The benefits of R programming in clinical trial data analysis – Pubrica

1. THE BENEFITSOF R PROGRAMMING INCLINICAL TRIAL DATAANALYSIS An Academic presentationby Dr.NancyAgnes,Head,TechnicalOperations,Pubrica Group: www.pubrica.com Email:sales@pubrica.com

2. Today'sDiscussion Outline In-Brief Introduction Benefits of R Programming in Clinical Trial Data Analysis Current Trends of R inPharma Reasons why R can be a Potentially Powerful Tool for Data Analysis R Packages for Clinical Trial Design, Monitoring, andAnalysis R Implementation in Pharma – Real-Time Examples Conclusion

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4. Introduction Despite its recent development over the pastseveral years, the use of R programming in medical writing solutions has not been the most widespread and apparent, its realistic use still seems to be impeded by multiple variables, often due to misunderstandings (e.g. validation) but also due to a lack of knowledge of itscapabilities. However, R is unquestionably building its ownniche in the pharmaceutical industry (larger by the day) among thesebottlenecks.

5. Benefits of R Programming in Clinical Trial Data Analysis In recent years, data science has fueledpowerful business decisions taken by industryleaders. Data scientists are tellers ofstories. They often need to dig into data,clean,transform, create & validate models, understand patterns, generate insights and, most importantly, effectively communicate results in regulatory writing services. Contd...

6. In addition to SAS, the most frequently spoken languages in statistics,analytics and visualization are R andPython. This article highlights R challenges observed, suggested approaches for risk assessment of R packages, Clinical TrialData Analysis mitigation &implementation.

7. Looking at current market trends, R utilization atthis juncture is less than 10% in activities related toMedical WritingCompaniesand PharmaRegulatory Submissions. Current Trends of R in Pharma R is, however, commonly used in programs in public health, healthcare economics, andexploratory/scientific research, detection of patterns, Plots/Graphs generation, basic Stat analysis and machinelearning. For CDISC (SDTM, ADaM) datasets creation, R isnot commonlyused. Contd...

8. "One of the programming community's common questions is, "Will we replace SAS with R or use both or other languages(Python)?". Instead of deciding between SAS or R or Python, I believe that one can make most of these programming languages to solve acceptable data science issues (one size does not fitall).

9. R is a statistical computing and graphicslanguage and environment. Under the terms of the GNU General Public License of the Free Software Foundation in source code form, it is available as FreeSoftware. Reasons why R can be a Potentially PowerfulTool for Data Analysis As an open-source program, R enjoystremendous community support. Availability of source code offers superior &detailed documentation. Contd...

10. R compiles and operates on a wide range of UNIX, Windows andmacOS architectures and related systems (including FreeBSD andLinux). R is strongly extensible and offers a broad range of mathematical (linear and nonlinear simulation, classical statistical experiments, study of time series, grouping, clustering) and graphical techniques. The ease, with which well-designed publication-quality plots can be generated, including mathematical symbols and formulae where appropriate, is one of R'sstrengths. Contd...

12. R has many packages formedical writingClinical Trialdataanalysis. R Packages for Clinical Trial Design, Monitoring, andAnalysis Following are few examples: A table (Create Tables for Reporting Clinical Trials), compare OEM (Comparison of medical forms in CDISC ODM format), CRTSize (Sample size estimation in a cluster (group) randomized trials), Blockrand (creates randomizations for block random clinical trials), DoseFinding (Supports design & analysis of dose-finding experiments), Pact (PredictiveAnalysis of Clinical Trials),etc.

13. AMGEN INTEGRATES SAS & RUSING MICROSOFTDEPLOYR: R Implementation in Pharma – Real- TimeExamples Although SAS was the primary tool at Amgen,R was regarded because of the lack of SAS graph macros(ggplot). As the SAS Grid & R environment washoused at Amgen on various physicalservers, integration was required andMicrosoft DeployR was thereforeselected. DeployR is a technology for integrating into web, desktop, tablet, and dashboardsystems for delivering Ranalytics. Contd...

14. SAS Procedure PROC Groovy allows Groovy code to be run on Java Virtual Machine via SAS Code (JVM). PROC GROOVY is used in this approach toinvoke the Java code that is called DeployR Java ClientLibrary. CHALLENGES & VALIDATION OF R: R is free but it's aninvestment. The main challenge of using R is ensuring validationdocumentation. R needs to be programmed (How do we develop software for Clinical science –that enables collaboration across the enterprise and theindustry). Contd...

15. R has too many Packages (Which packages arevalidated?). R Packages may come from anywhere & be written by anyone or may not followa typical SDLC (Software Development LifeCycle).

16. The roles and responsibilities ofStatisticalProgramming Servicesareoverlapping. All disciplines have distinct focuses,however. Clinical research, systematic reviews, and theworking climate are complex and multidisciplinary in biostatistical activities. Therefore, biostatistics Support Service isessential for fruitful, efficient, and high-quality collaborations to clearly define theresponsibilities. For which the ICH E6 guidance similarly formulatesthe tasks by concerning good clinicalpractice. Conclusion

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