How R is used by the FDA for regulatory compliance

I was recently alerted (thanks Maëlle and Mikhail!) to an enlightening presentation from last years' useR! conference. (This year's useR! conference takes place next week in Belgium.) Paul H Schuette, Scientific Computing Coordinator at the FDA Center for Drug Evaluation and Research (CDER), talked about how R is used in the process of regulating and approving drugs at the FDA. 

In what has become a common theme of FDA presentations at R conferences, Schuette refutes the fallacy that SAS is the only software that can be used for FDA submissions, by sponsors such as pharmaceutical companies. On the contrary, he says "sponsors may propose to use R, and R has been used by some sponsors for certain types of analyses and simulations (post-market)."

The myth persists despite the FDA's Statistical Software Clarifying Statement declaring that any suitable software can be used. This is probably because some data-exchange regulations do require the use of the "XPT" (also known as SAS XPORT) file format, but that data format is an open standard and not restricted to SAS. XPT files can be read into R with the built-in read.xport function, and exported from R with the write.xport function in the SASxport package. (If you have legacy data in other SAS formats, here's a handy SAS macro to export XPT files.) The R Foundation also provides guidance on how R complies with other FDA regulations in the document R: Regulatory Compliance and Validation Issues A Guidance Document for the Use of R in Regulated Clinical Trial Environments. 

In addition to sponsors using R in submission, R is also used internally at the FDA. Statisticians there may use the statistical package of their choice, provided it's fit for the purpose. The software used includes SAS, R, Minitab and Stata. Schuette notes that R is used specifically for:

• Statistical review of data analysis in clinical trial submissions. The primary goal here is, "Can we, on our own, replicate the conclusions of the sponsor?"
• Methodology development, innovation and evaluation.
• Graphics (in some cases, the detailed information folded and included with prescription medications feature R graphics).
• Simulations.
• The openFDA initiative, including this LRT Signal Analysis for a Drug Shiny application.

Check out the entire presentation, embedded below.

Channel 9: Using R in a regulatory environment: FDA experiences.