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

1. The Benefits of R Programming in Clinical Trial Data Analysis Dr. Nancy Agnes, Head, Technical Operations, Pubrica sales@pubrica.com In-Brief Medical Writing is an important part of health practice and our team of specialist medical writers offers the best quality and science standards with reliable, timely, and cost-effective clinical materials. By becoming sensitive and versatile to your needs, our medical writers become an extension of your team, leveraging our experience to turn your nuanced and diverse data into a reliable and evidence-based account of your drug's clinical profile in relation to care and patient safety. We have a wide variety of expertise and experience pharmaceutical industry, organizations for health research, and academics. Thorough scientific, mathematical, editorial and quality management carried out on all documents produced. Medical writing services include medicinal and regulatory correspondence, materials for instruction and medical writing consulting services. Keywords: Medical writing services, medical writing solutions, regulatory writing services, Medical Writing Help, Medical Writing Companies, medical Writing consulting services, Medical Writing for clinical trials, medical writing agency I. INTRODUCTION Despite its recent development over the past several 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 its capabilities. However, R is unquestionably building its own niche in the pharmaceutical industry (larger by the day) among these bottlenecks. II. BENEFITS OF R PROGRAMMING IN CLINICAL TRIAL DATA ANALYSIS In recent years, data science has fueled powerful business decisions taken by industry leaders. Data scientists are tellers of stories. 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. In addition to SAS, the most frequently spoken languages in statistics, analytics and visualization are R and Python. This article highlights R challenges observed, suggested approaches for risk assessment of R packages, Clinical Trial Data Analysis mitigation & implementation. and regulatory from the assessments are Writing, scientific Copyright © 2021 Pubrica. All rights reserved 1

2. III. CURRENT TRENDS OF R IN PHARMA quality plots can be generated, including mathematical symbols and formulae where appropriate, is one of R's strengths. Looking at current market trends, R utilization at this juncture is less than 10% in activities related to Medical Writing Companies and Pharma Submissions. R is, however, commonly used in programs in public health, healthcare economics, and research, detection of patterns, Plots/Graphs generation, basic Stat analysis and machine learning. For CDISC (SDTM, ADaM) datasets creation, R is not commonly used. "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 fit all). Regulatory exploratory/scientific IV. REASONS WHY R CAN BE A POTENTIALLY POWERFUL TOOL FOR DATA ANALYSIS R is a statistical computing and graphics language and environment. Under the terms of the GNU General Public License of the Free Software Foundation in source code form, it is available as Free Software. As an open-source program, R enjoys tremendous community support. Availability of source code offers superior documentation. V. R PACKAGES FOR CLINICAL TRIAL DESIGN, MONITORING, AND ANALYSIS R has many packages for medical writing Clinical Trial data analysis. Following are few examples: A table (Create Tables for Reporting Clinical 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 experiments), Pact (Predictive Analysis of Clinical Trials), SASxport (Read and Write 'SAS' 'XPORT' Files), ADCT (Adaptive & detailed Trials), compare R compiles and operates on a wide range of UNIX, Windows and macOS architectures and related systems (including FreeBSD and Linux). 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- dose-finding Copyright © 2021 Pubrica. All rights reserved 2

3. Design in Clinical Trials), ClinPK, cpk (Clinical Toolkit), randomizeR (Randomization for Clinical Trials), Base R (lot of functionality useful for design and analysis of clinical trials), Greport (Graphical Reporting for Clinical Trials), Coronavirus (Provides a daily summary of the Coronavirus (COVID- 19) cases by state/province) etc. VIII. CONCLUSION Pharmacokinetics In pharmaceutical firms, medical writing agencies, and organizations (CROs), R's acceptance as the program of choice is something that many doubted to see over their lifetime. Still, things are progressing quickly, even in the pharmaceutical industry. Nonetheless, there are also many misconceptions regarding R, not least if it is a method appropriate for producing deliverables such as submission- ready TLFs. In this blog, we have seen that R can be an extremely powerful tool to create Tables and the officer and flextable packages and tools already available figures ggplot2 is available), and that by leveraging its high flexibility it is possible to obtain high- quality results with comparable efficiency and quality to standard SAS code. contract research VI. R IMPLEMENTATION IN PHARMA – REAL-TIME EXAMPLES Amgen integrates SAS & R using Microsoft DeployR: 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 was housed at Amgen on various physical servers, integration was required and Microsoft DeployR was therefore selected. DeployR is a technology for integrating into web, desktop, tablet, and dashboard systems for delivering R analytics. 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 to invoke the Java code that is called DeployR Java Client Library. Listings using (and for great REFERENCES 1. The R Project for Statistical Computing can be found at https://www.r-project.org/ A detailed list of R packages for Clinical Trial design, monitoring and analysis can be found at https://cran.r- project.org/web/views/ClinicalTrials.html Guidance for the use of R in Regulated Clinical Trial Environment and R's SDLC process https://www.r- project.org/certification.html 2. 3. VII. CHALLENGES & VALIDATION OF R R is free but it's an investment. The main challenge of using R is ensuring validation documentation. R needs to be programmed (How do we develop software for Clinical science – that enables collaboration across the enterprise and the industry). R has too many Packages (Which packages are validated?). R Packages may come from anywhere & be written by anyone or may not follow a typical SDLC (Software Development Life Cycle). Copyright © 2021 Pubrica. All rights reserved 2