Follow-up periods and time-to-event outcomes were calculated from the first day of SBRT to the day that an event was confirmed. Cumulative LC rate, FFFM rate, RFS rate and OS rate were calculated from the Kaplan–Meier estimator, and then time-to-event outcomes were summarized using the Kaplan–Meier estimator with a log-rank test to compare stratified outcomes. Continuous covariates were divided at the median value to create stratification factors. Regarding BED10 for LC, other cut-off BED10 values of 106 and 150 Gy were used to create three groups: classic standard SBRT dose in Japan (48 Gy in 4 fractions) or less group (<106 Gy), higher than standard dose but less than ablative dose group (106–150 Gy) and ablative dose group (>150 Gy) [5 (link)]. The Kaplan–Meier curves were also described according to this group separation. In multivariate analyses (MVA), the Cox proportional hazards model was applied for factors with a log-rank P-value < 0.20 by using a stepwise backward elimination/forward addition approach with the Akaike information criterion (AIC) to construct the best MVA model. A P-value < 0.05 was defined as significant. Statistical analyses were performed using EZR version 1.37 (Saitama Medical Center, Jichi Medical University, Saitama, Japan), a modified version of R commander (R Foundation for Statistical Computing, Vienna, Austria) [9 (link)].
R commander
Manufactured by R Project for Statistical Computing
Sourced in Japan, Austria
R Commander is a graphical user interface (GUI) for the R statistical computing environment. It provides a menu-driven interface that allows users to access and utilize many of the core functions and capabilities of the R software. R Commander is designed to simplify the use of R, making it more accessible to users who are less familiar with command-line interfaces or programming. It provides a user-friendly way to perform common statistical analyses, data manipulation, and visualization tasks within the R environment.
Automatically generated - may contain errors
Lab products found in correlation
R software, by R Project for Statistical Computing (2 mentions)
R statistical software version 3, by R Project for Statistical Computing (1 mentions)
R version 2, by R Project for Statistical Computing (1 mentions)
Stata statistics data analysis software, by StataCorp (1 mentions)
Ezr software, by R Project for Statistical Computing (1 mentions)
Prism 7, by GraphPad (1 mentions)
Jmp version 15, by SAS Institute (1 mentions)
R statistical software version 4, by R Project for Statistical Computing (1 mentions)
52 protocols using r commander
1
Outcomes of Stereotactic Body Radiation Therapy
2
Identifying Patients with PsPD using ROC Curves
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All statistical analyses were performed using EZR (Saitama Medical Center, Jichi Medical University, Saitama, Japan, version 1.40), which is a graphical user interface for R software (The R Foundation for Statistical Computing, Vienna, Austria, version 3.5.2).18 (link) More precisely, it is a modified version of R Commander (version 2.5-1) designed to add statistical functions frequently used in biostatistics. Receiver operating characteristic (ROC) curve analyses were performed to determine the most appropriate cut-off values for haematological parameters that could identify patients with PsPD. The sensitivity and specificity values were computed to determine the cut-off points that would maximize the sums of the numbers of true positive and true negative predictions. Categorical variables were analysed using Fisher’s exact tests, and continuous variables were analysed using Mann–Whitney U-tests for non-parametric distributions. Overall survival (OS) was estimated using the Kaplan–Meier method and compared using the log-rank test. OS was defined as the time from the date of the initiation of ICI treatment to the date of death due to any cause. Differences were considered significant at p < 0.05.
3
Comparing Patient Characteristics in ED
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Data were presented as mean ± standard deviation (SD), and skewed variables were described as median and interquartile range. We compared patient characteristics upon presentation to the ED between the two groups using the Student t-test or the Mann-Whitney U test. The Fisher’s exact test was used to compare categorical variables. All analyses were performed with EZR (Jichi Medical University, Saitama Medical Center), a graphical user interface for R (v. 2.13.0; The R Foundation for Statistical Computing, Vienna, Austria), and a modified version of the R commander (v. 1.6-3), which was designed to add statistical functions frequently used in biostatistics research [8 (link)]. P < 0.05 was considered statistically significant.
4
Risk Factors for Positive FUBC in GNB Bacteremia
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Fisher exact tests were used to compare categorical variables. Mann-Whitney U tests were used to compare continuous variables. We performed a multivariate logistic regression analysis to identify risk factors for positive FUBC with the same GNB bacteremia. Age, gender, and variables with P < .10 in the univariable analysis were included in the multivariate analysis. These variables were examined for correlation before inclusion in the multivariate analysis. Finally, we calculated the yield of FUBCs by dividing the number of cases found to have positive FUBCs by the number of total episodes of GNB bacteremia that had FUBCs performed. We identified the yields of FUBCs in all patients with FUBCs, and we compared the yields in patients with or without any of the independent risk factors that were identified in the multivariate analysis. A 2-sided P < .05 was considered statistically significant. All statistical analyses were conducted using R commander, a graphical user interface for R (version 3.6.1; The R Foundation for Statistical Computing, Vienna, Austria).
5
Survival Analysis of Patient Groups
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The χ2 test was performed to determine the significance of differences between covariates. Survival durations were calculated using the Kaplan-Meier method and analyzed using the log-rank test to compare the cumulative survival durations in the patient groups. In addition, the Cox proportional hazards model was used to compute the multivariate hazard ratios for the study parameters. In all tests, a P value <0.05 was considered significant. All statistical analyses were performed using EZR (Saitama Medical Center, Jichi Medical University, Saitama, Japan), which is a graphical user interface of R, and more precisely, a modified version of the R commander (The R Foundation for Statistical Computing, Vienna, Austria) (15 (link)).
6
Equilibrium Adsorption Modeling: Freundlich and Langmuir
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Equilibrium adsorption was described using the Freundlich and Langmuir isotherms. The Langmuir equation, assuming one monolayer coverage on equal-energy active sites without interaction of the adsorbed solutes, has the following form:
where qm (mg g−1) is the maximum sorption capacity and KL (L mg−1) is the adsorption constant. The expression of the Freundlich isotherm model defines the heterogeneity of the surface as well as the exponential distribution of the active sites and energies:
where KF (Ln mg1−n g−1) is the Freundlich constant and can indicate uptake capacity, while 1/n (dimensionless) measures favorability of the process.
Modeling of the adsorption curves was performed using nonlinear regression analysis. The adsorption models were fitted to the experimental data using the R statistical software version 3.6.1 [19 ] and the R Commander [20 ] and nlstools packages for R (The R Foundation for Statistical Computing, Vienna, Austria) [21 (link)], which were also used to calculate the significance and quality of the model fits.
where qm (mg g−1) is the maximum sorption capacity and KL (L mg−1) is the adsorption constant. The expression of the Freundlich isotherm model defines the heterogeneity of the surface as well as the exponential distribution of the active sites and energies:
where KF (Ln mg1−n g−1) is the Freundlich constant and can indicate uptake capacity, while 1/n (dimensionless) measures favorability of the process.
Modeling of the adsorption curves was performed using nonlinear regression analysis. The adsorption models were fitted to the experimental data using the R statistical software version 3.6.1 [19 ] and the R Commander [20 ] and nlstools packages for R (The R Foundation for Statistical Computing, Vienna, Austria) [21 (link)], which were also used to calculate the significance and quality of the model fits.
7
Survival Analysis of SBRT Outcomes
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Time-to-event outcomes were calculated from the first day of SBRT to the day on which an event was confirmed. The confirmation of survival outcomes was performed with various methods such as telephone consultation, but other types of confirmation required some medical imaging. Cumulative incidence was calculated using the Kaplan–Meier method, and a log-rank test was used to compare Kaplan–Meier curves. Continuous covariates were divided at the sample median into two groups. A p-value less than 0.05 was defined as significant. Statistical analyses were performed using EZR version 1.52 (Saitama Medical Center, Jichi Medical University, Saitama, Japan), which is a modified version of R commander (R Foundation for Statistical Computing, Vienna, Austria) [11 (link)].
8
Renal Transplant Patients: Osmolality and uEV-AQPs
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Box plots were generated using the BoxPlotR: a web tool for generation of box plots (http://boxplot.tyerslab.com ) [22 (link)]. Differences between renal transplant patients and controls were analyzed by the Mann–Whitney U test or, when the sample mean should be compared with a hypothesized population mean, one sample t-test using EZR (Saitama Medical Center, Jichi Medical University, version 1.29) (http://www.jichi.ac.jp/saitama-sct/SaitamaHP.files/statmedEN.html ) on R commander (version 2.1–7), which is a graphical user interface for R (The R Foundation for Statistical Computing, version 3.2.1) [23 (link)]. Statistical analysis of correlations between osmolality and uEV-AQPs was performed using Pearson’s correlation test. All values were considered to be statistically significant at P < 0.05.
9
Diagnostic Accuracy of Sonographic Lymph Node Assessment
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Data are presented as frequency, median (range), and percentage. The sensitivity, specificity, positive predictive value (PPV), NPV, and DAR were calculated for each LN according to standard definitions. ROC analysis and Youden index (sensitivity + specificity − 1) were used to calculate the optimal cutoff for the SAR value. Based on a two‐sided hypothesis, comparisons between the two groups were examined using the Fisher's exact test or Mann‐Whitney U test. All B‐mode sonographic and EBUS elastographic parameters, which were potentially predictive of malignant LNs,12 , 14 were analyzed with multivariate logistic regression. The Cochran‐Armitage test was used to identify the trend with one‐sided P‐values. P‐values <0.05 were considered statistically significant. The statistical analyses were performed with EZR31 (Saitama Medical Center, Jichi Medical University, Saitama, Japan), which is a graphical user interface for R (The R Foundation for Statistical Computing, Vienna, Austria, v2.13.0), and a modified version of R commander (v1.8–4).
10
Airflow Obstruction in Asthmatic Smokers
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All analyses were performed using EZR (Saitama Medical Center, Jichi Medical University, Saitama, Japan), which is a modified version of R Commander, a graphical user interface for R (The R Foundation for Statistical Computing, Vienna, Austria) designed to add statistical functions frequently used in biostatistics [18 (link)].
The patients were divided into four groups: 1) non-asthmatic non-smokers, 2) non-asthmatic smokers, 3) asthmatic non-smokers, and 4) asthmatic smokers. Numerical data are presented as medians and interquartile ranges, and categorical data as counts and percentages. Continuous variables were compared using the Kruskal–Wallis test, followed by multiple comparison analysis with the Steel test setting non-asthmatic non-smokers as the control. Trends in the prevalence of airflow obstruction according to age group were analyzed using the Cochran–Armitage test. For categorical variables, the chi-squared test or Fisher’s exact test was used, with Bonferroni’s correction for multiple comparisons. Logistic regression analysis was used to compare clinical characteristics of non-smokers and smokers, among either non-asthmatics or asthmatics, after adjustment for age, sex, and % predicted values of FEV1. p < 0.05 was considered statistically significant.
The patients were divided into four groups: 1) non-asthmatic non-smokers, 2) non-asthmatic smokers, 3) asthmatic non-smokers, and 4) asthmatic smokers. Numerical data are presented as medians and interquartile ranges, and categorical data as counts and percentages. Continuous variables were compared using the Kruskal–Wallis test, followed by multiple comparison analysis with the Steel test setting non-asthmatic non-smokers as the control. Trends in the prevalence of airflow obstruction according to age group were analyzed using the Cochran–Armitage test. For categorical variables, the chi-squared test or Fisher’s exact test was used, with Bonferroni’s correction for multiple comparisons. Logistic regression analysis was used to compare clinical characteristics of non-smokers and smokers, among either non-asthmatics or asthmatics, after adjustment for age, sex, and % predicted values of FEV1. p < 0.05 was considered statistically significant.
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