R is a programming language for statistical computing and graphics. R is open source and developed in academic environment with advanced statistical techniques. RStudio is an integrated development environment (IDE) for R. It is available in two formats: RStudio Desktop is a regular desktop application while RStudio Server runs on a remote server which allows users to access RStudio using a web browser. Generally, companies use web based RStudio.
R is famous for its use in pre-clinical data analysis in drug research. In the comparatively unregulated phases before human subjects are subjected to new compounds, researchers could explore data with a variety of techniques. R’s elasticity and breadth of standard and novel methods for data analysis and graphics makes it an ideal choice to make new discoveries and guide the direction of subsequent clinical trials.
It is in the clinical phases, where human subjects are exposed to prospective new drugs for the first time, where R is less well-known. SAS is currently the dominant software tool for data preparation and statistical analysis in this domain. There is a misunderstanding that SAS is the preferred software of the FDA (which is the US regulatory agency that oversees drug approvals in USA), but in fact, the FDA does not recommend or require use of any specific software.
It is often asked whether R is “validated” for use in clinical trials, but, neither SAS nor R can be considered validated in isolation. That is because it is not software that’s validated, it’s the entire procedure of creating the submission to the FDA is defined as:
Establishing documented evidence which provides a high degree of assurance that a specific process will consistently produce a product meeting its programmed specifications and quality attributes.
Clearly, the software used for the data analysis portion of this process must follow to these same standards, so how can you establish the required detailed proof for R? To that end, the R Foundation has created the document R compliance is defined as “a guidance document for the use of R in regulated clinical trial environments”, the purpose of this document is to validate that R, when used in a qualified fashion, can support the various regulatory requirements for validated systems, most notably 21 CFR Part 11 and the relevant “good practice” documents (GxP) and industry guidance documents.
The document explains the precise software development life cycle for R. The software for the core R engine, plus the base and suggested packages included in the R distribution, is all managed under a secure source code management system accessible only by the R Group (a widely recognized, international team of experts) and the changes between half-yearly releases are clearly documented. To verify that R works as designed and documented, a collection of validation tests compares the output of R against known data and known results.
Naturally, the fact that R itself is maintained with a compliant development process does not complete the whole validation process, since R is always part of a larger process to collect, analyse, and report clinical trial data. (The data collection process usually happens in parallel of the statistical analysis process, and must usually be validated independently)
Once any software is installed for analysis, it must be validated to meet IQ/OQ/PQ criteria.
• IQ refers to Installation Qualification: has the software been installed correctly?
• OQ refers to Operational Qualification: is the software operating correctly?
• Finally, PQ refers to Performance Qualification: is the data analysis process effective and reproducible? (Reproducible analysis in clinical trials is an active area for the R)
There are many companies that offers R based validated computing environment like RStudio, Revolution etc. They provide process documentation and services to support the validation process. This enables the clients in the pharmaceutical sector to use validated R.
The need of programmer who has SAS, Clinical, CDISC standards and R expertise are very limited in industry. We (Rang Technologies) have good pool of consultants with those skills to offer services to our clients.
FDA employees have submitted various papers at statistical conferences on the use of R in clinical analysis. They have used in FDA Analysis and have described the use of R in FDA submission process as well. Statisticians at companies has emphasize on R use in clinical analysis too. And with pharmaceutical companies adopting modern statistical methods and graphical techniques in support of FDA submissions, the use of R is only likely to expand more.