Leveraging R and Shiny for Point and Click ADaM Analysis

1. Leveraging R and Shiny for Point and Click ADaM Analysis Ian Fleming and Fred Hofstetter NJ CDISC User Group January 2015

2. Agenda • Lifecycle of a TFL • The Promise of Standards • Overview of the Tool • ADaM Viewer Demonstration • Q&A

3. Lifecycle of a TFL

4. How does Pharma get here?

5. How do we get to a TFL?

6. Extensive Process • Transitions from one form to another require significant effort • Significant amount of single use programs • Use of “Validated Systems” • Typically SAS Macro based infrastructure • Company specific infrastructure

7. The Promise of Standards • CDISC formed in 1997 • “to develop and support global, platform-independent data standards that enable information system interoperability to improve medical research and related areas of healthcare.”

8. Jetpacks

9. Fundamental Question • Why hasn’t standards adoption brought the levels of efficiency that we were expecting • Tools? • The standards? • The Industry? • How do we explore the cause?

10. Rapid Prototyping • Originated in manufacturing • Facilitates real world testing of solutions • Development occurs through iteration • De-facto standard methodology for web development

11. Rapid Prototyping

12. The ADaM Viewer

13. Motivations • Proof of Concept for Rapid Prototyping methodology • The ability to build standard tools off of ADaM data • The feasibility of R and Shiny for this type of work

14. Brainstorming Requirements • Ability to read in ADaM submission transport files • Ability to produce minimal set of standard summaries • Point and click interface – no end user programming required • No install needed • FREE!

15. CDISC Tools • Lots of tools for some standards • CDASH (EDC systems, standard CRFs, etc.) • ODM (in/out from different data collection systems) • SDTM (validation, data visualization tools)

16. ADaM

17. Technology Options • SAS? • Need license(s) • No quick/easy point and click without other tools • Extensive knowledge of SAS stack needed • Java? • Lot of coding • Steep skill set • R?

18. R • Early History – 1990 • Ross Ihakaand Robert Gentleman • Department of Statistics at the University of Auckland • Open source statistical analysis software based on S programming language • Package based • Functional specific extensions

19. R: Early History • https://www.stat.auckland.ac.nz/~ihaka/downloads/Massey.pdf • If you want to know more…

20. Shiny • Web application framework for R • Package installed in R • Interactive data analysis with real time code execution based on user input • Web technology without having to know web technology • Minimal Infrastructure requirements

21. 3 weeks later… Prototyping complete Fully functional prototype • Ability to read in ADaM submission transport files  • Ability to produce standard types of summaries • Point and click interface  • No install needed 

22. Demonstration

23. Results • Rapid Prototyping  • 3 weeks from concept to full prototype • 2 resources working in their spare time • Standard tool for ADaM Analysis  • Consistently create results across any ADaM data • R and Shiny  • Very easy to create and deploy

24. Additional Benefits • Ability for non-technical people to look at analyses • Removing roadblocks to data • Ad-hoc confirmation of current analyses • Easily extendable • Easily accessible • Low/No cost

25. Summary • Rapid prototyping is a valuable tool • Next step: incorporate into our development process and interactions with users • R provides tools and packages for quick and powerful application development • Next step: how can we leverage this on a larger scale? • Able to produce easy point and click analysis for ADaM • Next step: Options for a universally available solution?

26. Questions