R for statistical computing and graphics

Manufactured by R Project for Statistical Computing

R is an open-source software environment for statistical computing and graphics. It provides a wide range of statistical and graphical techniques, and is widely used in various fields for data analysis, visualization, and modeling.

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R environment for statistical computing and graphics (11 citations) R software environment for statistical computing and graphics version 3.5.2 (7 citations) R software environment for statistical computing and graphics (6 citations)

2 protocols using r for statistical computing and graphics

COVID-19 Outcomes and Biomarkers

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Categorical variables are summarised with counts and percentages, continuous variables with median and interquartile range (IQR) (non-normal distribution). All statistical analyses were performed using R for statistical computing and graphics (version 3.6.2, R Foundation, Vienna, Austria). Subgroup analyses were performed using the nonparametric Mann-Whitney U test for (unpaired) continuous variables and a Fisher’s exact test for categorical variables. The following subgroups were defined: intensive care unit (ICU) admission, pulmonary embolism, elevated Troponin T and elevated NT-proBNP. At the time of statistical analysis seven patients were still hospitalised.

van den Heuvel F.M., Vos J.L., Koop Y., van Dijk A.P., Duijnhouwer A.L., de Mast Q., van de Veerdonk F.L., Bosch F., Kok B., Netea M.G., Hoogerwerf J., Hoefsloot W., Tjwa E.T., de Korte C.L., van Kimmenade R.R, & Nijveldt R. (2020). Cardiac function in relation to myocardial injury in hospitalised patients with COVID-19. Netherlands Heart Journal, 28(7-8), 410-417.

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Evaluating Cardiovascular Event Reduction

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Based on Dutch national data, we expect 41% of the patients to develop the primary event in the control group and 30% in intervention group.²⁶
^,²⁷ To test this difference at a P value of .05 and power of 80%, we need a total of 592 evaluable patients. We will perform 2 interim-stage analyses and 1 final-stage analysis for efficacy according to the Peto-Haybittle method. We will use predefined type I error boundary points with P values of .001 at each interim analysis and P value of .05 at the final analysis. First interim analysis will be performed when data are available on 200 patients, second when data are available on 400 patients, and final when data are available on all 592 patients. Anticipating a dropout rate of 10%, we aim to enroll 651 patients. Analyses will be performed using R for statistical computing and graphics (R Foundation, Vienna, Austria).

Gommans D.H., Nas J., Pinto-Sietsma S.J., Koop Y., Konst R.E., Mensink F., Aarts G.W., Konijnenberg L.S., Cortenbach K., Verhaert D.V., Thannhauser J., Mol J.Q., Rooijakkers M.J., Vos J.L., van Rumund A., Vart P., Hassing R.J., Cornel J.H., de Jager C.P., van den Heuvel M.M., van der Hoeven H.G., Verbon A., Pinto Y.M., van Royen N., van Kimmenade R.R., de Leeuw P.W., van Agtmael M.A., Bresser P., van Gilst W.H., Vonk-Noordergraaf A., Tijssen J.G., van Royen N., de Jager C.P., van den Heuvel M.M., van der Hoeven H.G., Verbon A., Pinto Y.M, & van Kimmenade R.R. (2020). Rationale and design of the PRAETORIAN-COVID trial: A double-blind, placebo-controlled randomized clinical trial with valsartan for PRevention of Acute rEspiraTORy dIstress syndrome in hospitAlized patieNts with SARS-COV-2 Infection Disease. American Heart Journal, 226, 60-68.

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