1. New Developments for Using R in the Biopharmaceutical Industry
Paul Schuette
Scientific Computing Coordinator
Office of Biostatistics
FDA/CDER/OTS/OB

2. Disclaimer
This presentation reflects the views of the author and should not be construed to represent the FDA's views or policies.

3. Statistical Software Clarifying Statement
“FDA does not require use of any specific software for statistical analyses, and statistical software is not explicitly discussed in Title 21 of the Code of Federal Regulations [e.g., in 21CFR part 11]. However, the software package(s) used for statistical analyses should be fully documented in the submission, including version and build identification. As noted in the FDA guidance, E9 Statistical Principles for Clinical Trials, ‘The computer software used for data management and statistical analysis should be reliable, and documentation of appropriate software testing procedures should be available.’ Sponsors are encouraged to consult with FDA review teams and especially with FDA statisticians regarding the choice and suitability of statistical software packages at an early stage in the product development process. ” (May 6, 2015)

4. R for Data Visualization
With packages such as shiny, plotly, r2d3, various html tools, R offers more capability for interactive data visualization than most statistical scripting languages.
Can we modernize and improve on traditional tables, figures and listings (TFLs)?

5. Open-Source Tools for Monitoring Clinical Trial Safety, Jeremy Wildfire
DIA-ASA Biopharm Working Group
safetyGraphics R package,
Liver toxicity (DILI)
Nephrotoxity
QTc prolongation
Bone marrow toxicity
Collaboration between clinicians, statisticians, programmers

6. Open Source Tools, Wildfire continued
How do we support the development and maintenance of open source tools?
Government Grants (NIH, FDA, etc.)
Successful application rate typically low
Industry
What’s in it for me?
Academia
Professional Societies
Consortium

7. Developing Powerful Shiny Applications for Life Sciences: Best Practices and Lessons Learned, Eric Nantz
Focus on necessary infrastructure: very helpful to share experiences.
Shiny interfaces
Automation
Best Practices (git for version control, etc.)
Shiny may not be designed for heavy duty computing, but can be enhanced to provide the necessary utility.
Reproducibility (shinymeta)

8. Use of R for Regulatory Submissions, Min Lee
Challenges of using R for regulatory submissions:
Fear of failure
Legacy software/code
Challenges with using R
Base R Version updates
Package Updates
Technical fixes?
Docker
Package Mangers (Packrat, etc.)

9. FDA Experiences
R is currently used by some OB reviewers for regulatory review work
R is widely used in academe, most new reviewers are familiar with R
R has great graphics and data visualization tools
R graphics have shown up on product labels
R Shiny is being used for both internal and external uses
R package SABE is in development, supplements guidance on topic
R/shiny programmers are sought for a new Analytics and Informatics group in the Office of Biostatistics

10. CID Pilot Program Complex Innovative Trial Design (CID) Pilot Program
Joint CDER, CBER 5 year pilot program
PDUFA VI commitment to enhance FDA capability
Trial designs include, but are not limited to:
Complex adaptive designs,
Bayesian designs (including the possibility of an informative prior),
Other novel designs

11. CID Pilot Program continued
“Initial priority will be given to trial designs for which analytically derived properties (e.g., type I error) may not be feasible and simulations are necessary to determine operating characteristics.” Federal Register, Vol. 83, No. 169, Thursday, August 30, 2018, Notices pp

12. CID, continued
A detailed simulation plan and report are expected as part of the CID meeting request and meeting package submissions. “Detailed simulation report that includes the following: a. Example trials in which a small number of hypothetical trials are described with different conclusions. b. Description of the set of parameter configurations used for the simulation scenarios, including a justification of the adequacy of the choices. c. Simulation results detailing the simulated type I error probability and power under various scenarios. d. Simulation code that is readable, adequately commented on, and includes the random seeds. The code should preferably be written in widely-used programming languages such as R or SAS to facilitate the simulation review.” I

13. CID Page

14. Summary
It’s an exciting time for Statistical Programming and Data Science
Interactive Data Visualization for Safety Analyses
Shiny can be computationally complex, as well as simple
Accepting R means having to overcome fear of failure and fear of change.
R is part of FDA efforts to enhance and modernize reviews, and ultimately improve public health

15. Questions?

17. Additional Slides/Backup

18. Product Label https://www. accessdata. fda

19. Another Product Label
R Graphic. Drug for the reduction of elevated intraocular pressure (IOP) in patients with open-angle glaucoma or ocular hypertension

20. Data Anomaly Detection
Use open source software to detect potential data problems
Example of CRADA software output
DABERS: Data Anomalies in BioEquivalence R Shiny app. Used for PK/PD profiles.
Cooperative Research and Development Agreement (CRADA) with CluePoints for detecting anomalous clinical trial sites.