Data analysis was performed with the R software environment for statistical computing and graphics (R Foundation for Statistical Computing, Wien, Austria). All data were analyzed using t tests or one-way analysis of variance followed by post hoc Dunnett’s test as appropriate and represented as the mean±standard error of mean. P values equal to or less than 0.05 were taken to be statistically significant.
R software environment for statistical computing and graphics
Manufactured by R Project for Statistical Computing
Sourced in Austria, United States
R is an open-source software environment for statistical computing and graphics. It provides a wide range of statistical and graphical techniques, including linear and nonlinear modeling, classical statistical tests, time-series analysis, classification, clustering, and others. R is a programming language and software environment for statistical computing and graphics.
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Lab products found in correlation
6 protocols using r software environment for statistical computing and graphics
1
Statistical Analysis of Experimental Data
2
Predictive Modeling for Clinically Significant Prostate Cancer
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Descriptive statistics included frequencies and proportions for categorical variables. Medians and interquartile ranges (IQR) were reported for continuously-coded variables. We relied on univariable and multivariable logistic-regression model analyses to test significant predictors for csPCA at biopsy and to build csPCA-predictive MRI-based risk-models in the subgroup of PI-RADS 5 patients. The covariates for adjustment were predefined and consisted of ECE (yes vs. no), PSA-D (continuously coded), and cT-stage (cT1 vs. cT2). Receiver Operating Characteristic (ROC) curves were drawn for the models, and the corresponding areas under the curve (AUC) were compared using the DeLong-test. All tests were two-sided, and the significance-level was set at p < 0.05. The R software environment for statistical computing and graphics (version 3.4.3, R Foundation for Statistical Computing) was used for all statistical analyses.
3
Statistical Analysis of Nonparametric Variables
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Descriptive statistics included frequencies and proportions for categorical variables. Mean, median, and interquartile ranges (IQR) were reported for continuously coded variables. The Wilcoxon sign rank test for paired samples was used to compare continuous nonparametric variables. In all statistical analyses, R software environment for statistical computing and graphics (R version 3.6.1, The R Foundation, Indianapolis, IN, USA) was used. All tests were two-sided with a level of significance set at p < 0.05.
4
Repeat Salvage Surgery Outcomes
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Descriptive statistics included frequencies and proportions for categorical variables. The medians and interquartile ranges (IQR) were reported for continuously coded variables. The statistical significance of differences in medians and proportions was evaluated using the Kruskal–Wallis and Chi-square tests, respectively. For comparison of contingency tables, the Fisher’s exact test was applied. Kaplan–Meier plots graphically depict the BRFS and cBR after repeat salvage surgery. Univariable and multivariable Cox regression models were used to investigate the association between oncological outcomes (BRFS, cBR, TFS) and selected variables [age at repeat surgery (continuously coded), Gleason Grade Group at RP (I–II vs. III–V), radiation therapy post RP (yes vs. no), time between initial SLND/RGS and repeat RGS (continuously coded), PSA at repeat RGS (continuously coded), number of PSMA PET-positive lesions prior to repeat RGS (continuously coded), and localization of PSMA PET-positive lesions (pelvic vs. retroperitoneal and pelvic vs. retroperitoneal only)]. All tests were two sided, with the significance level set at p < 0.05. The R software environment for statistical computing and graphics (version 3.4.3, R Foundation for Statistical Computing) was used for all statistical analyses.
5
Survival Analysis of Cancer Treatment
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OS and PFS were estimated by the Kaplan–Meier product-limit method and tested by means of two-sided log-rank tests; p-values for comparison among the three groups of BOR (CR/PR, SD and PD) were corrected using the Bonferroni method. Participants lost to follow-up were censored on the day of last contact. Prognostic factors, including BOR for PFS and OS, were evaluated by estimating HRs and 95% CIs using univariate and multivariate Cox proportional hazards models.
Variables adjusted in the multivariate analyses included sex (male vs. female), age (<65 vs. ≥65 years), ECOG PS (0 vs. 1–2), smoking, and alcohol consumption (ever vs. never). Platinum refractoriness (yes vs. no) and irAEs (present vs. absent) had p-values of less than 0.1 on univariate analysis of OS and were also included in multivariate analysis. All statistical analyses were performed using EZR [30 (link)] version 1.51 (Saitama Medical Center, Jichi Medical University, Saitama, Japan), a graphical user interface, together with the R software environment for statistical computing and graphics (The R Foundation for Statistical Computing, Vienna, Austria); p-values of < 0.05 were considered statistically significant.
Variables adjusted in the multivariate analyses included sex (male vs. female), age (<65 vs. ≥65 years), ECOG PS (0 vs. 1–2), smoking, and alcohol consumption (ever vs. never). Platinum refractoriness (yes vs. no) and irAEs (present vs. absent) had p-values of less than 0.1 on univariate analysis of OS and were also included in multivariate analysis. All statistical analyses were performed using EZR [30 (link)] version 1.51 (Saitama Medical Center, Jichi Medical University, Saitama, Japan), a graphical user interface, together with the R software environment for statistical computing and graphics (The R Foundation for Statistical Computing, Vienna, Austria); p-values of < 0.05 were considered statistically significant.
6
Assessing the Impact of ILND on CSM in SCCP
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The endpoint of interest was CSM. All analyses between ILND vs no ILND were first performed in pT1b and subsequently repeated in pT2-3 SCCP patients. Propensity score matching (PSM) with a 1:3 ratio was applied within pT stage-specific subgroups. Matching variables consisted of age, pT stage, primary tumor treatment, marital status, and geographic region. Survival analyses relied on Kaplan-Meier plots, as well as univariable and multivariable Cox regression models. Covariates consisted of age and treatment of primary tumor. Additionally, sensitivity analyses were performed according to historical and contemporary treatment periods: 2000-2009 and 2010-2018; as well as SEER geographic regions: West and Midwest, Northeast and South. Finally, sample-power analyses [16] addressed pT1b subgroup with limited numbers of observations and events.
In all statistical analyses, R software environment for statistical computing and graphics (R version 4.1.2; R Foundation for Statistical Computing, Vienna, Austria) was used [17] . All tests were two sided, with a level of significance set at p < 0.05. Owing to the anonymously coded design of the SEER database, study-specific ethics approval was waived by the institutional review board.
In all statistical analyses, R software environment for statistical computing and graphics (R version 4.1.2; R Foundation for Statistical Computing, Vienna, Austria) was used [17] . All tests were two sided, with a level of significance set at p < 0.05. Owing to the anonymously coded design of the SEER database, study-specific ethics approval was waived by the institutional review board.
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