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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 and regulatory

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 from the

pharmaceutical industry, organizations for

health research, and academics. Thorough

scientific, mathematical, editorial and

quality management assessments are

carried out on all documents produced.

Medical writing services include medicinal

and regulatory Writing, scientific

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.

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III. CURRENT TRENDS OF R IN

PHARMA

Looking at current market trends, R

utilization at this juncture is less than 10% in

activities related to Medical Writing

Companies and Pharma Regulatory

Submissions. R is, however, commonly used

in programs in public health, healthcare

economics, and exploratory/scientific

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).

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 & detailed

documentation.

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-

quality plots can be generated, including

mathematical symbols and formulae where

appropriate, is one of R's strengths.

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 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 (Predictive Analysis of

Clinical Trials), SASxport (Read and Write

'SAS' 'XPORT' Files), ADCT (Adaptive

Copyright © 2021 Pubrica. All rights reserved 2

Design in Clinical Trials), ClinPK, cpk

(Clinical Pharmacokinetics

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.

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.

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).

VIII. CONCLUSION

In pharmaceutical firms, medical writing

agencies, and contract research

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 Listings using

the officer and flextable packages and tools

already available (and for great

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.

REFERENCES

1. The R Project for Statistical Computing can be found

at https://www.r-project.org/

2. 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

3. Guidance for the use of R in Regulated Clinical Trial

Environment and R's SDLC process https://www.r-

project.org/certification.html