Statistical analyses were performed using R statistical programming (R Foundation for Statistical Computing, Vienna, Austria). Shapiro–Wilk test for normality was used to evaluate the normality of data within each ROI. Levene’s test for homogeneity of variance was used to examine the normality of data across patients, followed by ranked two-way repeated-measures analysis of variance (ANOVA) to evaluate the effects of tissue type and diffusion components. Individual differences were evaluated by post hoc Wilcoxon signed-rank test with Bonferroni correction to preserve rigor. The threshold for significance (α) was set at 0.05 for all analyses. Results are reported as median and interquartile range values.
R statistical programming
Manufactured by R Project for Statistical Computing
Sourced in United States
R is a free, 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.
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Lab products found in correlation
6 protocols using r statistical programming
1
Diffusion Component Analysis in Tissue Types
2
Optimizing Cardiovascular Risk Management
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Distributions of predicted 10‐year and lifetime risk of a (recurrent) CVD event were stratified according to history of CVD. A high CVD risk was defined as a 10‐year risk of CVD of more than 10%10 and a lifetime risk of CVD as greater than 50%. Distributions of the use of preventive GLAs with proven CV benefit (GLP‐1 RAs and SGLT‐2is) were stratified by history of CVD and according to deciles of predicted lifetime CVD risk. Distributions of the use of CVRM medications (antihypertensive medication, statins and aspirin) were assessed in the same way.
Distributions of numbers of life‐years gained without a (recurrent) CVD event with optimal CVRM and the addition of GLP‐1 RA and SGLT‐2i were stratified by history of CVD and assessed according to deciles of predicted lifetime risk. All analyses were performed with R‐statistical programming (version 4.0.3; R Foundation for Statistical Computing, Vienna, Austria).
Distributions of numbers of life‐years gained without a (recurrent) CVD event with optimal CVRM and the addition of GLP‐1 RA and SGLT‐2i were stratified by history of CVD and assessed according to deciles of predicted lifetime risk. All analyses were performed with R‐statistical programming (version 4.0.3; R Foundation for Statistical Computing, Vienna, Austria).
3
Feasibility of Health Coach Intervention for Breast Cancer Survivors
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Descriptive statistics were used to describe participant demographics and breast cancer characteristics. Except where stated otherwise, continuous variables were presented in mean (SD), categorical variables were presented as number (n) and percent (%), and the statistical type I error (α-level) was set at .05. Feasibility was calculated as the percentage of participants that (1) completed all 7n health coach sessions and (2) completed the 3-month final assessment compared to the baseline enrollment (n=20). Linear mixed models (LMM) with participant-level random intercept were fitted by repeated measures of outcome and fixed effects of the visit. LMM analyses were performed to investigate the intervention effect on physical function, PROMIS, sedentary behavior, and physical activity measures from baseline to the 3-month assessment. The coefficient (β) is an estimation of intervention effect from the baseline to the 3-month visit. All analyses were performed in R statistical programming (R Foundation for Statistical Computing) [56 ] language and LMM was implemented in R package nlme [57 ,58 ].
4
Pathologists' Diagnostic Agreement Analysis
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To analyze the diagnostic agreement among pathologists, the Fleiss’ kappa coefficient was used. The analysis was performed using R statistical programming (version 3.4.1; http://www.r-project.org , accessed on 15 February 2024).
5
Prognostic Factors in Cancer Survival
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The Chi-squared (χ2) test and Fisher’s exact test were used to compare low- and high-grade calculated N2 (cN2) and TSR data with clinicopathological factors. Continuous data were converted to categorical variables using cutoff values, where the sum of the sensitivity and specificity was maximized for the prediction of DFS using a time-dependent receiver operating characteristic (ROC) curve. For multivariate cox regression analyses, we used the variables that were significantly associated with DFS in a Kaplan—Meier curve analysis by log-rank tests. To minimize overfitting [27 (link)], we used lymphovascular perineural invasion (LVPI) as a parameter, which is calculated by combining lymphatic invasion, venous invasion, and perineural invasion instead of using each factor as an independent variable. Age, TNM stage, tumor size, and LVPI were used as compounding factors, to which cN2 and TSR were added one by one. Two-sided p values < 0.05 were considered statistically significant. All of the analyses were performed using SPSS software (version 20.0; IBM, Armonk, NY, USA) and R statistical programming (version 3.4.1; http//www.r-project.org , accessed on 7 March 2022).
6
SEIQHRF Epidemic Modeling in R
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The SEIQHRF model was implemented using R statistical programming (The R Project for Statistical Computing. https://www.R-project.org/ ). Twenty simulation runs were undertaken and estimates from each were averaged to obtain the final estimates. Considering the computational intensity of running the simulations, parallel processing was employed using four computer processing units, with the model having a runtime of 365 days. We extracted the distributions of the timing of transitions to various compartments. This was done as a check to confirm that they were reasonable for the transition parameters that they represented and by defining a function that extracted the timing from the simulation results object. Then, we plotted the timing for visualisation. The modelling results were compared to those of the actual trajectory of the epidemic in Saudi Arabia.
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