We performed non-parametric statistical tests because of the small sample sizes of vmPFC lesion patients (n=5) and BDC patients (n=5) using SPSS. We used a two-tailed Wilcoxon signed-rank test for paired data to compare the proportion of gambles selected for the loss condition as compared to the gain condition, within each group. For between-group analyses we used a two-tailed Kruskal-Wallis test along with two-tailed Mann-Whitney U tests for pairwise comparisons. Because these tests collapse across all sure amount values, they are extremely conservative estimates of our within- and between-group reflection effects. We therefore also ran a mixed effects logistic regression using the R statistical package (https://www.R-project.org ) that allowed us to test for an overall interaction of group (vmPFC, NC, BDC) and condition (gain, loss), controlling for the effect of different levels of sure value (e.g., $5–$95), with respect to the subject’s preference for the gamble versus the sure option.
R statistical package
Manufactured by R Project for Statistical Computing
Sourced in United States, Austria
R is an open-source software environment for statistical computing and graphics. It provides a wide variety of statistical and graphical techniques, including linear and nonlinear modeling, classical statistical tests, time-series analysis, classification, clustering, and more. R is an interpreted language and is designed to work with data structures and data analysis.
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Lab products found in correlation
Sas version 9, by SAS Institute (4 mentions)
Statistica 7, by StatSoft (2 mentions)
Stata, by StataCorp (2 mentions)
Matlab, by MathWorks (2 mentions)
Spss statistical software, by IBM (2 mentions)
Spss statistic, by IBM (2 mentions)
Jmp pro, by SAS Institute (2 mentions)
Sas software, by SAS Institute (2 mentions)
Prism 8, by GraphPad (2 mentions)
Sas 9, by SAS Institute (2 mentions)
Statistical package for social science v 24, by IBM (1 mentions)
Slicer, by 3D Slicer (1 mentions)
Smili, by 3D Slicer (1 mentions)
R packages version 3, by R Project for Statistical Computing (1 mentions)
R package version 3, by R Project for Statistical Computing (1 mentions)
Jmp pro 14, by SAS Institute (1 mentions)
Stata version 14, by StataCorp (1 mentions)
Prism version 8, by GraphPad (1 mentions)
Spss 24.0 for windows, by IBM (1 mentions)
Spss version 22, by IBM (1 mentions)
177 protocols using r statistical package
1
Reflection Effects in Lesion Patients
2
Predicting Prostate Cancer Diagnosis and Aggressiveness
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The study population was split into two groups according to PCa diagnosis/HG-PCa. Frequencies and proportions were used to report categorical variables that were compared by means of the Chi-squared test. Continuous variables were presented as median and interquartile ranges (IQRs) and were compared using the Kruskal–Wallis test. Logistic regression models were used to identify predictors of PCa diagnosis and high-grade disease. Coefficients were then used to generate a nomogram to predict tumor diagnosis, whose calibration was assessed using the Hosmer–Lemeshow test. Calibration plots were generated with 200 bootstraps resampling to explore nomogram performance, and decision curve analyses (DCAs) assessed the net benefit of the model. For all tests, the significance level was set at a p value of <0.05. Statistical analysis was performed using the Statistical Package for Social Science v. 24.0 (IBM, Somers, NY, USA), the R statistical package (The R-Project for Statistical Computing, www.r-project.org ) and STATA (STATACorp. 2019. STATA Statistical Software: Release 16. College Station, TX, USA: STATACorp LLC).
3
Evaluating Prediction Model Performance
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The prediction performances of each prediction model were evaluated using NHIS-HEALS data and external test data, Rotterdam Study. Model discrimination was quantified by calculating the C-statistics for the survival model. All statistical analyses were conducted with SAS (version 9.4, SAS Inc., Cary, NC, USA) and the R Statistical Package (www.R-project.org ). The statistical significance criterion was set at 2-sided p < 0.05.
4
Mouse Brain Volume Analysis Protocol
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All statistical analyses were performed using the R statistical package (https://www.r-project.org/ ). Brain volumes were segmented according to the publicly available Dorr–Steadman–Ullman–Richards–Qiu–Egan 40-μm atlas, which defines 180 structures (356 bilaterally) within a three-dimensional atlas of adult C57Bl/6J mice66 (link),67 (link),68 (link),69 (link). Segmentation and labeling were performed using the RMINC package (https://github.com/Mouse-Imaging-Centre/RMINC ). All regional volumes were normalized to total brain volume for each mouse before model fitting. A linear regression model with interaction-with-genotype and interaction-with-sex terms was fit to brain volume data to compare pairs of structures. Sex and interaction with sex were included as covariates in the structural covariance model used to measure the interaction-with-genotype effect. In this model, ‘genotype’ is a two-level factor, with ‘wild type’ and ‘mutant’ as the two levels. The interaction-with-genotype coefficients were tested for significance using a two-sided t-test. P values were corrected for multiple comparisons using the Benjamini–Hochberg procedure; these FDR-corrected P values are herein referred to as q values. The coefficient of determination (R2) for each model was also evaluated.
5
Statistical Analysis of Research Data
6
Chromogranin A Stability Analysis
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After natural logarithm-transformation of the measurements, a normal distribution was seen in the 406 patients. A paired t-test was performed on these data and the concordance between the assays was measured by the correlation coefficient. Passing & Bablok curve fitting and a difference plot was performed. Linear regression analysis of covariates was made. For analysis of the covariate location, we used deviation coding in which the mean difference is used as reference. The CgA stability over time and at different temperatures was calculated as a recovery percentage of the CgA concentration at time 0. Recoveries ranging from 90% to 110% were deemed to be acceptable.
All analyses were performed with the R statistical package (version 3.1.0, 2014; www.r-project.org). Passing–Bablok and difference plot analysis were done in Analyse-it v2.30 (Analyse-It Software Ltd, www.analyse-it.com). Two-tailed P<0.05 was considered statistically significant.
All analyses were performed with the R statistical package (version 3.1.0, 2014; www.r-project.org). Passing–Bablok and difference plot analysis were done in Analyse-it v2.30 (Analyse-It Software Ltd, www.analyse-it.com). Two-tailed P<0.05 was considered statistically significant.
7
Mouse Brain Volume Analysis Protocol
8
Metabolomics Analysis of Endurance Level
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A linear regression model was run using R statistical package (version 2.14, www.r-project.org/ ) to assess association between metabolites and endurance level (moderate versus high). The model also corrected for the following possible confounders: sport power, gender, hemolysis levels (determined visually by Metabolon) and metabolites PCs. Multiple testing was Bonferroni corrected. A meta-analysis was utilized to identify metabolites equally influenced by endurance level in both metabolomics datasets in the current study and previously published study12 (link). Initially, functions from the R library ‘esc’ were used to convert the beta value from the regression analysis of individual datasets into effect size (in this case, difference in mean between low and high levels of endurance). The metafor R library was then used to run the metanalysis on the derived effects size from the individual datasets. The p-values from the meta-analysis were corrected for multiple testing based on FDR correction.
9
Multivariate Deformation Analysis for Glioma Prognosis
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All preprocessing steps were performed using ITK (www.itk.org ) based implementations. The image processing and deformation analysis for the patients were performed in a parallel computing environment utilizing the Case Western Reserve University High Performance Computing Cluster. FSLUtils tool of FSL47 (link) (fsl.fmrib.ox.ac.uk/fsl/fslwiki/Fslutils) was used to compute the variance of voxel-wise deformation magnitudes. The Spearman correlations were performed using the Python Scipy package (www.scipy.org ). The second set of correlations that involved computation of partial correlations for age and tumor volume as confounding factors, and computation of C indices were performed using the R statistical package (www.r-project.org ). All figures were created using 3D slicer (slicer.org">www.slicer.org ) and Smili (github.com/shakes76/Smili).
10
Statistical Analysis of Experimental Data
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Statistical analysis was performed using the R statistical package (www.r-project.org ). Statistical significance was evaluated using two‐tailed paired Student's t‐test, the Wilcoxon rank‐sum test or one‐way ANOVA followed by the Bonferroni post‐hoc test. Pearson correlation analysis was performed to observe a possible correlation. For all analyses, the level of statistical significance was set at **P < 0.01 or *P < 0.05.
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