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Transcript of The benefits of R programming in clinical trial data analysis – Pubrica
Copyright © 2021 Pubrica. All rights reserved 1
The Benefits of R Programming in Clinical Trial Data Analysis
Dr. Nancy Agnes, Head,
Technical Operations, Pubrica
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.
Copyright © 2021 Pubrica. All rights reserved 2
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