Jake Gallagher, Worldwide Flex
Reflections on innovation and change in statistical programming
Artificial intelligence, the rise of open-source tools, and the steady evolution of data standards are changing how statistical programming and biostatistics get done. The interesting part is no longer whether these tools can help. It is about how to put them to work within regulated clinical research without sacrificing the accuracy and oversight that the work depends on. This piece looks at the practical questions teams are weighing now, including how to validate AI, how to govern it, how privacy and regulation shape its use, and where human judgment fits in the workflow.
AI in Clinical Workflows
AI has started to deliver real value in day-to-day clinical work. Generative AI can draft and check programming code, help with sample size estimation, summarize safety narratives, and speed up routine data review. In data management, it can flag outliers and inconsistent entries faster than a manual scan. In statistical programming, it can suggest code, document it, and surface errors earlier.
The value shows up when AI supports an expert rather than stands in for one. Where accuracy and compliance are not negotiable, an AI output is a draft to be checked. A generative model can produce text that looks right and is wrong, so the reviewer’s judgment is the control that keeps the output sound. That is why most teams are framing AI as assistance for experienced statisticians and programmers, with people accountable for what gets finalized for presentation.
From Interest to Evidence: Validating AI
The first practical question is validation. How do you show that an AI tool is good enough for the specific job you are asking it to do?
The FDA gave the industry a useful structure here. Its January 2025 draft guidance, Considerations for the Use of Artificial Intelligence to Support Regulatory Decision-Making for Drug and Biological Products, proposes a risk-based approach built around what it calls the context of use, meaning the specific role the model plays in answering a specific question. The more a decision relies on the model, and the larger the consequence if the model is wrong, the more evidence you need that the model is credible for that use. The guidance lays out a step-by-step path: define the question, define the context of use, assess the risk, plan and carry out the work to establish credibility, then document the results and judge whether the model is fit for the job.
For a biometrics team, that translates into habits already familiar from software validation. Write down what the tool is for, match the depth of testing to the risk, keep the evidence, and keep a clear record of versions and changes. An AI tool that drafts a non-critical summary needs lighter checking than one that influences an analysis in a submission. Tying the level of scrutiny to the context of use keeps effort where it matters.
Governance & Privacy
Validation answers whether a tool works. Governance answers who decides how it is used, on what data, and with what records. Both questions matter before an AI tool goes near study data.
Privacy is the sharpest concern. Health data is protected by HIPAA in the U.S. and GDPR in the E.U., and those rules apply regardless of how new the technology is. Putting protected health information into a general-purpose AI service is risky for two reasons. De-identification is never absolute, so some re-identification risk remains, and models can memorize fragments of their training data and reproduce them later. The safer pattern is to keep patient-level data out of public models, work with de-identified or synthetic data where possible, and use tools that run inside controlled, contracted environments with clear data-handling terms.
Good governance puts structure around the rest. A defined approval path sets which tools are allowed and for what. Audit trails record what the AI did and who reviewed it. Attention to bias matters for any model that touches patient data. Regulation is still forming, with the E.U. AI Act among the first broad frameworks, so a governance approach that is written down and revisited as rules settle will age better than one built around a single tool.
Open-Source Tools
Open-source languages, mainly Python and R, continue gaining ground in statistical programming, data visualization, and automation. Their flexibility, active user communities, and low cost make them attractive. Pairing them with established platforms can turn work that once took many hours into a much shorter task, freeing people for analysis that requires judgment.
The practical catch with open source is validation, and the industry has worked out sensible answers. The R Validation Hub, supported by the R Consortium, offers a risk-based way to qualify R packages for regulated use, so teams test the packages they depend on in proportion to the risk those packages carry. Reproducibility tooling, such as renv, records exactly which package versions an analysis has used, so results can be regenerated later. The work is not theoretical. The R Consortium’s submission pilots have sent all-R analysis packages to the FDA, and the agency was able to run the code and reproduce the tables, with a successful pilot review completed in 2024. One lesson from those pilots is worth repeating: the sponsor remains responsible for selecting well-maintained, reliable open-source packages.
None of this retires SAS. It remains central in clinical research thanks to its track record and regulatory familiarity. The healthy direction is for platforms to work together, with SAS, R, and Python each used for what it does best in a given study.
Data Standards as the Connective Tissue
Data standards are what allow all of this to remain reviewable. CDISC standards, mainly SDTM for tabulation and ADaM for analysis, give clinical data a common shape that regulators expect and that reviewers can follow. When data follows those standards, analysis is easier to trace from raw values to results, which is exactly what makes AI-assisted or open-source output something a reviewer can check rather than take on faith.
Standards also make tools interchangeable. If inputs and outputs follow CDISC, it matters less whether a table came from SAS or R because the structure and traceability remain consistent. The ongoing work is balancing standardization with the flexibility that novel designs and new data sources need, and that balance is where experienced data managers and programmers earn their keep.
Where People Fit
Across all these threads runs the same point: people are in control. Humans define the context of use, decide how much testing a tool needs, review what the AI produces, and put their names to what goes into a database or a submission. AI can make a programmer faster and a data manager sharper, but accountability stays with the team. Worldwide Flex builds its functional teams around that idea, pairing experienced statisticians, programmers, and data managers with tools that help them work faster while maintaining human-in-the-loop oversight.
Looking Ahead
The combination of AI, open-source tools, and solid data standards is opening a better way to work in statistical programming. The teams that benefit most will be those that treat practical questions, validation, governance, privacy, and oversight as part of the design rather than an afterthought. Handled that way, these tools improve speed and quality together, and that ultimately serves the patients the work is for.
References
- U.S. FDA. Considerations for the Use of Artificial Intelligence to Support Regulatory Decision-Making for Drug and Biological Products. Draft guidance for industry, January 2025.
- R Consortium, R Submissions Working Group. Pilot 3 (all-R ADaM and TLF package) successfully reviewed by the FDA, final response 2024.
- R Validation Hub (R Consortium and PSI). Risk-based validation framework for R packages in regulated environments.
