The software used for statistical analysis was R environment for statistical computing and graphics (R Foundation for Statistical Computing, Vienna, Austria), version 4.1.2 [27 ]. The following parameters were calculated: mean differences, standard deviations (for normally distributed data), medians, upper and lower quartiles (for non-normally distributed data), and ranges. Nevertheless, to help other studies, both types of statistics were used. The assessment of statistical differences before and after the end of treatment was performed using paired t-test (for normally distributed data), Wilcoxon’s signed rank test (for non-normally distributed data), and Stuart-Maxwell (for categorical nominal data). To assess intra-observer reliability, the interclass correlation coefficient was computed, along with a 95% confidence interval. The statistical significance level was set at p ≤ 0.05. For all the statistical tests we used two-tailed p values.
R environment for statistical computing and graphics
Manufactured by R Project for Statistical Computing
Sourced in Austria
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 more. R is widely used in academia and industry for data analysis, visualization, and modeling.
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Lab products found in correlation
11 protocols using r environment for statistical computing and graphics
1
Statistical Analysis of Treatment Outcomes
2
Clinicopathological Features of CoLCNEC
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The associations between clinical, immunophenotypical and molecular features and CoLCNEC groups were assessed using the Fisher exact test or the Kruskal–Wallis test, as appropriate. Correction for multiple comparisons was performed according to Benjamini–Hochberg. Overall survival (OS) was assessed from diagnosis to death or last follow-up by the Kaplan–Meier method. The log-rank test was used to assess the survival difference between patient groups. Cox proportional regression analysis was used to assess the association between clinical-pathological features and OS. Hazard ratios (HR) are presented with a 95% confidence interval (CI).
Data analysis was performed using the R environment for statistical computing and graphics (R Foundation, Vienna, Austria—Version 3.6.2) and MedCalc for Windows version 15.6 (MedCalc Software, Ostend, Belgium). All tests were two-sided and p-values < 0.05 were considered significant.
Data analysis was performed using the R environment for statistical computing and graphics (R Foundation, Vienna, Austria—Version 3.6.2) and MedCalc for Windows version 15.6 (MedCalc Software, Ostend, Belgium). All tests were two-sided and p-values < 0.05 were considered significant.
3
Statistical Analysis of Skewed Data
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Qualitative data was displayed as frequencies and percentages. Skewed continuous data was presented as medians, interquartile ranges, and boxplots with bee swarm plots. Normality of the data was verified with quantile-quantile plots and Shapiro—Wilk tests. Comparisons between groups concerning qualitative data were performed using the chi-squared and Fisher’s exact tests. Comparisons between two independent groups regarding skewed continuous data were performed with the Wilcoxon rank sum test. For all statistical tests, the 0.05 significance level was used and two-tailed p values. All analyses were conducted in an R environment for statistical computing and graphics (R Foundation for Statistical Computing, Vienna, Austria), version 4.1.2 [44 ].
4
Statistical Analysis of Oncological Outcomes
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All statistical analyses were performed using the R environment for statistical computing and graphics (R Foundation, Vienna, Austria). For dichotomous factors and linear vectors the Wilcoxon Mann–Whitney rank sum test was applied. For variance analysis of variables with more than two categories, the Kruskal–Wallis test was performed. Double dichotomous contingency tables (e.g., mutational status to lymph node invasion status) were analysed using Fisher's exact test. To test dependence of ranked parameters with more than two groups (e.g., mutational status and tumour type) Pearson's χ2 test was used. Kaplan–Meier analysis was performed to test associations between mutation status and OS as well as progression-free survival (PFS). The PFS was calculated from the first day of chemotherapy until radiologic progression, death from any cause or the last time of follow-up without progression. Overall survival was defined as the time from date of diagnosis until the date of death or last follow-up. Patients were censored at the last follow-up if still alive or loss of follow-up. Surveillance of PFS and OS was stopped on 31 August 2014. Significant differences in PFS or OS between groups were verified by the Wald test, likelihood ratio test and Score (logrank) test (Supplementary Table S3 ). The level of statistical significance was defined as P⩽0.05.
5
Statistical Analysis of Categorical and Continuous Data
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Categorical variables were presented by counts and percentages, while comparisons between two independent groups were made with the chi-square test or Fisher’s exact test (when the expected frequencies were low). Continuous skewed variables were presented by medians and interquartile ranges, while comparisons between two independent groups were performed using the Mann–Whitney U test. For all statistical tests, a significance level of 0.05 was used, and the two-tailed p-values were computed. All statistical analyses were carried out with the R environment for statistical computing and graphics (R Foundation for Statistical Computing, Vienna, Austria), version 4.0.2.
6
Statistical Analysis for Research Data
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For all statistical tests used, the significance level alpha was 0.05, and the two tailed p value was computed. All statistical analyses were performed in R environment for statistical computing and graphics (R Foundation for Statistical Computing, Vienna, Austria), version 3.4.3 [R Core team. Vienna, Austria].
7
Statistical Analysis of Qualitative and Quantitative Data
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Qualitative data are presented by counts and percentages, and normally distributed continuous data as means and standard deviation. Associations between qualitative variables were checked with Chi-square test. Comparisons between two groups regarding normally distributed continuous data were performed with independent samples t-test. Tests were presented as two-tailed P-value, of 0.05 level of confidence. The statistical analysis was made using the program R Environment for statistical computing and graphics (R Foundation for Statistical Computing, Vienna, Austria) version 3.2.1.
8
Survival Analysis of Clinical Cohort
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Regarding prognosis, the overall survival was defined as the time from treatment until death or the study ending date (November 2020). Disease-free survival was defined as the time from treatment until recurrence or the study’s end date (November 2020). Further, known predictors for survival were added as adjusting variables in multivariate Cox regression models. The proportional hazard assumption was checked with a formal statistical test for all models, while the linear functional form for continuous variables was checked using model residuals plots inspection. For multivariate models, multicollinearity was checked with variance inflation factors. The two-tailed p-value was computed for all statistical tests, and the results were statistically significant for values below 0.05. Data were analyzed using the R environment for statistical computing and graphics (R Foundation for Statistical Computing, Vienna, Austria), version 3.6.3 [R Core Team. R: A Language and Environment for Statistical Computing and IBM SPSS Statistics 25.0.] (IBM, Armonk, NY, USA).
9
Effectiveness of SRP plus HY Treatment
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For each of the test (SRP plus HY) and control (SRP) groups, 60 quadrants were analyzed. The qualitative data were presented as absolute and relative frequencies. Normally distributed quantitative data were presented as means and standard deviations, while non-normally distributed data were presented as medians and interquartile ranges. The method of analyzing the results was intention-to-treat. Comparisons between baseline and follow-up, as well as comparisons for dependent samples for normally distributed data, were performed with a t-test for dependent samples and Wilcoxon signed-rank test for data not following the normal distribution. For all outcomes, a superiority hypothesis was used. The means of the differences between observations were presented, along with 95% confidence intervals. For all statistical tests, a 0.05 level of significance was used, as well as a two-tailed p-value. No corrections for multiple testing were employed. All analyses were carried out with the R environment for statistical computing and graphics (R Foundation for Statistical Computing, Vienna, Austria), version 4.1.2 [35 ].
10
Analyzing Joint Effusion with Ultrasonography
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The receiver operator characteristic curve (ROC) was plotted for the presence of the joint effusion identified with MRI, using the capsular width (mm) as measured by 20 MHz US. Its chart was plotted along with a 95% confidence interval computed by bootstrapping. The best cut-off was computed by identifying the best Youden index (sensitivity + specificity—1). A table with all the cut-off values along with all sensitivities and specificities was computed. All statistical analysis was carried out with the R environment for statistical computing and graphics (R Foundation for Statistical Computing, Vienna, Austria), version 3.4.3 [21 ].
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