R is a full-featured and mature environment for clinical data analysis. However, a common misperception exists that R cannot support the various regulatory requirements for clinical data analysis. This presentation provides an overview of proceedings at useR! conferences and elsewhere regarding the acceptability of use of R in regulated environments, e.g., clinical trials for pharmaceuticals and medical devices.

1. Challenges in Clinical Data
Analysis with R
Ian Cook
Raleigh-Durham-Chapel Hill R Users Group
January 24, 2013

2. Background
• R is a full-featured and mature environment for
clinical data analysis
• Significant use of R by industry in non-regulated
environments, but R has been slower to
penetrate regulated environments, e.g. clinical
trials, financial services
• Common misperception exists that R cannot
support the various regulatory requirements for
validation/qualification

4. FDA Regulatory Guidance
• 21 CFR Part 11 - Electronic Records; Electronic Signatures
• Validation of systems to ensure accuracy, reliability and consistent
intended performance
• Paper equivalence in deployed environment
• 21 CFR Part 58 - Good Laboratory Practice (GLP)
• 21 CFR Part 312 - Good Clinical Practice (GCP)
• 21 CFR Part 210 - Current Good Manufacturing Practice
(cGMP)
• Guidance for Industry - Computerized Systems Used in
Clinical Investigations (2007)
• General Principles of Software Validation; Final Guidance for
Industry and FDA Staff (2002)

5. JSM : Use of R in Regulated Environment
• FDA talk with Sue Bell at JSM 2006:
“Times „R‟ A Changing: FDA Perspectives on
Use of „Open Source‟”
• No regulation prohibiting open source
• FDA narrowly interprets and enforces 21 CFR 11
• Software installation, operation and performance must be
“qualified” to be reproducibly installed and tested to ensure
accuracy, reliability and consistent intended performance in
regulated company’s environment (IQ/OQ/PQ)
• FDA expects to be able to reconstruct a clinical study submitted
to the agency
• FDA may ask to see the regulated company’s documentation
that demonstrates software qualification

6. useR! : Use of R in Regulated
Environment
• useR! 2007 session hosted by Marc Schwartz
• Perception of SAS as the gold standard, but reasons to be
optimistic about future of R in regulated environments
• useR! 2007 Frank Harrell talk
• Validation should encompass practices to prevent user
error
• R includes tools to eliminate tedious low-level commands
and manual actions, resulting in fewer sources of error
• useR! 2007 Anthony Rossini and David A.
James (Novartis) talk
• R presents risks in regulated environments, but
manageable given proper validation and qualification
procedures

7. useR! : Use of R in Regulated
Environment (continued)
• useR! 2007 Mat Soukup (FDA) talk
• FDA Reviewer Expectations/Requests for
submissions generated with R
• What R functions are used and where do they reside (base
vs. user-contributed packages)?
• Have the R functions been properly validated in user
environment?
• Can the validation tests be reproduced?
• Is there any certification of the validation test?
• Are there any known data structures which can potentially
alter results?

8. useR! : Use of R in Regulated
Environment (continued)
• Announcement of document from the R
Foundation at useR! 2007:
“R: Regulatory Compliance and Validation
Issues: A Guidance Document for the Use of
R in Regulated Clinical Trial Environments”
• Document addresses significant hurdles, but
burden of implementation on users
http://www.r-project.org/doc/R-FDA.pdf

9. useR! : Use of R in Regulated
Environment (continued)
• useR! 2011 Ian Cook and Michael O’Connell
(TIBCO) talk
• Framework for successfully complying with regulatory
software validation requirements when using R
• useR! 2012 Jae Brodsky (FDA) talk
• Drug developers may use R in their FDA
submissions.
• "R use at the FDA is completely acceptable and has
not caused any problems."

10. Learning More
“Clinical Trial Data
Analysis Using R”
(2010)
• Presents methods for
analysis of clinical trial
data
• Shows step by step how
to implement the
statistical methods using
R
http://www.crcpress.com/pro
duct/isbn/9781439840207