Rethinking Programming Validation and Traceability in Clinical Trials

A Discussion with Andrew (Andy) York

**Episode Summary ** What does “quality” really mean in statistical programming?

In this episode of The Effective Statistician, I speak with Andrew (Andy) York about the evolving world of programming validation, traceability, and quality assurance in clinical trials. Andy has decades of experience in statistical programming, leadership roles across pharma and CROs, and now works with AI-driven solutions focused on improving validation and traceability.

We discuss why traditional approaches to validation are becoming increasingly difficult to sustain, how expectations from regulators continue to grow, and why traceability is far more than just linking programs and datasets.

Andy also shares how modern AI-powered tools can automatically map programming workflows, connect datasets and outputs, and create end-to-end traceability from raw data to final tables, figures, and listings.

If you work with statistical programming, clinical data workflows, submissions, or validation processes, this episode will challenge some long-held assumptions and introduce you to where the future may be heading.

**Why You Should Listen **

• Learn what “quality” in programming really means beyond simply writing working code
• Understand the challenges of maintaining traceability across complex clinical trial workflows
• Discover why manual validation processes are becoming harder to scale
• Hear how AI is starting to transform validation and traceability in programming
• Explore the balance between regulatory expectations, efficiency, and confidence in outputs
• Gain insights from someone who has seen the evolution of statistical programming from the very beginning

**Episode Highlights **

• 00:01:30 — Andy York’s journey into statistical programming Andy shares how he started programming during the early days of SAS in pharma and how the role of programmers evolved over the decades.

• 00:04:41 — What does programming quality actually mean? We discuss confidence in outputs, customer expectations, regulatory requirements, and creating programs that your future self can still understand years later.

• 00:06:46 — The regulator’s perspective on validation and traceability Andy explains why full traceability from raw data to final outputs is essential for regulatory confidence.

• 00:08:15 — The limitations of traditional traceability approaches We reflect on the common experience of manually navigating folders, programs, and datasets to reconstruct programming logic.

• 00:09:45 — How automated traceability changes the game Andy explains how modern tools can automatically create end-to-end traceability matrices across programs, datasets, and outputs.

• 00:10:45 — Forward traceability vs. backward traceability A fascinating discussion about not only tracing outputs back to source data, but also understanding where every data point flows forward through the analysis process.

**Links and References: **

• Verisian - https://verisian.com/
• Bayer case study https://verisian.com/customer-stories/how-bayer-uses-verisian-ai-to-automate-submission-document-generation