R language platform

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R is an open-source programming language and 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 others.

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R language (52 citations) R platform (27 citations)

5 protocols using r language platform

Statistical Analysis of Questionnaire and PRM

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Statistical analyses were performed using SPSS 26.0 software (IBM SPSS Statistics, Armonk, NY) and R language platform (Version 4.0.0, R foundation for Statistical Computing). The parameters for the questionnaire survey and PRM were compared between groups using Student’s t-tests and one-way ANOVA for normally distributed values and using Mann–Whitney U-tests and Kruskal Wallis tests for non-normally distributed data. The classification models were constructed with the class “Random Forest Classifier ()” from the Scikit-learn toolkit, Version 0.23.2 (Python Software Foundation, Fredericksburg, VA); (

https://scikit-learn.org/stable/index.html

).17 Programming was based on the Python 3.6.12 platform (Python Software Foundation). All reported p-values were two-sided with a 0.05 significance level.

Pu Y., Zhou X., Zhang D., Guan Y., Xia Y., Tu W., Lu Y., Zhang W., Fu C.C., Fang Q., de Bock G.H., Liu S, & Fan L. (2022). Re-Defining High Risk COPD with Parameter Response Mapping Based on Machine Learning Models. International Journal of Chronic Obstructive Pulmonary Disease, 17, 2471-2483.

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Evaluating Retinal Layers in Primary Open-Angle Glaucoma

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Statistical analyses were performed using R Language Platform (version 3.6.2; R Foundation). The Shapiro-Wilk test was used to test the normal distribution of continuous variables. To compare the two POAG groups, a two-tailed t-test was used for normally distributed continuous variables assuming unequal variance and the Wilcoxon rank-sum test was used for non-normally distributed continuous variables. Categorical variables were compared with Fisher exact test. Bonferroni correction was used for the comparison between three groups (control, paracentral and periphery VF loss). Statistical significance was defined at P < 0.05. Inter-reader agreement was assessed using intraclass correlation coefficients (ICC). Pearson correlations between affected PaTD and OCTA parameters or RNFL thickness were calculated for all POAG eyes.
The correlations between affected hemispheric OCTA parameters and RNFL thickness were also assessed in POAG groups. Additionally, partial correlations between affected PaTD, or PeTD and OCTA parameters or RNFL thickness adjusting for age were evaluated. The bootstrapping procedure was applied to calculate the statistical distributions of the correlations and partial correlations, and the two-tailed t-test was subsequently used to compare whether two partial correlation distributions were significantly different.

Nascimento e Silva R., Chiou C.A., Wang M., Devlin J., Li D., Lovelace S., Wang H., Greenstein S.H., Brauner S.C, & Shen L.Q. (2020). Quantification of the peripapillary microvasculature in eyes with glaucomatous paracentral visual field loss. Ophthalmology. Glaucoma, 4(3), 286-294.

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Statistical Analysis of Continuous and Categorical Variables

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Data analysis was performed using R Language Platform (Version 3.4.3; R Foundation, Vienna Austria). The 2-tailed student t test was used to test group differences for continuous variables. The Fisher exact test was used to compare frequencies of categorical variables between groups. For subgroup analysis, 1-way analysis of variance (ANOVA) was performed to compare continuous variables between 3 groups with Bonferroni correction for multiple comparisons. Interreader reproducibility was assessed using the intraclass correlation coefficients (ICC). P < 0.05 was considered significant.

Nascimento e Silva R., Chiou C.A., Wang M., Wang H., Shoji M.K., Chou J.C., D’Souza E.E., Greenstein S.H., Brauner S.C., Alves M.R., Pasquale L.R, & Shen L.Q. (2019). Microvasculature of the Optic Nerve Head and Peripapillary Region in Patients With Primary Open-Angle Glaucoma. Journal of glaucoma, 28(4), 281-288.

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Statistical Analysis of COPD and Non-COPD Data

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All the statistical analyses were performed using the R language platform (Version 4.0.0, R Foundation for Statistical Computing, Vienna, Austria). To compare the distribution of the categorical variables between the COPD and non-COPD data, we used the Chi-squared test. To compare the distribution of the quantitative variables between the COPD and non-COPD data, we first tested if the data distribution followed a normal distribution using the Shapiro-Wilk Test. If the distribution was normal (P>0.05 for the Shapiro-Wilk test), we used the Student’s t-test for further analysis; otherwise, the Mann-Whitney U test was used. In terms of the classification model performance, we compared the accuracy, sensitivity, specificity, the highest negative predictive value (NPV), positive predictive value (PPV), and AUC values of different models using the DeLong test. In all the statistical analysis, a P value less than 0.05 was considered statistically significant.

Guan Y., Zhang D., Zhou X., Xia Y., Lu Y., Zheng X., He C., Liu S, & Fan L. (2024). Comparison of deep-learning and radiomics-based machine-learning methods for the identification of chronic obstructive pulmonary disease on low-dose computed tomography images. Quantitative Imaging in Medicine and Surgery, 14(3), 2485-2498.

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OCTA and Nailfold Metrics in POAG

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Statistical analysis was conducted using R Language Platform (Version 3.4.3; R Foundation, Vienna, Austria). Interreader reproducibility was measured through intraclass correlation coefficients (ICCs) for all OCTA and nailfold measurements. The mean values of measurements by both readers were used for analyses. Two-tailed Student's t-test was used to test group differences for continuous variables, and Fisher's exact test was used to compare differences in frequencies of categorical variables between groups. Multiple linear regression analyses were performed with OCTA and nailfold metrics as dependent variables to assess the difference between POAG and controls while accounting for age and gender as covariates; controls served as the reference group. Multiple linear regression was applied to assess the partial Pearson's correlations between nailfold and OCTA measurements among all participants and was adjusted for age and the time between two imaging visits. In the POAG group, partial Pearson's correlations between the same metrics were obtained while adjusting for HVF MD and the time between two imaging visits. P value correction for multiple comparisons by the false discovery rate (FDR) method was applied for both univariable and multivariable analysis, and FDR-corrected P < 0.05 was considered significant.⁴¹ (link)

Shoji M.K., Cousins C.C., Saini C., Nascimento e Silva R., Wang M., Brauner S.C., Greenstein S.H., Pasquale L.R, & Shen L.Q. (2021). Paired Optic Nerve Microvasculature and Nailfold Capillary Measurements in Primary Open-Angle Glaucoma. Translational Vision Science & Technology, 10(7), 13.

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