R statistical computing software

Q-Q plot and boxplot were used to check the normality of the data. One-sample t-test and one-sample Wilcoxon signed-rank test were applied as needed to test the differences of hormone measurements at both baseline and after FSH stimulation for Phase I compared to Phase II. We also tested for differences in the log fold change of E₂, Inh B and insulin hormone before and after FSH. For all analysis, p values of < 0.05 were considered statistically significant. Statistical analysis were performed using the R statistical computing software (version 2.6.2, http://www.r-project.org, 2009).

All statistical analyses were carried out using the R statistical computing software (https://www.R-project.org). For all transfection experiments, luciferase reporter activity levels were normalized as log₂ fold change relative to the non-deletion construct baseline average. To determine if co-transfection produced a dose-dependent effect and whether there was an interaction between dose and vector type (non-deletion/deletion luciferase reporters) a two-way analysis of variance (ANOVA) was performed for each co-transfection experiment. Dose, luciferase reporter type plus a dose:vector interaction variable were included in each ANOVA. P ≤ 0.05 were considered significant. Tukey's post-hoc analysis was performed when there was significant interaction between vector type and dose.
To determine the association of the rs3045215 deletion with coronary artery calcification a linear regression model was used. The rs3045215 variant was coded as counts of the minor (deletion) allele, i.e., HNR = 0, HET = 1, and HR = 2. The analysis was split by sex and CAD case status with smoking status and age included in the linear model. Agatston scores were normalized using log₂ after adding 1 to address zero values in the data. Linear regression was performed using the lm function in R and data was plotted using the data visualization package ggplot2 (39 ).

Data visualization and statistical analysis were performed using R statistical computing software (https://www.r-project.org/). Scatter plots were generated using ggplot2 package. Student’s t-test of the difference between two data sets were conducted using t.test function in R. For multiple comparisons, we performed ANOVA with Tukey’s test for post hoc analysis using aov and TukeyHSD function in R.

Statistical analysis was performed by using Excel (Office 2016, Microsoft) and R statistical computing software (version 3.0.3; http://www.r-project.org/). ADC variations in a test-retest scenario were assessed according to the Bland-Altman methodology [24 (link), 25 (link)]. Mean, relative difference, and standard deviation of the differences of two subsequent measurements on each machine are calculated and depicted in a Bland-Altman plot. The coefficient of variation (CoV) was estimated as CoV = standard deviation / mean (%). A Wilcoxon signed-rank test was performed to assess the variations in ADC values calculated at the MRI device and centrally. The significance level was adjusted according to the number of comparisons that were performed (Bonferroni correction, p < 0.007).

Continuous variables were represented as mean ± standard deviation or median and range and categorical variables as the percentage of the total number. The Fisher's exact test was performed to compare the differences between the categorical variables. Continuous variables were compared using Student's t-test. For all of the statistical analyses, a p value of <0.05 was considered statistically significant. The optimum threshold for total fistula length discriminating presence or absence of abscess on MRI was calculated based on receiver operating curve analysis (12 (link)). All statistical analyses were carried out using R statistical computing software (version 3.6.3, The R Foundation for Statistical Computing).

Numerical variables were recorded and analysed as median [25^th - 75^th percentile] while categorical variables were expressed as frequencies and percentages. Comparisons between groups were based on the Mann-Whitney or Chi square tests. The predictive accuracy of testosterone was evaluated using Receiver Operating Characteristic (ROC) analysis and quantified in terms of Area Under the Curve (AUC) and corresponding 95% confidence interval (95% C.I.). The independent role of testosterone in predicting pathological outcomes at radical prostatectomy was assessed using multivariable logistic regression models; a bootstrap approach, based on 1999 bootstrap replications, was used to compare the percentage change in predictive accuracy (in terms of AUC) between nested logistic models.
Statistical analyses and modelling were performed with R statistical computing software (R Foundation for Statistical Computing, Vienna, Austria). P-values < 0.05 were considered statistically significant.