There are likely two “high-level” issues to consider and I will defer to others with more “hands on” interaction with the FDA on this topic.
The first is, irrespective of software implementation, the acceptance by the FDA and other regulatory agencies, of Bayesian methods in submissions. As you are probably aware, there is 2010 guidance from the FDA that addresses this at various levels:
That being said, there is relatively new draft guidance, which includes discussion of Bayesian methods, that if you are not aware of, you may want to review:
This draft document begins to provide insights into current thinking within the FDA, that is more recent than the 2010 document. That may help you to identify challenges on the FDA side relative to general acceptance.
The second issue is software, and as you likely know, there is the 2015 FDA statistical software clarifying statement:
which makes it clear that the FDA does not require (nor endorse or validate) any specific software, despite long-standing perceptions to the contrary.
Validation is essentially all on the end-user, not on the software publisher. That is, there is no “Good Housekeeping Seal of Approval” stamp on any software, physically or virtually, that obviates the burden on the end user. So, a lot of the software side of things comes down to the end-user having internal SOPs (e.g. IQ/OQ/PQ) that are relevant in this domain. The scope of these SOPs will be largely driven by how risk averse is the environment that the end-user is operating within.
That does not mean that the software publisher, author, maintainer and indeed the community at large, cannot assist in that process by creating and making available tools and procedures that can streamline the process for the end-user. They need to be motivated, given the extra burden involved, to support the use of these tools within this specific domain, which may only represent a small proportion of their user base.
Some years ago (circa 2006-ish), a small working group, including Frank and me, drafted a guidance document on the use of R in regulatory submissions, which has been updated over time:
The intent of the document was to hopefully clarify some key issues, and to help provide a framework that would enable an increasing use of R in this domain. Looking back, I hope that it has, and there has also been greater acceptance of R at the FDA, thanks to both increasing internal support and comfort with open source software more generally.
In the intervening years, some of the commercial vendors of R have built toolsets and implemented internal validation processes, that have resulted in more restrictive “validated releases”, that have undergone these additional internal quality checks, as value added services to their clients. I can’t speak to the impact that these services have had, as I don’t use them and have not heard from folks that do.
There are community efforts, and @pmbrown referenced PSI, which I know has been involved in recent years, at least with R, in providing a forum for the discussion and implementation of tools that may be helpful. There is more information on their efforts here:
and you may wish to contact them to see if there are any relevant efforts, either within their organization, or perhaps with other parties, that would be relevant to Stan.
As @pmbrown notes, there is a good chance that larger and more established industry entities will be more resistant, and will require more documentation and processes to achieve a level of comfort. Change is not easy, and you can be hindered by normal human behavior that is resistant to change as a result. There need to be clear, value based, catalysts for the change and the nature of those will vary depending upon the environment.
We have seen that, for example, with R more generally over the years, and that has been aided by new statistical staff coming on board with experience in using R, both on the industry side and on the FDA side, and that has slowly changed the internal dynamics. On the industry side, there has also been a slow, but measurable, migration of the tools over the years from the pre-clinical side to the clinical side as comfort increases.