QCast Episode 66: What is Clinical Traceability?

Clinical traceability allows teams to understand where clinical evidence came from, how it was transformed, and which records and decisions support a reported result. In this QCast episode, co-hosts Jullia and Tom look at how that chain of clinical traceability can extend across source data, SDTM and ADaM datasets, statistical programs, outputs, and study documents, particularly when work is distributed across sponsors, CROs, laboratories, and technology providers.

The practical challenge is often less about whether files exist and more about whether the relationships between them remain understandable. Changes to derivations, external data transfers, system migrations, provider handovers, and incomplete metadata can all make it harder to reconstruct what happened later. The discussion also considers the limits of relying on standards or technology alone, and why traceability needs to be designed into study processes from the outset.

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Key Takeaways

Traceability depends on connected evidence

A complete traceability chain should allow someone to move from a reported result back through the relevant analysis dataset, derivation, tabulation data, and source information. That connection also needs to preserve the context behind the data, including versions, specifications, and important analysis decisions.

Outsourcing increases the need for clear ownership

When clinical data move between sites, laboratories, CROs, sponsors, and technology platforms, identifiers and metadata need to remain consistent enough to preserve those relationships. External transfer specifications, reconciliation processes, version control, and clearly assigned responsibilities can reduce the risk of context being lost during handovers or system changes.

Standards and technology support traceability but do not create it

CDISC structures such as SDTM and ADaM can support traceability, while metadata and tools can make relationships easier to inspect. However, conformance and visual lineage alone cannot compensate for undocumented derivations, unclear ownership, or incomplete records. A useful check is to trace an important result backwards to its source and then follow a source variable forward to the analyses that depend on it.

Full Transcript

Jullia

Welcome to QCast, the show where biometric expertise meets data-driven dialogue. I’m Jullia.

Tom

I’m Tom, and in each episode, we dive into the methodologies, case studies, regulatory shifts, and industry trends shaping modern drug development.

Jullia

Whether you’re in biotech, pharma or life sciences, we’re here to bring you practical insights straight from a leading biometrics CRO. Let’s get started.

Tom

Clinical traceability sounds straightforward at first. You collect clinical data, process it, analyse it, and report the results. But what does traceability actually mean once that information has moved through several systems and organisations?

Jullia

Well at its simplest, it means being able to follow the evidence back to where it came from and understand what happened to it along the way. So, if you have a treatment effect in a Clinical Study Report, you should be able to work backwards through the statistical output, the analysis dataset, the tabulation data, and ultimately the original observations that support it.

And it’s also more than knowing where a file sits. You need to understand the meaning of the data, the transformations applied to it, which version was used, and why particular decisions were made.

Tom

So just because I can find the final dataset and the output, that doesn’t necessarily mean I have good traceability?

Jullia

No. You could have all the files and still struggle to reconstruct the evidence. Take a p-value reported in several documents, for example. Finding the number is easy enough. But if someone asks where it came from, you need the endpoint definition, analysis population, statistical method, variables, source records, program version, and the approved output behind it. Without those connections, sure you’ve stored the result, but you haven’t necessarily preserved its lineage.

Tom

And that becomes more difficult when the study is outsourced, presumably, because the information isn’t moving through one organisation?

Jullia

Exactly. As you’re saying, clinical data might pass through sites, central laboratories, technology vendors, a CRO, and the sponsor before it reaches a submission. External data can arrive through separate transfer processes, programming may happen somewhere else again, and the final documents may be produced by another team.

Now that model can work perfectly well. Really, the issue is whether the relationships between those activities remain clear when responsibilities and systems are distributed.

Tom

Could you give me a fairly ordinary example of where that relationship might break?

Jullia

So imagine a laboratory result coming into the study through an external transfer. It’s loaded, reconciled, mapped into SDTM, and then used to derive an analysis variable in ADaM.

Months later, somebody notices an unexpected value in a table. They need to establish whether the issue began with the original laboratory record, the transfer, the mapping, the derivation, or perhaps the analysis program itself. If identifiers have changed between systems, or the transfer specification is incomplete, that investigation becomes much harder than it should be.

Tom

And I suppose the same problem applies when something changes rather than simply going wrong. Say a derivation is updated during programming?

Jullia

Yes, because then you need to understand the impact of the change. Which outputs use that variable? Which tables or figures need to be rerun? Has the change affected any conclusions already incorporated into documents?

Tom

Now there’s a misconception there that I’ve heard before. If a study is CDISC compliant and the datasets pass validation, isn’t most of this already taken care of?

Jullia

So while CDISC standards are an important part of it, conformance isn’t the same thing as complete traceability.

SDTM gives you a standard structure for tabulation data. ADaM supports analysis-ready datasets and traceability of analysis variables. Define-XML, annotated CRFs, reviewer’s guides, specifications, and other metadata add more of the context.

But a structurally compliant dataset doesn’t automatically tell you whether every important result can be reconstructed from source through analysis. That depends on how well those components have been connected and documented.

Tom

So metadata is doing quite a lot of work here?

Jullia

Yes, see metadata describes what something means, where it came from, how it was transformed, and how it relates to other information.

For example, an ADaM variable may derive from one or more SDTM variables. The metadata should help someone understand that relationship rather than requiring them to reverse-engineer the program years later. Likewise, analysis results metadata can connect an output with the analysis dataset and method that produced it.

Tom

Years later is an important phrase there. Teams naturally focus on the next lock or submission, but these records may have to make sense well beyond the immediate study team, right?

Jullia

Definitely, yes. People change roles, CROs change, systems are replaced, and studies can be combined into larger programmes over time. You might also need the evidence again when responding to a regulatory question or when somebody takes responsibility for a programme they didn’t originally work on.

And memory is a poor traceability system. If the reason for a programming decision or data-handling rule exists mainly in somebody’s recollection, that knowledge is very vulnerable.

Tom

What tends to cause trouble during those handovers? Is it usually missing data?

Jullia

Sometimes, but quite often it’s missing context more than anything. You see, you may receive the datasets and programs but not the assumptions behind them. A derivation may be coded without an adequate specification. A legacy conversion may have mappings but little explanation of known limitations. Or a system migration may preserve the data values but lose useful metadata or audit history.

Provider transitions create the same risk. A technically complete transfer can still leave the incoming team asking why something was done.

Tom

That makes me think traceability has to be designed before anybody needs to use it. Trying to rebuild all of this at submission sounds painful.

Jullia

Well, it usually does make more sense to establish it prospectively. You decide how identifiers will work across systems, what metadata need to be retained, how external transfers will be specified and reconciled, and how programs, logs, approvals, and versions will be controlled.

You also need clarity around ownership. Outsourcing an activity doesn’t remove the need for sponsor oversight, so responsibilities need to be clear across the sponsor, CRO, laboratories, and technology providers involved.

Tom

Where does technology fit? Because there are increasingly sophisticated tools for showing data lineage, and it would be tempting to see the platform as the solution.

Jullia

Technology can make traceability much easier to inspect. Metadata relationships can be visualised, for example, so teams can follow connections across datasets, variables, analyses, and outputs. Automated checks can also help identify missing relationships.

But a visual lineage map is only as useful as the information underneath it. If the metadata is incomplete, or teams haven’t agreed which records are authoritative, the tool can present a very polished view of an incomplete chain.

Tom

There’s also another side to clinical traceability that we haven’t touched yet, because it isn’t all just about digital data.

Jullia

You’re right, the principle extends to clinical trial materials as well. That could mean an investigational medicinal product, comparator, kit, device, or biological sample.

For an investigational product, you may need to trace it from sourcing and manufacture through shipment, site receipt, allocation, dispensing, return, and ultimately destruction. Depending on the study, storage conditions and temperature excursions may also form part of that record.

Tom

Can you give an example of why the physical and digital sides have to connect?

Jullia

So, suppose an IRT or RTSM system shows that a particular kit was assigned to a participant. The site’s pharmacy and administration records provide evidence of what physically happened to that kit.

If those records disagree, you need to reconcile them. Perhaps the kit was assigned but not administered, or a transaction wasn’t entered correctly. Traceability means being able to resolve that difference rather than treating each system as an isolated record.

And barcode scanning, serialisation, sensors, those kinds of technologies can help of course, but we come back to the same point as with the clinical data. They can reduce manual transcription and give teams better records of movement or storage conditions, but they still don’t ultimately replace governance.

A sophisticated system won’t compensate for unclear kit identifiers, poorly defined responsibilities, or reconciliation that isn’t being performed. The technology needs to fit the trial and the information that genuinely has to be reconstructed.

Tom

Say if you were reviewing an outsourced study and wanted a quick sense of whether traceability was in good shape, what would you look for?

Jullia

I’d start by picking an important result and trying to follow it backwards. Can I identify the analysis dataset and variables? Can I understand the derivation? Can I get back to the relevant tabulation and source information without relying on somebody to explain undocumented steps?

Then I’d reverse the direction. Take an important source variable or external data stream and ask where it ends up. Which analyses depend on it, and what happens if it changes?

I’d also look closely at transfers and transitions. Are external-data specifications complete? Are reconciliation activities clear? If a study has moved between systems or providers, can you see what was migrated, what changed, and what limitations were identified? Because those moments are often where an otherwise reasonable traceability framework gets weakened.

Tom

Before we finish, what would you want listeners to keep in mind?

Jullia

Probably two things. First, traceability is about being able to reconstruct evidence, not simply retaining files. A result should remain connected to its source data, transformations, analysis method, program, and relevant decisions.

Second, outsourcing doesn’t prevent strong traceability, but it does make clear governance more important. When several organisations and systems are involved, standards, metadata, responsibilities, and transfer controls have to connect them.

With that, we’ve come to the end of today’s episode on clinical traceability. If you found this discussion useful, don’t forget to subscribe to QCast so you never miss an episode and share it with a colleague. And if you’d like to learn more about how Quanticate supports data-driven solutions in clinical trials, head to our website or get in touch.

Tom

Thanks for tuning in, and we’ll see you in the next episode.

About QCast

QCast by Quanticate is the podcast for biotech, pharma, and life science leaders looking to deepen their understanding of biometrics and modern drug development. Join co-hosts Tom and Jullia as they explore methodologies, case studies, regulatory shifts, and industry trends shaping the future of clinical research. Where biometric expertise meets data-driven dialogue, QCast delivers practical insights and thought leadership to inform your next breakthrough.

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