Life after the v3.x SDTMIGs

Hard to believe that it’s been 11 years since the release of v3.1 of the SDTMIG.  Since then there have been 4 additional versioned releases, all based on the SDTM general class model, intended for representation as SAS v5 XPORT files.  SDTMIG still has plenty of life to it – in fact, one might argue that it’s just beginning to hit its stride now that use of CDISC standards will be mandatory in the US and Japan late in 2016.   But, let’s face it, as a standard that predates Facebook, YouTube, smartphones and reality TV, it’s also getting long in the tooth, and, indeed, may already be something of a legacy standard.

Perhaps the biggest limitation to the current SDTMIG is the restriction to use SAS v5 XPORT, a more than 30-year old format devised in the days of MS-DOS and floppy disks that is still the only data exchange format that the FDA and PMDA will currently accept for study data in submissions. While alternative formats have been proposed – the HL7 v3 Subject Data format in 2008, RDF in an FDA public meeting in 2012, the CDISC dataset-xml standard in 2013 – the FDA is still stuck on XPORT.  Recently they’ve asked the PhUSE CSS Community to help evaluate alternatives, which indicates that things haven’t progressed much closer to a decision yet.

The ripple effects of XPORT have severely limited the usefulness and acceptance of the SDTM beyond regulatory submissions – especially to those who haven’t grown up as SAS programmers working with domain and analysis datasets.  So any major new revision of the SDTMIG needs to start there, to split out all the XPORT-specific stuff.  This involves using longer field names, richer metadata, more advanced data types and eliminating field length restrictions.  That’s the easy part, but that’s not enough.  If we’re going to reconsider the SDTMIG, then we should use the opportunity to think broadly and address other needs as well.

We need a longer-term replacement, but we also need to keep the current trains running on time now.  Now that people are just getting used to the idea of a regulatory mandate to use SDTM and SEND, we certainly don’t want to change too much just yet.  We need to keep it stable enough so new adopters can get used to it – rapidly changing terminology gives them enough of a challenge to deal with without the pressure of adopting new IG versions.  I recently described one way to help minimize the number of necessary future versions of the existing XPORT-bound IG as a recipe.   We could do this now with the current version 3.2 and address many new needs.

On the other hand, we should be working on the next generation while we keep that venerable current one going.  In Chicago, the White Sox didn’t tear down the old Comiskey Park until the new U.S. Cellular field was finished — they built the new while using the old.  And they minimized making too many repairs to the old once they started working on the new.   So while we can assume we’ll need XPORT for some time even if a replacement exchange format is finally chosen, that shouldn’t stop us from rethinking the SDTMIG to better meet future needs now.  It’s time to think ahead.

What might a next generation SDTM look like?  A new SDTM for the future might have some of the following characteristics:

  1. As implied above, it should support standard content that’s independent of the exchange format. The standard should be easily representable in RDF, JSON (with HL7 FHIR resources and profiles), XML (and, yes, even XPORT for legacy purposes – at least for some years).
  2. A general class structure as used in the current model must remain as the heart of SDTM, though likely with some variations. We’ll want to retain the 3 general classes and most, but maybe not all variables (though such variables need precise definitions and more robust datatypes).  The core variables are essential, but perhaps some variables that are unique to a specific use case (such as those being introduced with new TAs or for SEND) can be packaged as supplements to augment the core under certain conditions.  What if there was a way to add new variables to general classes, timing and identifiers without necessarily creating a new IG version?  Rather than having to keep issuing new versions each time we want more variables, can’t a curated dictionary of non-standard variables – all defined with full metadata and applicable value sets – be used and managed separately in a manner similar to coding dictionaries?
  3. We may need some new general classes as well, such as the long-recognized need for a general class to represent activities such as procedures.
  4. We should reassess, with the benefit of hindsight, what data really belongs in which class. For example, perhaps substance use data (smoking, recreational drugs, alcohol) might be better represented as findings along with other lifestyle characteristics, which would better align with how such data is represented in healthcare systems.  Disposition data might fit better as an activity rather than event.
  5. Thorough definitions for each variable (a task already in progress), and variable names that are more intelligible – without being limited to 8 characters with a domain prefix – are mandatory.
  6. We should remove redundant information that can easily be looked up (as Jozef Aerts has long proposed). Lookups can be made via define-xml codelists or web services.
  7. Other non-backwards compatible corrections to known issues, deep in the weeds should also be addressed – such as distinguishing timings associated with specimen collection from point in time result findings – and resolving that strange confusion between collection data and start date in the Findings class.
  8. Perhaps a reconsideration and simplification of the key structure is in order, replacing the Sequence variable with a unique observation identifier/Uniform Resource Identifier (URI) that can be referenced for linked data purposes and make it easier to represent more complex associations and relationships (including the ability to be extended dimensionally with meta observations such as attributions and interpretations). This would be part of a richer metadata structure that should also support the representation of concepts.
  9. A more advanced extension mechanism that replaces the cumbersome supplemental qualifier approach is critical (such as the one already proposed by SDS) so users can easily incorporate those special use case variables mentioned in item 2 above.
  10. And we need the ability to align better with other healthcare-related information, to make it possible to use clinical study data with other real world data sources, and the courage to modify the SDTM to facilitate such alignment where appropriate.

Now, some might argue that this is still limiting ourselves to 2-dimensional representations here – which is indeed a valid criticism.  But maybe the longer term solution involves more than one representation of the data.  Perhaps we have a broad patient file with both structured and unstructured source information as a sort of case history, and representations/views in tabular structures that are derived from it – an old idea which might be getting closer to prime time.  Thinking beyond the table/dataset way of thinking should certainly be part of the exercise.

I know many are already impatient for change (at least as far as XPORT is concerned), and others feel we should just throw it all away and adopt more radical solutions.   But my personal feeling is that we need to keep what we have, which has already taken us much farther than we could have imagined 15 years ago, and build from that.  The approach echoes that of a 2009  New Yorker article by the great Atul Gawande about the upcoming healthcare reform, where he advocated building up from our history of employer-provided insurance rather than jumping to something radically different, like single-payer.  “Each country has built on its own history, however imperfect, unusual, and untidy… we have to start with what we have.”

So whatever we do, we should start with SDTM as governing model that really drives implementation, with more extensive metadata, clear definitions, complex datatypes, and a simpler extension mechanism.  An improved SDTM can drive implementation and result in a more streamlined implementation guide, that also shows how to apply research/biomedical concepts, controlled terminologies and computer-executable rules (e.g. for verifying conformance, derivations, relationships, etc.) and where to find use cases and examples. Such use cases and examples (as for Therapeutic Areas) could be maintained separately in a knowledge repository, and the SHARE metadata repository would provide all the pieces and help put them together.  We start with the SDTM and metadata and build out from there.  But we need to build in a way to converge with the opportunities provided by what’s going on in the world of healthcare, technology and science.  Like the Eastbound and Westbound project teams of the transcontinental railroad 150 years ago, we should endeavor to meet in the middle.

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The “Cubs Way” to Future Submission Data Standards

Even for those who don’t follow baseball, you must have heard something about the storybook year of the out of nowhere Chicago Cubs in 2015.  No, they’re not going to win the 2015 World Series, but they made the Final Four, and somehow, that didn’t feel like losing this time around.

You must know this about the Cubs: 107 years since their last championship, which is generally acknowledged as the benchmark of futility in professional sports.  For clinical data geeks, you might think in terms of a similar drought — the many years we’ve been handicapped with SAS V5 transport format (XPT).  XPT stems from the days of the Commodore computer, 5-1/4” floppy disks and MS-DOS 640kb memory limits, and while it hasn’t been around quite as long as the Cubs’ last World Series trophy, it’s a Methuselah in tech years.

However, just like the Cubs and their venerable Wrigley Field, it looks like it’s going to be around for awhile, and definitely needs some attention.  So can we learn any relevant lessons from the 2015 Cubs?

  1. Think long term – with a plan. The old Cubs way (overpriced has-been free agents and bad trades) had never worked, so the new regime sacrificed current performance for the promise of future competitiveness, losing enough games to gain high draft picks and flip-trading useful veterans for uncertain prospects.  With respect to XPT, this might mean living with a partial improvement (like the CDISC Dataset-XML) for awhile while working on a separate longer-term solution that will will keep us competitive for decades.
  1. Keep meeting current needs (but only to a point). The Cubs still had to field a team that showed enough to keep fans on board and invested in the future.  In our world that means giving users time to gain basic literacy and get the most value possible out of current CDISC data standards with XPT (and maybe Dataset-XML), now that those will be required by FDA and PMDA (who aren’t about to change suddenly before the rule formally goes into effect).   This might also mean that we limit the degree of change to the current published standards with some minimal fine-tuning that users can easily absorb until they gain basic literacy, while concentrating most of the attention on that much more robust next generation solution that will make the big leaps tomorrow.
  1. Be patient so the prospects can develop.  In other words, even if the future solution isn’t necessarily mature now, that may be fine as long as it’s got the talent to take you a where you need to go in the future.  Such a description might fit HL7 FHIR and the Semantic Web, for example.
  1. Fill in the missing pieces along the way. – The Cubs soon realized they needed more starting pitching and situational hitting, which will guide their winter and spring moves for next year.
  1. Don’t worry about future salaries (I mean file size)! In 1908, the highest paid star baseball player made $8500, and in 1988 a floppy disk held 1.44 MB, less than a typical MP3 song that you can play from your watch.  This should not be an obstacle to moving beyond XPT.  Things get bigger over time; get over it.

Of course, the jury’s still out on whether the Cubs will ever make it, but it seems there’s more excitement about next year here in the Windy City than ever before.  It would be wonderful if we could say the same sort of thing about the future of clinical data by spring training, 2017.