The minimum p-value method with log-rank statistics was implemented using the R package “maxstat.” The freely available R package “SemiPar” was applied to fit logistic regression models with splines. The two-sided significance level for all tests was set at 0.05, and any p-values less than this threshold were deemed statistically significant. The R programming language, version 4.1.2 (R Foundation for Statistical Computing, http://www.R-project.org ), was utilized for conducting the statistical analyses.
R programming language version 4
Manufactured by R Project for Statistical Computing
R is a free, open-source programming language and software environment for statistical computing and graphics. Version 4.2.1 was released in June 2022. R provides a wide variety of statistical and graphical techniques, and is highly extensible.
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Lab products found in correlation
5 protocols using r programming language version 4
1
Statistical Analysis Methods for Logistic Regression
2
In Vivo T2 Measurement Analysis
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Statistical comparisons between phantoms and cell samples were performed using a t-test. P values less than 0.05 were considered significant. For in vivo experiments, a total of 216 T2 observations from 12 mice were available for analysis. Statistical significance was set at p ≤ 0.05p ≤ 0.05 and all tests were two-sided. Missing observations were reported and were excluded on an analysis-by-analysis basis. Paired t-tests were performed for comparisons of prescan and 24-h T2 measurements within treatment group and a one-way ANOVA was performed for comparisons between groups. All analyses were done in R programming language, version 4.1.2 (R Foundation for Statistical Computing, Vienna, Austria). All graphics were produced using the R package ggplot2, version 3.3.5 (Hadley Wickham).
3
Tacrolimus PBPK Model Development and Validation
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PK‐Sim® and MoBi® Version 10 (Open Systems Pharmacology Suite, www.open‐systems‐pharmacology.org , 2021) were used for the development of the tacrolimus PBPK model, parameter identification (Levenberg–Marquardt algorithm), and model sensitivity analyses. Published clinical study data were digitized with the help of Engauge Digitizer Version 12.1 (Mitchell et al.24 ) according to best practices.25 The compilation of plots as well as calculations of PK parameters and quantitative model performance measures were performed using the R programming language Version 4.2.1 (R Foundation for Statistical Computing).
4
Quinidine PBPK Model Development and Evaluation
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The development of the quinidine PBPK model, parameter optimizations, and sensitivity analysis, as well as simulation of different DD(G)I scenarios were performed with PK‐Sim (version 11, Open Systems Pharmacology Suite, www.open‐systems‐pharmacology.org ). Published plasma concentration‐time profiles were digitized with Engauge Digitizer 10.12 (M. Mitchell, https://markummitchell.github.io/engauge‐digitizer ). Model evaluations (i.e., graph generation and calculation of pharmacokinetic parameters as well as statistics) were accomplished using the R programming language version 4.2.1 (The R Foundation for Statistical Computing, Vienna, Austria) and Rstudio 2022.07.0 (Rstudio).
5
Evaluating Retinal Vessel Whitening
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Demographic and clinical characteristic data were presented as count (%) for categorical variables and mean (95% confidence interval) for continuous variables. Statistically significant differences between cohorts were determined using t-tests and Fisher’s Exact tests, for subject-level continuous and categorical characteristics, respectively, and linear mixed-effects models were used to address the use of two eyes from patients. Given the smaller sample size of the subjects with peripheral retinal vessel whitening cohort, parametric assumptions were confirmed through Shapiro-Wilks, Kolmogorov–Smirnov, and F-tests. Additionally, Fisher’s Exact test was used to evaluate pairwise comparisons of individual categorical characteristics between vessel whitening cohorts. Statistical analyses were performed using the R programming language version 4.2.1 (R Foundation for Statistical Computing, Vienna, Austria). A P-value ≤ 0.05 was considered statistically significant.
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