Statistical analyses were performed using R Core Team software (2020; R Foundation for Statistical Computing, Vienna, Austria). Continuous variables were presented as the mean ± standard deviation. Categorical variables were presented as frequencies and percentages. The comparisons of categorical data were performed using the Fisher exact test, and odds ratios were calculated. The statistical significance level was set at a P value less than .05.
R core team software
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 developed and maintained by the R Core Team.
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Lab products found in correlation
5 protocols using r core team software
1
Statistical Analysis of Categorical Data
2
Extraction and Characterization Protocol
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The extraction experiments were performed at least in duplicate, while the characterizations were performed in triplicate or quintuplicate. The results were evaluated for significance (p ≤ 0.05) using Duncan’s multiple comparisons test with SAS® software (version 9.3, SAS Institute Inc., Cary, NC, USA) and t-tests using R Core Team software (version 4.2.1, The R Foundation, Vienna, Austria, 2022).
3
Immune Markers and COVID-19 Infection
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Unadjusted logistic regression models and models fully adjusted for sex, mean hours exposed to COVID-19 at school and in the community, mean percentage of time wearing a face mask at school and in the community, and days since blood sample collection were used to examine the relationship between immune markers, and infection risk. All analyses were conducted using SAS software (version 9.4; SAS), R Core Team software (version 4.1.2; 2021; R Foundation for Statistical Computing), and GraphPad Prism software (version 9.4.1 for Mac; GraphPad Software).
4
Immunotherapy in Advanced Cancer Patients
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The sample size was determined by the available patients meeting the inclusion criteria. We conducted the statistical analysis using R Core Team software (R Foundation for Statistical Computing, Vienna, Austria), version 2019.39 Categorical variables were presented by numbers and percentiles. Continuous variables were reported by medians and ranges. Categorical variables were compared using the χ2 method or Fisher’s exact test, while continuous variables were compared using either a t-test or Mann-Whitney-Wilcoxon test. OS was assessed by the Kaplan-Meier method, with the log-rank test for comparison between groups. The propensity score matching analysis matching patients in the two compared groups for age and ECOG PS was performed with a caliper of 0.5, a 1:1 matching (ICI administration vs no ICI administration), and an AUC (area under the curve) of 0.67. Cox proportional hazards univariable models including prespecified covariates were constructed. Covariates for the multivariate Cox regression model were selected from the statistically significant covariates found in the aforementioned univariate model. P values less than 0.05 were considered statistically significant. No correction for multiple comparisons was performed.
5
Data Analysis Using SAS and R
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UK data extraction and data analyses were performed using SAS version 7.1 (SAS Institute Inc, Cary, NC). US data extraction and analyses were performed using R Core Team software (R Foundation for Statistical Computing, Vienna, Austria, 2013). Graphic representation of the data was developed using the software package R ggplot. 20 20.
Wickham, H. ggplot2: elegant graphics for data analysis Springer-Verlag, New York, NY, 2016 Crossref Google Scholar
Wickham, H. ggplot2: elegant graphics for data analysis Springer-Verlag, New York, NY, 2016 Crossref Google Scholar
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