrosteatotic Liver Graft Evaluation
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Variables were reported as median and range for continuous variables, and percentage for categorical variables. χ2 test was used to analyze categorical variables. For continuous variables, Welch’s test was used for unequal variance and Mann-Whitney for non-parametric. Logistic regression was used to analyze continuous and categorical variables. Univariate analysis was performed to determine differences of donor and recipient demographics and variables between absent/mild and moderate macrosteatotic grafts. Data is shown as means ± standard error of the means. Limit of statistical significance was set at P < 0.05. Analysis was performed with SAS JMP version 13.0 (SAS Institute, Cary, NC, United States).
7
Liver-Directed Therapy and Transplant Outcomes
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Continuous variables are listed as median with interquartile range (IQR) while categorical variables are listed as percentage of the total. Chi-square and Fisher’s exact test were used to analyze categorical variables. Bridge-to-transplant survival was calculated using the log-ranked Kaplan–Meier method. Logistic regression analysis was used for factors associated with bridge-to-transplant outcomes and initial response to liver-directed therapy. Univariate factors were deemed significant for P value < 0.100 and evaluated in multivariate analysis. Milan criteria instead of largest lesion size were used for multivariate analysis. Multivariate analysis of factors associated with bridge-to-transplant survival was performed using Cox proportional hazards model with significance determined by likelihood ratio test. Matched pairs analysis with nonparametric Wilcoxon signed-rank test was used to determine significant differences in factors at baseline and post-treatment. One-way ANOVA with Tukey multiple comparison test was used to determine significance between ALC at different times during waitlist period. Analyses were performed using SAS JMP version 13.0 (SAS Institute, Cary, NC) or GraphPad Prism version 8.2.0 (San Diego, CA).
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