Dear CFAST:  Please Slow Down to Catch Up

A new year and many new things to think about in the greater world of research and healthcare, though there are still plenty of unfinished thoughts about CDISC to expound upon.  In my last post, I tried to sum up my thoughts on SDTM, advocating a more restrained approach that prioritized stability of the core SDTM/SDTMIG standard over frequent change, and proposing a cookbook recipe approach to addressing gaps in the model, especially for the CFAST Therapeutic Area User Guides (TAUGs).

And today, I’d like to echo the slow food movement, with a plea directly to CFAST to also take a breather and slow down. This may seem like a departure from my past point of view – I was a strong advocate of adopting Scrum for standards in order to get more things done. But moving ahead more rapidly should not be done at the expense of consistency and clarity of approach.   CFAST TAUGs have been produced at a relatively prolific rate of 6 or more per year since 2013.  Yet the pressure during that span has been to issue even more TAUGs each year, rather than maintaining consistency in the modeling approaches used by them all.

So, as former Chair of the CFAST TA Steering Committee, I’d like to respectfully suggest that they apply the brakes for a bit until they can go back and bring the full body of published TAUGs to a common parity by fixing the inconsistencies that currently exist.

This is the long-standing “maintenance issue” that was never completely addressed during my term as Chair.  There are several cases where modeling approaches adopted in one TAUG changed when a new TAUG was issued, which might not be apparent to those who don’t read every separate publication.  These inconsistencies in approach work against the goals of standardization, and can only aggravate the types of variances between submissions that have often frustrated FDA reviewers with SDTM submissions to date.  Now’s the time to go back and fix these dangling loose ends, since the FDA has yet to issue a clear message endorsing TAUGs, and since many sponsors are still evaluating how to utilize them.

To take one prominent example, there have been at least four different approaches to represent the diagnosis date of the condition or disease under study in TAUGs over the years:

  1. Putting the date of diagnosis in q Medical History (MH) domain record, using MHCAT = ‘PRIMARY DIAGNOSIS’ and MHSCAT to distinguish between an onset course and a current course (though these important categorization values are not specified as controlled terminology). This approach was used in the Alzheimer’s, Parkinson’s and other TAUGs.
  2. Creating a Findings About (FA) record, with the FAOBJ = <disease or condition>, and the date as an observation result. This approach was used in the Asthma TAUG.
  3. Creating a supplemental qualifier record with QNAM=’MHDXDTC’, an approach that was used in the Diabetes TAUG.
  4. Creating a new MH record using the new SDTM v1.5 variable MHEVTTYP = ’DIAGNOSIS’ – which was an approach recommended by the SDS Leadership group (including me) in 2014 and was adopted as the preferred approach in some of the more recent TAUGs (e.g., MS). Unfortunately, adoption of this approach has not been embraced by the full SDS team and is limited because it’s not included in the SDTMIG and requires a new variable which is not in the currently accepted v1.4 SDTM.

Now, given that the date of diagnosis is likely to be of interest to all medical reviewers, the notion of how to represent a date of diagnosis of the disease under study consistently should be tackled independently of any TA.  And the correct approach should be easy to find by implementers.  But, currently, the method may vary depending on what document each implementer uses as a guideline.

One way for dealing with this type of maintenance issue is to remove it from the individual TAUGs and making it a standardized convention that could be consulted by any implementer as a separate resource.  Using my cookbook recipe approach, such a guideline might say:

  • Always represent Diagnosis Date as an MH event record
  • Use MHEVTTYP to distinguish it from other elements of history with controlled terminology value = ‘DIAGNOSIS’.
  • If using SDTM v1.4 or earlier, represent MHEVTTYP (with controlled terminology QNAM value of ‘MHEVTTYP’ and QVAL = ‘DIAGNOSIS’.

Now (especially if supplemental qualifiers are represented within the parent domain where they belong) wouldn’t that be easier on the FDA reviewer?  Wouldn’t it be helpful to always know where to find such an important bit of data no matter who submits it irrespective of which reference (IG, UG) an individual sponsor employee may have chosen to follow at the time?  I mean, can CDISC and CFAST really say they has an effective standard if they’re not consistent on such points?

So, one more shout in the dark:

  • Let’s ease the constant pressure to create new TAUGs, domain models and new IG versions (which often seem like they’ll never be finished anyway)
  • Stop creating new TAUGs that contradict modeling approaches of older ones until all the published ones can be made consistent to a common baseline. One way to do this would be to remove all references how to represent certain modeling use cases like Diagnosis Date from the published TAUGs and replace with a link to a document showing a preferred standard approach (though an example might still be provided in the TAUG).
  • Start representing more recipes and examples for other preferred modeling conventions with better engagement of the CDISC standards implementer community as an interactive forum.
  • Make a firm decision once and for all between “Taste Great” and “Less Filling” as key modeling principles moving forward.

If you’d like to learn more about some of the significant variations across the CFAST TAUGs, please refer to this excellent paper by Johannes Ulander and Niels Both presented at PhUSE 2015.


A Call to CDISC Arms – Recapping My SDTM Posts

I’ve been posting a series of thoughts on the CDISC SDTM over the past few weeks, and realize that most people won’t have time to read them all, so thought I’d sum up with these CliffsNotes before moving on to other thoughts for improving clinical research.  So, as former CDISC CTO, here are my essential hindsight thoughts in a nutshell on what should be done with the SDTMIG:

  1. Prioritize stability for the SDTMIG so the world can catch up. In SDTM as a Cookbook I make a plea to aim for more SDTMIG stability by minimizing new versions, suggesting that new domains be presented as recipes to be crafted from the SDTM rather than as fixed domain models in the IG.  I propose a leaner, more flexible approach to applying the SDTM to create new custom domains for data not previously addressed in the current SDTMIG, a suggestion which should be adopted by TAUGs immediately.
  2. Fix supplemental qualifiers now! RIP Time for Supplemental Qualifiers makes another plea to represent non-standard variables (NSVs) in the parent domain where they belong, using Define-xml tags.  Acknowledging past FDA concerns about file size, suggests an alternative of representing these as a separate file that can be easily merged onto the parent as extra columns (much more easily than the vertical method in SUPPXX files today).  This has been a nuisance for far too long – fix it.
  3. Hit the brakes on creating so many new domains. As in the “Cookbook” blog,  The Lumping and Splitting Acid Test makes another plea to be more conservative in creation of new SDTMIG domains, proposing a default “lumping” approach which should only support new domains when there’s no ambiguity about what data to put there.  The more domains we give adopters to choose from, the more likely that they’ll make different decisions – the exact opposite of what a standard should do.  It’s also time to realize that such granular separation of domains probably won’t be the way of the future with patient data in integrated data repositories – see the FDA’s Janus CTR for an example.
  4. Describe use cases for applying the SDTM instead of IG version updates. A Recipe from the SDTM Cookbook gives an example of creating a new domain following the SDTM as a Cookbook approach.   This can be done without creating new IG versions, meaning that people can actually get accurate new information more quickly instead of waiting years for the next IG version.
  5. Time to plan the next generation. Life after the v3.x SDTMIG offers yet another plea to refrain from creating too many new versions of the SDTMIG v3.x series, and begin work instead on a next generation v2 SDTM and, subsequently, v4 SDTMIG that fixes long-standing problems and limitations and is more compatible with interoperability goals and newer technologies.
  6. Clarify the role of CFAST Therapeutic Area User Guides. No blog on this yet, but it’s essential to clarify that CFAST Therapeutic Area User Guides should NOT be considered as separate new standards – they should be viewed as ways to apply current standards. We can provide new examples of how to use a standard whenever a valid new example is created – but we simply can’t allow more confusion to be introduced into an industry still learning how to use our standards by frequently pushing out conflicting information or confusing directions about draft domains or modeling approaches that don’t conform to current standards acceptable to regulators.  My feeling is that TAUGs should focus on describing use cases and recipes to apply the current standards as well as identify new terminology to be applied.  Of course, would be better if we fixed a few things first – notably supplemental qualifiers and probably the disease milestones approach introduced with the Diabetes TAUG.  So maybe a v3.3 SDTMIG is necessary – but let’s draw the 3.x line there.

As we get clearer to the onset date of the binding guidance, we need to think differently if we want to make implementation of data standards the success story it ought to be.  So (maybe after finishing v3.3 soon, I hope) let’s take a breather, let the soup simmer awhile.  Meanwhile, let’s arouse the community to share their implementation challenges and experiences more openly and widely (as some of the User Groups already do, with a special mention to the energetic example set by the CDISC Japan User Group).  Let’s agree we need to gather more common practical experience and knowledge before we jump to develop new disruptive content.

And let’s get going collecting those requirements for the next, great leap forward instead.

That’s the pitch, so let the cards and rotten tomatoes fly.

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.

A Recipe from the SDTM Cookbook

In my earlier posting on SDTM as a Cookbook, I described an alternative approach for defining new domain models for use with CFAST Therapeutic Area User Guides (TAUGs). Based on an internal poll of SDS team members, there seems to be a desire to create many domain models (a predilection toward splitting, rather than the lumping approach I favor).  Yet creating new domains is a frustrating and lengthy process. Although these are now mostly being modeled by CFAST teams with very specific use cases, there has been a tendency to also vet them through SDS in a more generalized form as part of a batch associated with a future SDTMIG release, a process which can take 2-3 years or more.  In the meantime, TAUGs are faced with proposing draft domain models under a stricter timeline, well before they exist in the officially sanctioned normative SDTMIG world.

What a waste — and it gets worse.  Once the domain is issued as final as part of an SDTMIG version update (indeed, that’s assuming the SDS team consensus allows it to and it actually passes the comment process) it now has to be evaluated by FDA before they can determine whether they’re ready to accept it.   Although 15 TAUGs have been posted to date, the FDA has yet to clearly indicate their readiness to accept any of them.  And the acceptance process has also been excruciatingly long (it took nearly 2 years for FDA to announce readiness to accept SDTMIG v3.2 – even then with some restrictions). In the meantime, people simply make up some other approach to get their daily work done – the antithesis of standards.

Let’s take a current example of how we may have applied the cookbook approach to draft domains included with the just-posted TAUG-TBv2. This TAUG includes 5 new draft domains as well as revisions to 3 existing domains which are presented as SDTMIG domain models.  One of the new domains (which was vetted with SDTMIG v3.3 Batch 2 and also used in Asthma but not yet released as final) is the RE (Respiratory Physiology) domain. This is a Findings General Class domain, which is mostly consistent with the current version of the SDTM v1.4 except for the addition of 3 new variables:  REORREF, RESTREFN and REIRESFL. (An earlier version of this domain was also included in the Asthma and Influenza TAUGs.)

Now a cookbook recipe might present this RE domain as a list of steps to follow to “roll your own” domain.  Instructions might include:

  1. Create a new custom domain with the standard identifiers and all variables from the SDTM v1.4 Findings General Class.
  2. Assign the Domain Code and prefix “RE” from controlled terminology.
  3. Insert the standard timing variables that are typically provided with a Findings Physiology domain.
  4. Create the following 3 new Non Standard Variables (NSVs):
    1. REORREF

(Note definitions for these might be pulled from a newer version of the SDTM which is now being updated more frequently, or else from a CDISC Wiki resource).

  1. Remove any unnecessary or irrelevant permissible variables such as REMODIFY, RETSTDTL, etc. – just like you do with published domains. (Note that these are all permissible variables – assigning a new NSV as Required or Expected would be a complication, but this would be an odd choice for a newly created variable anyway).
  2. Add any additional NSVs in the usual manner (this would be much smoother if the new proposed method of putting NSVs in the parent domain was adopted).
  3. Apply other controlled terminology bindings for variables within the domain (such as RECAT, RETESTCD, etc.  that are declared in a sample define.xml file that’s posted along with the recipe.

As the output of this exercise one would normally create the define.xml metadata for the domain as well as a RE.XPT file (which will later be populated with data values).   The sample define file included with the recipe would also specify which controlled terminologies apply to both the standard and new non-standard variables (I assume that new terminology values that would be intended for use in this domain would be created through the normal terminology request process, and simply referenced in the define-xml example). The recipe could still provide a draft domain in the usual Word Table or Excel format – but this would be presented as an example rather than a normative specification, similar to including an illustration or photo in a recipe.

I believe it should be sufficient to apply the standard class-level validation rules (which include checking for controlled terminology assignments), which can be addressed separately from the domain model, so there should not be any specific new user acceptance testing required by FDA. FDA might also specify separate content-based checks they may want, but these can be added at any time later, once they’ve had a chance to review submissions using this model.  But new rules can also be added outside the IG.  And while it will be technically a non-binding custom v3.2 domain in each submission, if it’s conforming to the recipe (which can be clearly stated in the define) it can serve the same purpose as a new SDTMIG domain in a future version. The difference is that it can be put directly into use.  A beneficial side effect is that this also encourages early testing among the research community, which might result in beneficial tweaks to the recipe, which can be maintained over time and augmented with more and more examples suggested by adopters in a crowd-sourced Wiki sharing environment, which should only serve to make the domain model more solid over time.   Sure, this might require review and curation by the SDS team, but that should be a lot less onerous than the current process.

The benefits of such an approach include:

  • Making it simpler and easier to create new domain models based on existing published versions, which might help shorten the development time for TAUGs
  • Allowing sponsors to adopt these new models more rapidly without waiting for new domains or FDA announcements
  • Making it possible for FDA to accept these models without a lengthy acceptance process
  • Providing an improved, rapidly evolving Wiki-based knowledge resource to help sponsors address representation of data that doesn’t fit in existing final domains in a more consistent manner.
  • Minimizing the number of new versions of the SDTMIG that have to be handled by industry and regulatory authorities.

Of course, adopting such an approach is not trivial.  It would require buy-in by FDA and industry, and would require new methods for sharing these recipe guidelines (probably via the Wiki) and a whole lot of communication and training.  But it seems to me it would be a much more practical way to move forward to extend the reach of the SDTM for new TAs in a leaner, quicker manner with fewer maintenance and version management headaches.

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.

The Lumping and Splitting Acid Test

Call me “Lumpy.”  And I’m a lumpaholic.  I acknowledge this predilection, because I simply feel lumping makes things easier, and I’m generally more comfortable with big pictures than detailed nuances.

But I’m not a lumping zealot.  I have many close friends are splitters, and I can respect their point of view some of the time.   It’s not unlike the dynamic between friends who are either Cubs or Sox fans in Chicago, for instance – we can agree to disagree, even when they’re wrong.

The term “lumping and splitting” apparently was coined by Charles Darwin with respect to how to classify species, and is explained nicely here:

  • (For lumpers) “two things are in the same category unless there is some convincing reason to divide them.”
  • (For splitters) “Two things are in different categories unless there is some convincing reason to unite them”.

This clearly defines the opening position of each camp, and relies on the existence of a compelling and logical reason to go the other way, which sounds reasonable enough.

Now a partisan divide of lumpers and splitters also exists in the CDISC SDS/SDTM microcosm, where the term is applied to the decision on whether to create new domains.   Lumpers believe there should be a limited number of domains, to make it easier for sponsors to know where to put data.  Splitters want to create many more domains with finer distinctions between them.  The CDISC SEND team follows a very fine-grained splitting approach.  But that does not necessarily mean human trials should follow the same pattern, since a lumping approach has also been followed with questionnaires and lab results data.  In the latter cases, the SDTMIG describes a way to split these often massive lumped domains into separate files to conform to FDA file limits, but they’re still considered the same domain.

This difference of opinion has recently been illuminated as the team has wrestled with how to represent morphology and physiology data, a topic that’s critical to CFAST Therapeutic Area standards.  Some years ago, the SDS team made a decision to separate physiology data by body system, and reserved some 15 separate physiological domain codes even though no specific use cases had been defined for all of those yet, while lumping together morphological observations in a single domain.  This proved problematic, because it wasn’t always clear which type of test belonged where.  So the team decided (through an internal poll) to merge Morphological observations into the various physiology domains.  An alternative lumping proposal to combine all physiology data in a single data, and use a separate variable for Body system (as was already the case in Events and the PE domain) was proposed but did not gain sufficient traction.

Splitting may be fine as long as there’s that convincing reason and it’s clear where to split – like the “Tear Here” perforation on a package.  We can easily see that AEs differ from Vital Signs (though the latter may indicate a potential AE).  And I’m not suggesting that we lump together domains previously released (well not all, anyway). But what do you do with a complex disease such as cancer or diabetes, or a traumatic injury that affects multiple body systems?  what happens with, say, observations related to a bullet wound that penetrates skin, muscle and  organs when you have to split these over multiple domains?

In such cases, wouldn’t a physician – or FDA medical reviewer – want to see all of the observations relevant to the patient’s disease state together rather than jumping around from file to file and trying to link them up?   And how are patient profile visualizations affected when multiple, possibly related morphological and physiological observations are split across many separate files, since patient profiles tend to look in a finite number of domains for core data (typically DM, EX, LB, VS, AE and CM) – adding in dozens of others is likely to be challenging.  And maybe it would reduce the constant stress of having to accommodate more and more new domains with each new version if SDS didn’t feel a need to keep adding more and more domains each time.

This brings me back to a “first principle” – making it easy on the reviewer. If you know exactly what you’re looking for, and you’re confident everyone else knows to put the same type of data in the same place, then maybe it’s easy to point to a separate fine-grained domain.  But what if you’re looking to see possible associations and relationships that may not be explicit (which FDA reviewers have lamented previously).  I think for most of us who are familiar with spreadsheets and other query and graphics tools, it’s far easier to have all the data you want in one place and rely on filter and search tools within a structured dataset rather than search/query across a directory full for files.  And that’s one reason why FDA has been working so long on moving from file based data review to their Janus Clinical Data Repository (CDR) – so reviewers can find the data they want through a single interface.  In my experience, CDRs (including Janus) do not usually split most findings type data by domain.

Lumping solutions should be more consistent between sponsors and studies, since there’s a simpler decision on where to put data.  Just like when shopping at Costco, it’s easier to make a decision when there are fewer choices involved. And just as it’s quicker and easier to pick out some bathroom reading from a single shelf rather than have to search through an entire library, lumping should make it quicker and easier to access the data you want.

So, what exactly is the acid test?   Whichever approach is chosen for SDTMIG in the future (and it should be up to the community to weigh in when the next comment period begins), one overriding principle must be in place:  a new domain split should never be created unless there’s a clear rationale for splitting (and in the case of Physiology, I don’t really see that myself), and it’s absolutely, unambiguously clear when the new domain is to be used for what kind of data.  If there’s any ambiguity about whether a certain type of data could go here or there, then we should opt for a lumping solution instead, and allow users to rely on query and filter display tools to pick out what they want.  Meanwhile, we can rely on our statistical programmers put data logically together in analysis files just as we always have.

So, lumpers of the world, unite!  There are simply too many other problems to tackle other than the “What domain do I use this time?” game.  Like defining rich metadata models for sets of variables with valid terminology (i.e., concepts), or defining a next generation SDTM that isn’t based on SAS XPT.

In the meantime, another scoop of mashed potatoes for me, please – lumps and all.

R.I.P. Time for Supplemental Qualifiers

Warning:  this one’s primarily for SDTM geeks.

Back when the SDTM and SDTMIG v3.1 were being created circa 2003, there was never a delusion that the SDS team had thought of everything.  The SDTMIG domains were created by taking the least common denominator among CRFs from several major pharma companies.  It was always understood that we could only standardize on a core set of variables – that individual studies would almost always find cases when they’d need to add additional variables for some specific purpose.

The chosen solution for handling these extra variables was (shudder) supplemental qualifiers (SQs).  The original use case for SQs was to provide a way to represent flag variables like “clinically significant” that might be attributed to different people – an independent reviewer or DSMB, for instance.  But this was expanded to handle other variables that didn’t fit within any variable defined in the general classes.  A name/value pair structure with a different row for each variable and its value was adopted – quite flexible, but not very user friendly.  This was not viewed as a problem by all — there was a perception (held by one our FDA observers among others) that by making it difficult to represent SQs, sponsors would be disinclined to use them, and thus the standard would be leaner and more consistent and not get cluttered with other messy data.

But that assumption was wrong.  It turned out that many standard domains often need additional variables to fully describe a record – often essential information related to a specific therapeutic area.  So the SQ files kept getting bigger and bigger.

And SQs were clunky in many ways.  It was necessary to use value-level metadata to describe the variable in define.xml files.  And some tools or users had difficulty merging them back into parent domains.  And because they were so unwieldy, voluminous and hard to read, some reviewers simply gave up looking at them at all, resulting in risks that critical information might be missed during a review.

So some SDS team members wisely proposed an alternative proposal to place these SQs (which they renamed “Non-Standard Variables” or “NSV’s”) in the parent domain.  Instead of physically separating these out into another file structured differently, the proposal appended these to the end of the dataset record and relied on Define metadata to tag these as non-standard.  The metadata tag would make it straightforward to strip these out into a separate SuppQual structure if that was still needed for some reason (such as conforming to a database load program expecting such a file) but the dataset would already include these variables where they belong so they’d be less likely to be missed.

But this reasonable proposal wasn’t viewed as a panacea by everyone.  FDA was still concerned that this would encourage sponsors to add more and more unnecessary variables – which might just be noise to a reviewer.  And they worried about increasing the file size beyond their acceptable limits.  (But at least they didn’t disagree that these were a whole lot more trouble in their present form than they anticipated).

Meanwhile, other members of the SDS team objected to the proposal as an unnecessary change – since most companies had already invested in ways to create these and didn’t want to have to change again (even if the datasets would be more useful and their processes simpler if they did).  This, of course, is the notoriously stubborn  “sunk cost” fallacy.

But let’s pause now for a moment.  We know that the current SuppQual method is a clunky solution, which was already revised once (in SDTMIG v3.1.1, when a single file proved unmanageable and too big to submit), and that we still hear it can cause review problems for many and is seen as an unnecessary extra non-value added step by many more.  But we don’t want to offer a simpler and more efficient solution instead because we’ve already invested in the clunky solution?  Hello?

So, here’s another suggestion.  Let’s create a separate file with the exact same structure as the parent domain – namely, use the SDTM unique keys (STUDYID-DOMAIN-USUBJID-XXSEQ) and add in all the NSVs as additional columns.  Such a structure would allow full metadata representation in Define-XML – just like the other variables — and is a much simpler merge (and, for sponsors, also a simple split to take them out).  To allow applications to realize that this is a different merge from the normal SUPP—format, perhaps a new file name prefix can be used, such as SUPW (for “wide” or some other name, whatever).

Under such a scenario, FDA should be happy that file sizes are smaller (and smaller than the current tall and skinny SuppXX files, since they tend to expand to reserve as much space for all variables as required by the biggest), and the variables can be easily viewed in the dataset whether they’re merged or not – making it possible to only merge in the ones of interest if the possibility of noise is still a concern.

Not quite as elegant as a single file solution, but certainly seems to me better than the status quo.  And for those SDTM old-timers who still want to do it the old way, well, they can probably adapt the code they’ve already written to strip out the NSVs when they create SDTM (and put them back for their statistical programmers and internal reviewers) already and keep wasting time doing it the old way as well if that’s what really makes them happy.

Seriously, can’t we bury these SUPPxx files once and for all and try to agree to make SDTM datasets just a little more useful?  What’s the controversy with that?

SDTM as a Cookbook

The original CDISC Study Data Tabulation Model Implementation Guide (SDTMIG) was built around a core set of domains describing data commonly used in most clinical trials — much of which were necessary to understand basic product safety.  Over time, the SDTMIG has been expanded with new versions that incorporated more and more domain models.  SDTMIG v3.2 has nearly 50 domains, with several more to come with the next version.

This domain-based approach, while very familiar to those involved in study data management and analysis, has been a mixed blessing in many ways:

  • FDA acceptance testing of new SDTMIG versions has been taking a year or more post publication, and sometimes this belated support for new versions comes with exceptions — a serious disincentive to early adoption by sponsors.
  • While continually adding new domain models may be helpful to some, others may find these threatening – due partly to the excessive effort of change management in a regulated systems environment in general and especially if they’ve already developed alternative approaches to modeling similar data in custom domains which might require last minute database changes to support new SDTMIG versions for ongoing drug development programs.
  • The time it takes the SDS team to release a new SDTMIG version is at least two years or more, and its already massive but steadily increasing size and complexity makes updating more difficult and adoption and understanding by end users burdensome. And still publicly available validation tools have not been able to keep up.
  • Meanwhile, the long timeline has made it necessary for the timeboxed CFAST Therapeutic Area (TA) User Guides to issue Draft or Provisional domain models to capture TA-specific requirements, which FDA seems reluctant to accept, again discouraging early adoption and injecting more fear, uncertainty and doubt since such domains may still undergo change before being added to a new SDTMIG version.

So this spiral of steadying increasing complexity and version management seems to be a cause of widespread consternation.

Cooking Up an Alternative Solution

What if we considered the SDTMIG as something more like a cookbook of recipes rather than a blueprint?  A recipe describes the general ingredients, but the cook has discretion to substitute or add some additional ingredients and season to taste.   What if the SDS and CFAST teams — rather than continuously creating more and more new domain models (sometimes with seemingly overlapping content), and waiting to embed these within a new version of the normative SDTMIG every few years to address any changes or additions —  took a more flexible, forgiving approach.  So a new Therapeutic Area User Guide would continue to describe what existing final SDTMIG domains may be most appropriate for representing TA data such as relevant disease history, baseline conditions, and, when necessary, include directions for cooking up a new custom domain for, say, key efficacy data.  The recipe would state which SDTM general class to use, which variables from that class apply, what current or new controlled terminologies should be used, and other implementation details, such as any incremental business rules that might be used for validation checks.  in lieu of a traditional domain model, the recipe could include an example of the Define-xml metadata for the domain.  But it’s up to the cook to adjust for the number of guests at the table, prepare, add garnishes and serve.  Such a recipe could be referenced (just like an SDTMIG version and a domain version) in a define-xml file (a proposed new v2.1 of Define-XML has features to support this) as if it was a standard, but, in reality, it would be a best practice guideline.

Representing domain models as recipes (or guidelines if you prefer) would have the advantage of producing domains that would already be acceptable for FDA submission (since custom domains are being accepted by FDA), and yet still being checked for consistency with controlled terminology and validation rules.  And adopters of CFAST TA standards might start using these more quickly, allowing these to be field tested so they can evolve more rapidly without waiting years for a new SDTMIG version to bless them.  As people learn more and gain more confidence, they can post additional examples and comments to a Wiki that can inform future user so everyone builds on everyone else’s experience over time.

Under this approach, the SDTM becomes the real model/basis for the standard, and the domain models would be treated as representative use cases.  The recipe can also include incremental validation rules, but must remain faithful to any SDTM model-level rules (which would have to be promoted upward from the IG presumably). New models may need new variables in many cases, and these variables should be added to newer versions of the SDTM.  But, assuming the SDS team finally adopts a more efficient manner of representing Non-Standard Variables (as they have already proposed in two drafts for comment) it would be easy enough for custom recipe-driven domains conforming to a current version of the SDTM to add each necessary new variable as an NSV at first.  These NSVs could then to uplifted to become standard variables later in a new SDTM version, which would only require a simple change to an attribute in the define.xml once that new version becomes available. Either way, the new domain model can be used immediately by applying current available structures in the SDTM with some incremental new terminology (such as a new domain code describing the contents and a new controlled term for a variable name.)

This does not mean the SDTMIG goes away – it’s still needed to describe the context, general assumptions and rules, and show how to implement key SDTM concepts like the 3 general classes, trial design model, special purpose and relationship classes.  But the IG wouldn’t need to cover each new variable and domain anymore and could focus on explaining core SDTM concepts with assumptions and rules instead.  It could make for a leaner SDTMIG, which doesn’t have to change so often, and impart more flexibility and agility in the use of domains.

Such an approach could also be a good first step toward decoupling the SDTMIG from the highly cumbersome, restrictive use of SAS v5 Transport files, and make the model more adaptable for implementation in new technological frameworks, such as HL7 FHIR and the semantic web.

Of course this still leaves a number of challenges in terms of terminology management, but that’s a topic for another day.

In the meantime, what do you think about a simpler SDTMIG, that emphasized applying the general model with terminology to meet the needs for new CFAST domains?