The benefits of R programming in clinical trial data analysis - Pubrica
                     THE BENEFITS OF
R PROGRAMMING IN 
CLINICAL  TRIAL DATA 
ANALYSIS
An Academic presentation by
Dr. Nancy Agnes, Head, Technical Operations, Pubrica 
 Group: www.pubrica.com
Email: [email protected]
Today's Discussion
Outline In-Brief  
Introducti
on
Benefits of R Programming in Clinical Trial Data 
Analysis  Current Trends of R in Pharma
Reasons why R can be a Potentially Powerful Tool for Data 
Analysis  R Packages for Clinical Trial Design, Monitoring, and 
Analysis
R Implementation in Pharma – Real-Time 
Examples  Conclusion
In-Brief
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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.
Benefits of R  
Programmin In recent years, data science has fueled powerful 
g  in Clinical   business decisions taken by industry leaders.
Trial Data  
Data scientists are tellers of stories.
Analysis
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...
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.
Current 
Looking at current market trends, R utilization at this
 Trends juncture is less than 10% in activities related to 
 of R in  Medical  Writing Companies and Pharma Regulatory
Submissions.
Pharma
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.
Contd...
"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).
Reasons
why R can be R is a statistical computing and graphics language 
 and environment. Under the terms of the GNU  
 a Potentially General Public License of the Free Software  
 Powerful Foundation in source code form, it is available as  
Free Software.
Tool  for Data 
 Analysis As an open-source program, R enjoys tremendous
community support.
Availability of source code offers superior & detailed 
 documentation.
Contd...
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.
Contd...

R Packages 
for  Clinical R has many packages for medical writing Clinical
 Trial data analysis.
Trial  Design,  
Monitoring,  Following are few examples: A table (Create Tables 
 for Reporting Clinical Trials), compare OEM  
and Analysis (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), etc.
R Implementation  AMGEN INTEGRATES SAS & R 
 in Pharma – USING
Real-  Time  MICROSOFT DEPLOYR:Although SAS was the primary tool at Amgen, R 
Examples  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.
Contd...
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.
 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).
Contd...
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).
Conclusion
The roles and responsibilities of Statistical Programming
 Services are overlapping.
All disciplines have distinct focuses, however.
Clinical research, systematic reviews, and the working 
 climate are complex and multidisciplinary in  
biostatistical activities.
Therefore, biostatistics Support Service is essential 
 for fruitful, efficient, and high-quality collaborations to  
clearly define the responsibilities.
For which the ICH E6 guidance similarly formulates the 
 tasks by concerning good clinical practice.
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