I have not been doing a great job of creating updates about my THR modeling work, so here is one easy very-partial remedy for that. The FDA Center for Tobacco Products is holding a workshop about modeling and other methods that relate to their regulations and I am presenting a talk using these slides.
As those of you following my work on this subject know, I am rather critical of the usefulness of the existing “models” of tobacco use. (For those not familiar, follow the tag on this post for previous and future posts on the subject.) I use the scare-quotes because I argue that most of what are called models in this space fail because they are not actually a simplified version of the real system, but are really just complicated calculation tools. They completely omit the underlying mechanisms of the system — the consumer economics — and thus just translate high-level statistics (e.g., “assume 2% of the smokers transition to e-cigarettes each year”) into high-level outcomes (e.g., “the smoking rate over time follows this path”). This does not mean they are not useful, of course. There is value in that. But the value is calculating the answer to high-level hypothetical questions, not actually representing the system.
My argument in this talk is about how the lack of that real representation of the system means, most obviously, that it is impossible to make predictions about previously unobserved phenomena. (If the only use of data is to say “we have observed that when X happened then Y resulted”, you cannot say much about situations that have not happened before.) But it also means that even the high-level predictions are likely to be wrong because they are based on a misuse of the data (which I call superstition rather than science).
I made the tactical error of offering to present on any of several aspects of my research agenda, but fortunately the organizers shared my opinion that this bit is the most crucial for people to understand at this point. (Note to self: Don’t count on that in the future.) The talk is likely to come as a rather unwelcome coda (it is scheduled very late in the workshop) to a series of presentations about “models” that fail to do what I am saying must be done. Of course, I might be pleasantly surprised and discover that my message has already been covered. Such good news for the science would be bad news for my talk, of course, making it awkward with a lot of phrases like “this has already been discussed, but to reiterate the point”. But I am not optimistic/pessimistic that there is much chance this will occur.