In this QCast episode, co-hosts Jullia and Tom unpack CDISC standards, which is the comprehensive framework that governs how we capture, organise, and submit clinical trial data from first protocol draft through pre-clinical studies. We'll guide you through the core content models such as the Protocol Representation Model, CDASH, SDTM, ADaM, and SEND as well as explain the exchange formats like ODM-XML and Define-XML that keep disparate systems talking.
You'll discover why adopting CDISC end-to-end reduces manual work, accelerates regulatory review, and builds a library of analytics-ready datasets. We also share practical steps for auditing your studies, running pilot mappings, investing in validation tools, and training your team. Whether you're a data manager, statistician, or regulatory specialist, this episode will you give you the insights you need to standardise with confidence.
What are CDISC Standards?
CDISC standards are a harmonised framework of models and formats that dictate how to organise, describe, and exchange clinical trial data from first draft protocol through pre-clinical studies to analysis submissions, ensuring everyone (from sponsors to regulators alike) uses the same language.
Core Components of CDISC Standards
• Protocol Representation Model (PRM) is a machine-readable structure for objectives, study arms, and visit schedules.
• CDASH are standardised case report form fields and response options for consistent data capture.
• SDTM & SEND are tabulation models that organise human (SDTM) and non-clinical (SEND) data into domains like labs and adverse events.
• ADaM are analysis-ready datasets with every derived variable traceable back to its source.
• Controlled Terminology & Therapeutic Area Standards are share vocabularies and disease-specific extensions to maintain uniform meaning.
• Exchange Formats (ODM-XML, Define-XML, etc) are metadata-rich containers that carry datsets, variable definitions, and statistical recipes seamlessly between systems.
How CDISC Outperforms Legacy Methods
By replacing bespoke mappings and spreadsheet juggling with automated conversions and built-in validation, CDISC standards reduce manual rework, submission defects, and accelerate regulatory review, all while enabling plug and play reuse of clean, analytics-ready data.
Operational Essentials for Successful Implementation
• Conduct a gap audit of both active and legacy studies to prioritise work.
• Pilot an end-to-end mapping of one dataset into SDTM and ADaM to surface tooling or workflow gaps.
• Invest in validation tools (e.g. Pinnacle 21) and integrate terminology checks into automated pipelines.
• Provide formal CDISC training and certification for data managers, programmers, and statisticians.
• Select EDC and analytics platforms that natively export ODM-XML, Define-XML, etc.
Common Pitfalls to Avoid
• Tackling controlled terminology too late in the submission process.
• Underestimating the effort required to produce Define-XML and Analysis Results Metadata
• Leaving legacy datasets stranded without a clear transformation pipeline.
• Treating CDISC adoption as a one-off project instead of an ongoing quality-assurance practice.
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.
Jullia
Today we’re going to be diving into understanding CDISC standards in clinical research. In short, CDISC is the rulebook that tells the world how to capture, format and submit clinical trial data. If you work with case-report forms, programme SDTM datasets or guide submissions through the FDA or EMA, CDISC is likely already part of your workflow. And as regulators globally now mandate these formats, understanding them isn’t just a nice-to-have, it’s critical path work that’s essential.
Tom
Yeah, think of CDISC as the universal power adaptor for clinical research: one consistent plug so sponsors, CROs and regulators can exchange data without fiddly converters. In this episode, we’ll unpack the full landscape, from high-level content models like the Protocol Representation Model, exchange formats like ODM-XML and even the emerging tech supporting validation. By the end of our discussion, you’ll know not only what the standards are, but why early adoption trims timelines, reduces costs and raises your compliance game.
Tom
Let’s start with the core building blocks. When someone says “CDISC content standards”, what sits under that umbrella and how do they link together from first protocol draft to final statistical report?
Jullia
Picture the study lifecycle as a relay. The Protocol Representation Model opens the race by structuring objectives, arms and schedules in a machine-readable template, reducing ambiguities later on. Next, CDASH governs how sites capture data. Your case-report forms have uniform field names and picklists so downstream mapping is painless. Once the raw data lands, SDTM packages observations into tidy domains like demographics, adverse events, and labs so regulators can query trends without wrestling spreadsheets. ADaM then reshapes those SDTM tables into analysis-ready files with every derived value explicitly traceable back to the source. Beyond the well-known trio of CDASH, SDTM, and ADaM sit some equally vital pieces of the puzzle. SEND standardises pre-clinical animal studies, ensuring safety signals are comparable across compounds. QRS, on the other hand, harmonises questionnaires and rating scales to make sure patient-reported outcomes line up across every trial. Then there’s Controlled Terminology which locks down vocabulary to ensure different phrases and terms mean the same thing everywhere, and Therapeutic Area Standards give disease-specific blueprints for oncology, cardiology and beyond. Together they form an unbroken chain of custody for your data, boosting quality and shaving weeks off review cycles.
Tom
Thanks, Jullia. However, trials rarely live in one system these days. More often than not they’re in EHR feeds, ePRO apps, central labs, and more. So, how do CDISC exchange standards make sure those silos can talk without garbling the message?
Jullia
That’s a good question, Tom. So, the hero here is ODM-XML. Think of it as the padded envelope carrying your metadata, audit trail and CRF layouts between platforms. For full datasets, Dataset-XML wraps SDTM tables in a regulator-friendly structure to make sure nothing gets lost at import. Define-XML travels alongside as the field guide, ensuring every variable, unit and code list is spelled out in plain language for reviewers. When you submit results, Analysis Results Metadata records the statistical recipe so authorities can rerun analyses with confidence. And of course, we can’t forget the Laboratory Data Model. This standardises the representation of laboratory test results in clinical trials around the world, cutting re-checks when units differ. Together these formats are the shipping labels that stop your data crate going astray between collection, analysis and submission.
Tom
While these standards are all well and good, there’s no denying that budgets can be tight. What’s the concrete return on investing in full-scale CDISC adoption, and how should teams roll it out without derailing ongoing studies?
Jullia
That’s a fair point. Broadly speaking, there are four main benefits. First is efficiency. Automated mapping means fewer manual touch-ups and faster study timelines. Then there are cost savings. Less rework and smoother submissions can cut contractor hours. Next is regulatory speed. Regulators can review standardised datasets far quicker. Finally, we’ve got data re-use. Once your studies share a common structure, meta-analysis and real-world evidence projects become almost plug-and-play. Implementation, on the other hand, works best as a six-step loop. Map raw data to SDTM, align with ADaM for traceability, validate via tools like Pinnacle 21, generate Define-XML, train staff continuously and automate wherever possible using advanced tools to spot anomalies. But of course, as with anything, there are some common challenges to consider. These include legacy spreadsheets that pre-date CDISC, region-specific quirks, and forgetting controlled terminology until submission week. The best way to avoid this is by ring-fencing a transformation pipeline for historic trials and prioritising terminology checks into nightly validation runs. It’s important to address culture too by implementing ongoing workshops and certification to keep everyone in sync.
Tom
All right, before we officially start wrapping things up, let’s have a pen-and-paper moment. What should listeners actually do in the next quarter to stay, or become, CDISC compliant?
Jullia
The age-old question. First things first, start with a gap audit. List every active and legacy study with its current format. Prioritise those heading for submission within twelve months. Next, stand up a pilot mapping project. One phase-III dataset converted end-to-end into SDTM and ADaM can help expose tooling gaps early on. Third, invest in validation software. The free Pinnacle 21 Community edition is fine to begin with when you’re working on pilots, but enterprise versions can catch edge-case errors like inconsistent controlled terminology. Fourth, formalise training. A two-day CDASH-to-SDTM boot camp for data managers pays back immediately. Fifth, future-proof your tech stack. Insist new EDC or statistical platforms export ODM-XML, Define-XML and ARM out of the box. And finally, implement continuous monitoring into SOPs so flagging using advanced tools suggests fixes long before the data-lock scramble. If you miss any of these, you risk delays, rejections, or worse, a complete data remodel just weeks before your PDUFA date.
Tom
Thanks Jullia. Before we end today’s discussion, let’s recap the top takeaways. First, the CDISC content standards, from the Protocol Representation Model right through to SEND for pre-clinical studies, gives you a clear, step-by-step blueprint for organising every piece of trial information. In other words, you’ll know exactly how to frame your protocol, capture your data, and present your results so that regulators can follow along without getting lost. Second are the exchange standards. Think ODM-XML for carrying your case-report forms, Define-XML for spelling out every variable and code list, and the Lab Data Model for standardising test results. These are like a set of reliable connectors. They ensure your EDCs, analysis tools and submission portals all speak the same language and keep information flowing smoothly.
And third, if you commit to adopting the full suite of CDISC standards early on, you’ll reap tangible rewards. These include shorter study timelines because less time is wasted on manual fixes, lower costs thanks to fewer last minute reworks, and a growing library of clean analytics-ready datasets that you can simply plug into future projects at the click of a button.
Jullia
That’s spot on, Tom. And with that, we’ve come to the end of today’s episode on understanding CDISC standards in clinical research. If this discussion inspired you to dust off a legacy dataset or rethink your submission pipeline, head over to the CDISC Library for the latest models and guidance. And of course, don’t forget to subscribe to QCast so you never miss an episode and share our show with others. If you’d like to learn more about how Quanticate supports data-driven solutions in clinical trials, don’t hesitate to head to our website or get in touch.
Tom
Thanks for listening and we’ll see you in the next episode.
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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