R software version 3

Manufactured by R Project for Statistical Computing

Sourced in United States, Austria

R version 3.6.2 is a free and 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 others. R is an implementation of the S programming language and has a syntax that is similar to that language, making it easy for users familiar with S to transition to R.

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Lab products found in correlation

Sas version 9, by SAS Institute (20 mentions) Spss version 25, by IBM (14 mentions) Sas 9, by SAS Institute (13 mentions) Sas software version 9, by SAS Institute (12 mentions) Rms package, by R Project for Statistical Computing (11 mentions) Prism 5, by GraphPad (7 mentions) Prism 6, by GraphPad (7 mentions) Prism 8, by GraphPad (7 mentions) Spss software version 22, by IBM (7 mentions) Spss 21, by IBM (6 mentions) Spss version 23, by IBM (5 mentions) Spss version 19, by IBM (5 mentions) Spss software, by IBM (5 mentions) Spss statistics version 26, by IBM (4 mentions) Spss statistics for window, by IBM (4 mentions) Spss statistics 21, by IBM (4 mentions) Empower r, by X&Y Solutions (4 mentions) Spss version 22, by IBM (4 mentions) Sas statistical software version 9, by SAS Institute (4 mentions) Spss statistics version 23, by IBM (4 mentions)

1 245 protocols using r software version 3

Dentin Bond Strength Evaluation

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Bond strength values for each group were analyzed by Shapiro–Wilk test (R Software version 3.4.3, The R Foundation for Statistical Computing, Vienna, Austria) in order to assess the normality of the data distribution. Factorial ANOVA and post hoc Tukey test (R Software version 3.4.3, The R Foundation for Statistical Computing, Vienna, Austria) were used to determine statistically significant differences between factors: the dentin-like caries lesions model (two levels—chemical and biological models) and dentin remineralization treatment (four levels—SD, DD, NaF + DD, CPP-ACP + DD and P₁₁-4 + DD) on dentin/resin bond strength, and additional Dunnett test to determine statistically significant differences between the experimental groups and the control group (sound dentin). The Kruskal Wallis test was used to evaluate the failure mode. The R Software version 3.4.3 (The R Foundation for Statistical Computing, Vienna, Austria), was used to perform the tests. Statistical difference was set at α = 5%.

Barbosa-Martins L.F., de Sousa J.P., Alves L.A., Davies R.P, & Puppin-Rontanti R.M. (2018). Biomimetic Mineralizing Agents Recover the Micro Tensile Bond Strength of Demineralized Dentin. Materials, 11(9), 1733.

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Propensity Score Matching for Cardiac Outcomes

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All variables were tested for normality with the Shapiro–Wilk test. Data are presented as mean±SD or as absolute numbers and percentages where appropriate. Comparison of continuous data between groups was performed with the Student t test for independent samples or with 1‐way ANOVA or with the Mann–Whitney test. The Pearson χ² or the Fisher exact test were used to compare categorical variables. Propensity score matching was performed with a 1:1 ratio taking into account the following variables: (1) age, (2) sex, (3) left atrium size, and (4) presence of a cardiac implantable electronic device. For propensity score matching, the package MatchIt with the method nearest and a caliper width equal to 0.25 on R software version 3.6.2 (R Foundation for Statistical Computing, Vienna, Austria) was used. Recurrence‐free survival over time was calculated by the Kaplan–Meier method, log‐rank test, and Cox regression was used for comparison between groups. The proportional hazards assumption and influential observations for the Cox model were tested.
All tests were 2‐sided, and a P value of 0.05 was considered statistically significant. Analyses were performed with SPSS 23.0 statistical software (IBM Company, Chicago, IL) and with R software version 3.6.2 (R Foundation for Statistical Computing).

Bisignani A., Conte G., Pannone L., Sieira J., Del Monte A., Lipartiti F., Bala G., Miraglia V., Monaco C., Ströker E., Overeinder I., Almorad A., Gauthey A., Franchetti Pardo L., Raes M., Detriche O., Brugada P., Auricchio A., Chierchia G, & de Asmundis C. (2022). Long‐Term Outcomes of Pulmonary Vein Isolation in Patients With Brugada Syndrome and Paroxysmal Atrial Fibrillation. Journal of the American Heart Association: Cardiovascular and Cerebrovascular Disease, 11(15), e026290.

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Predicting Functional Outcomes with QFR

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Categorical variables were presented as absolute frequencies and percentages. Continuous variables were presented as mean ± standard deviation. Data were analyzed per patient for baseline characteristics and vessels for functional and angiographic evaluation. Correlation between residual QFR and FFR, and NHPR was obtained by Pearson’s correlation analysis. Sensitivity, specificity and accuracy were calculated to evaluate the capacity to predict post-PCI good functional results (FFR > 0.90 or NHPR > 0.95) with pre-PCI residual QFR (cut-off value 0.9). All tests were two sided. Differences were statistically significant when the p-value was < 0.05. Statistical analysis was performed using R software, version 3.6.1 (R Project for Statistical Computing).

Amat-Santos I.J., Marengo G., Sánchez-Luna J.P., Cortés Villar C., Rivero Crespo F., Jiménez Díaz V.A., de la Torre Hernández J.M., Pérez de Prado A., Sabaté M., López-Palop R., Vegas Valle J.M., Suárez de Lezo J., Fernandez Cordon C., Gonzalez J.C., García-Gómez M., Redondo A., Carrasco Moraleja M, & San Román J.A. (2023). Validation of Quantitative Flow Ratio-Derived Virtual Angioplasty with Post-Angioplasty Fractional Flow Reserve—The QIMERA-I Study. Journal of Cardiovascular Development and Disease, 11(1), 14.

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Predicting Lymph Node Metastasis

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A Chi-square test was used to compare the categorical variables. Multivariable logistic regression analysis was performed with the following clinical and pathological candidate predictors: age, gender, tumor size, tumor differentiation degree, LMR, NLR and PLR, which were applied to develop a diagnostic model for LNM using the primary cohort. A bi-direction stepwise selection process with the Akaike information criterion as the stopping rule was performed. The nomogram was formulated based on the results. To evaluate the discrimination of our predictive model, the concordance index (C-index) and receiver operating characteristic (ROC) curve were constructed, and a calibration curve was used to assess the consensus degree of our models. In this study, SPSS 26.0 and R software (version 3.6.1, www.rproject.org) were used in statistical analyses.

Song L., Heng Y., Hsueh C.Y., Huang H., Tao L., Zhou L, & Zhang M. (2022). A Predictive Nomogram for Lymph Node Metastasis in Supraglottic Laryngeal Squamous Cell Carcinoma. Frontiers in Oncology, 12, 786207.

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COVID-19 Clinical Features and Treatments

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We report the frequency of clinical features, relevant laboratory findings, and treatments among patients stratified according to SARS-CoV-2 test results, age groups, and clinical syndromes. Continuous variables were expressed as medians and interquartile ranges or ranges, and categorical variables were expressed as counts and percentages. We did not impute missing data. We analyzed all data using R software, version 3.6.1 (R Project for Statistical Computing).

Feldstein L.R., Rose E.B., Horwitz S.M., Collins J.P., Newhams M.M., Son M.B., Newburger J.W., Kleinman L.C., Heidemann S.M., Martin A.A., Singh A.R., Li S., Tarquinio K.M., Jaggi P., Oster M.E., Zackai S.P., Gillen J., Ratner A.J., Walsh R.F., Fitzgerald J.C., Keenaghan M.A., Alharash H., Doymaz S., Clouser K.N., Giuliano JS J.r., Gupta A., Parker R.M., Maddux A.B., Havalad V., Ramsingh S., Bukulmez H., Bradford T.T., Smith L.S., Tenforde M.W., Carroll C.L., Riggs B.J., Gertz S.J., Daube A., Lansell A., Coronado Munoz A., Hobbs C.V., Marohn K.L., Halasa N.B., Patel M.M, & Randolph A.G. (2020). Multisystem Inflammatory Syndrome in U.S. Children and Adolescents. The New England Journal of Medicine.

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Comprehensive Bioinformatics Analysis Protocol

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R software version 3.6.1 (R Project for Statistical Computing) was used for statistical analyses and data visualization with the following packages: survival, survminer, survivalROC, glmnet, biomaRt, ggplot2, gridExtra, clusterProfiler, and forestmodel. KNIME software version 4.0.2 (KNIME AG) was used for data wrangling. Statistical analysis was performed from April to July 2020.

Graf R.P., Eskander R., Brueggeman L, & Stupack D.G. (2021). Association of Copy Number Variation Signature and Survival in Patients With Serous Ovarian Cancer. JAMA Network Open, 4(6), e2114162.

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Interobserver Variability in Surgical Indications

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Categorical variables are reported as frequency (n) and percentages and continuous variables as mean value and standard deviation. To evaluate interobserver variability, absolute and relative differences from the mean of the measurements were calculated. Then, the intraclass correlation coefficient and its 95% confidence interval to determine the variability of the measurements were calculated. Bland-Altman analysis was also used for evaluating interobserver variability.
To analyze the interobserver concordance in the surgical indications, the longitudinal diameter considering the cut-off points of the guidelines were categorized (10 and 15 mm) and the Kappa index and its 95% confidence interval in each of the situations were calculated. Data were analyzed using the IBM SPSS Statistics for Windows, Version 25 (IBM, Armonk, New York) and R software, version 3.6.1 (R Project for Statistical Computing).

Villalba G.C., López J., Garcia-Granja P.E., Sevilla T., Revilla A., de Miguel M., Pulido P., Gómez I, & San Román J.A. (2023). Measurement of vegetations in infective endocarditis: An inaccurate method to decide the therapeutical approach. Cardiology Journal, 30(1), 68-72.

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Comparative Statistical Analysis Protocol

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Wilcoxon test is used to compare the differences between groups of classified variable. Spearman analysis is used to calculate the correlation between continuous variables. The above collected data was analyzed using R software version 3.6.1 (https://www.r-project.org). Differences between groups were analyzed using t test. Pearson’s correlation analysis was used to calculate correlation coefficients (r) using Graphpad Prism 7. With all statistical methods, p < 0.05 was considered statistically significant.

Yu W., Liu F., Lei Q., Wu P., Yang L, & Zhang Y. (2022). Identification of Key Pathways and Genes Related to Immunotherapy Resistance of LUAD Based on WGCNA Analysis. Frontiers in Oncology, 11, 814014.

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Antiplatelet Therapy Discontinuation and Cerebrovascular Events

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The relationship between discontinuation of antiplatelet therapy and intracranial hemorrhage or cerebral infarction was analyzed using multiple logistic regression, considering all confounding variables. The odds ratio (OR) and 95% confidence interval (CI) were calculated, and the statistical significance level was set at 0.05. The survival rate according to the continuation of antiplatelet therapy was obtained using Kaplan-Meier analysis, and the difference in survival rate according to the continuation of antiplatelet therapy was verified using the log-rank test, a nonparametric test method. In addition, the hazard ratio according to continuation of antiplatelet therapy was obtained using the Cox proportional hazards model. The analysis was conducted by applying the same statistical method as described above to the period of DAPT use. R software (version 3.6.1; R Project for Statistical Computing, Vienna, Austria) was used for analysis’ the “dplyr” package was used for data cleansing; “moonBook” packages were used for multiple logistic regression analysis; “survminer” and “survival” packages were used for the survival analysis.

, & Kim T.G. (2023). Discontinuation of antiplatelet therapy after stent-assisted coil embolization for cerebral aneurysms. Journal of Cerebrovascular and Endovascular Neurosurgery, 25(2), 132-142.

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Optimizing Prognostic Biomarkers in Cancer

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We used the X-tile program (Yale University School of Medicine, New Haven, CT, USA) to determine the optimal cut-off values of TGR₀ and baseline SLD (SLD₀) to maximize PFS differentiation (26 (link)). According to the TGR₀ cut-off point, patients were divided into two groups, and baseline characteristics between the two groups were compared. Continuous variables were expressed as median (range) and analyzed using Mann-Whitney U-test or independent t-test depending on the normality of distribution; categorical variables were expressed as number (%) and analyzed using Fisher’s exact test or Chi-square test as appropriate. PFS and OS survival curves were generated using Kaplan-Meier method and the differences were compared using the log-rank test. Investigation of the effect of TGR₀ and other baseline parameters on treatment outcomes was performed using univariate and multivariate Cox regression analyses. Two-sided P-value of less than 0.05 was considered statistically significant. All statistical analyses were performed using the R software version 3.6.1 (https://www.r-project.org/).

He L.N., Zhang X., Li H., Chen T., Chen C., Zhou Y., Lin Z., Du W., Fang W., Yang Y., Huang Y., Zhao H., Hong S, & Zhang L. (2021). Pre-Treatment Tumor Growth Rate Predicts Clinical Outcomes of Patients With Advanced Non-Small Cell Lung Cancer Undergoing Anti-PD-1/PD-L1 Therapy. Frontiers in Oncology, 10, 621329.

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