R statistical programming software

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 variety 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 and visualization.

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4 protocols using r statistical programming software

Meta-analysis of Surgical Technique Outcomes

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All meta-analyses were performed with R statistical programming software (version 3.6.1; R Foundation for Statistical Computing, Austria), using the “meta” package. The I² statistic was used to characterize heterogeneity [23 (link)]. The fixed-effect model was used where there was no evidence of significant heterogeneity between studies (I² statistic < 50%), or a random-effects model when heterogeneity was likely (I² statistic ≥ 50%). For dichotomous outcomes, the odds ratio (OR) was reported as the summary statistic. For continuous outcomes, the mean difference was reported.
For the following outcome measures assessed with the SLR, meta-analysis could not be performed: number of trays; operating room turnover time; return to function. However, descriptive analyses were undertaken to compare the impact of VISIONAIRE guides versus conventional instrumentation on these outcomes.

Tibesku C.O., Haas S.B., Saunders C, & Harwood D.A. (2022). Comparison of clinical outcomes of VISIONAIRE patient-specific instrumentation with conventional instrumentation in total knee arthroplasty: a systematic literature review and meta-analysis. Archives of Orthopaedic and Trauma Surgery, 143(7), 4379-4393.

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Evaluating STOP-Bang Test for Severe OSA

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The patients were divided by presence or absence of severe OSA, and clinical features obtained from the questionnaire were compared. We present continuous variables as median and range and categorical variables as numbers and percentages. We compared continuous variables by Wilcoxon rank-sum test. We compared categorical variables using the χ² test when appropriate; otherwise, we used Fisher’s exact test.
The receiver operating characteristic (ROC) curve analysis was performed to evaluate the diagnostic value of the STOP-Bang test for detecting severe OSA and determining the best cutoff value. We used Spearman's rank method to examine the correlation between the STOP-Bang score or ESS and AHI. To detect the independent risk factor of severe OSA, we performed multivariate logistic regression analyses with backward elimination using the categorical variables that had shown statistical significance in univariate analyses.
All analyses were performed using EZR (Saitama Medical Center, Jichi Medical University, Saitama, Japan) ^{(17) (link)}, a graphical user interface for R statistical programming software (The R Foundation, Vienna, Austria). We regarded a P value under 0.05 as statistically significant.

Oshita H., Ito N., Senoo M., Funaishi K., Mitama Y, & Okusaki K. (2020). The STOP-Bang Test Is Useful for Predicting the Severity of Obstructive Sleep Apnea. JMA Journal, 3(4), 347-352.

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Metabolomic Analysis of Breast Cancer

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All the downstream processing and data analyses were performed using R statistical programming software (R Foundation for Statistical Computing, Vienna, Austria) [37] . After removing the compounds with more than 40% of missing values, the missing values in the remaining metabolites in the BCa cell lines were imputed using the K-Nearest Neighbour (KNN) algorithm (“imputation” package [38] , K = 5). Following the preprocessing, of 673 compounds, the BCa cell line has 76 were unique metabolites, whereas our recently published BCa tissue metabolome data set [39] (link) had a total of 219 compounds of which 168 metabolites were named.
Imputed data were median centered and Inter Quartile Range (IQR) scaled following log₂ transformation. Two-sided t tests were performed to identify differential metabolites by comparing luminal and basal subtypes coupled with False discovery rate (FDR) adjustment (adjusted P values < .2) using the Benjamini Hochberg (BH) method [40] along with estimated fold change using the Differential Expression via Distance Summary (DEDS) package [41] .

Putluri N., Maity S., Kommangani R., Creighton C.J., Putluri V., Chen F., Nanda S., Bhowmik S.K., Terunuma A., Dorsey T., Nardone A., Fu X., Shaw C., Sarkar T.R., Schiff R., Lydon J.P., O’Malley B.W., Ambs S., Das G.M., Michailidis G, & Sreekumar A. (2014). Pathway-Centric Integrative Analysis Identifies RRM2 as a Prognostic Marker in Breast Cancer Associated with Poor Survival and Tamoxifen Resistance. Neoplasia (New York, N.Y.), 16(5), 390-402.

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Evaluating Computer-Aided Polyp Detection

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We assessed the changes in polyp detection performance for each group according to the CADe assistance, using accuracy, sensitivity, and false positive rate (FPR) as evaluation metrics. The difference in the effects of CADe assistance based on participant groups and polyp characteristics was also evaluated. Additionally, we compared the change in polyp localization performance according to the CADe assistance using the intersection over union (IoU) to measure localization accuracy. An IoU value <0.5 indicated the polyp absence.
The changes in performance metrics (accuracy, sensitivity, FPR, and IoU) were compared using a one-sample proportional test. A generalized linear mixed-effect model was used to measure the effect of CADe on the polyp detection performance. A receiver operating characteristics curve was also plotted to evaluate the detection performance of our CADe system. Statistical significance was set at p<0.05. Categorical variables are presented as frequency counts and percentages. Continuous variables are expressed as means and standard deviations. All statistical analyses were performed using the R statistical programming software (R Core Team 2022; R Foundation for Statistical Computing, Vienna, Austria, http://www.R-project.org).

Lee J., Cho W.S., Kim B.S., Yoon D., Kim J., Song J.H., Yang S.Y., Lim S.H., Chung G.E., Choi J.M., Han Y.M., Kong H.J., Lee J.C., Kim S, & Bae J.H. (2024). Impact of User's Background Knowledge and Polyp Characteristics in Colonoscopy with Computer-Aided Detection. Gut and liver.

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