It’s the thing that immediately springs to mind when we first learn that SEND and SDTM are so closely related. The idea that there may be shared databases and tools, and most importantly, the nonclinical data and clinical data could be compared.
I’m guessing that anyone reading this blog will know that SEND is the standard for the representation of nonclinical data, but not all of us will be familiar with SDTM. It is the Study Data Tabulation Model, and the SDTM Implementation Guide (IG) is the clinical equivalent of SEND. Both the SEND IG and SDTM IG are implementation of the same model, meaning that they share the same variables, concepts, and rules. Well, mostly. Enough that SDTM domains and SEND domains, look the same. At least at first glance.
A lab result in SDTM-IG has pretty much the same variables and concepts to an equivalent result in SEND.
Upon realizing this, it’s no great leap to start to wonder about the possibilities of shared tools and visualizations. Both SDTM and SEND data could quite comfortably live in the same warehouse, which brings us on to the really big question: “If we see something unexpected in clinical, could we look back and see if we could have predicted this from the nonclinical data?”.
Yes, unsurprisingly, lots of individuals are talking about this possibility, at least on the nonclinical side, but is anyone actually doing it?
One of the counter arguments often cited is that while SEND and SDTM maybe almost identical in form, they are quite different in function. In the clinical world, tables, summaries and statistics are generally not run directly against the SDTM data, but rather the data are first represented in ADaM (Analysis Dataset Model). In the nonclinical world, we generate tables, summaries and statistics straight off the SEND datasets. So, in terms of function, SEND is closer to ADaM than SDTM, yet ADaM is not based on the same underlying model, and so can look very different to SEND.
However, when we see that SDTM and SEND look almost identical, we can’t help but be tempted to consider the possibilities of exploring what one could tell us about the other. So, I return to my opening thought. Everyone is talking about it, but nobody knows how to do it. We can all see the possibilities and so we all think everyone else is doing it. We think we should be doing it too. Everyone is talking about, but is anyone actually doing it?
As usual, please reach out to me to let me know your thoughts. If this is an area you are working in, I’d love to discuss these ideas further. Drop me a line at firstname.lastname@example.org
‘Till next time