R Programming in Clinical Trials: Benefits & Uses

Author: Artemis Toumazi

, Senior Biostatistician
Published on: 22/10/2026

Estimated Reading Time: 4 min

Excelya PV & Safety Department

Executive Summary

In clinical research, biometrics teams are expected to do more than produce analyses at the end of a study. They contribute to study design, support decision-making, accelerate reporting, and improve the traceability of results throughout the project lifecycle. In this context, R is more than a programming language: used appropriately, it can become a strategic tool for the whole biometrics function. Regulators such as the FDA and EMA accept analyses produced with open-source software when the methods, processes, and outputs are appropriately controlled, validated, and documented [1]. Industry initiatives, including international regulatory submissions using R, are also helping demonstrate how open-source workflows can be applied in a regulated environment.

Jump to section

1Clinical Safety Alignment

2SAE Set-Up Essentials

3
Conclusion: Sustainable SAE Alignment

4
FAQ

Clinical Biostatistics & Statistical Programming

Benefits of R Programming Across the Clinical Trial Lifecycle

Flexibility across the clinical trial lifecycle

One of R’s main strengths is flexibility. From early protocol discussions to final outputs, it can support sample-size exploration, simulations, data visualization, statistical modelling, automatic SDTM and ADaM dataset creation with sdtm.oak and admiral respectively [2, 3], table-listing-figure generation with packages like ggplot2, gtsummary and tplyr, and reproducible reporting with Markdown and Quarto [4, 5, 6]. This enables statisticians and programmers to move beyond a purely executional role and contribute more proactively to study strategy and methodological discussions.

Rapid insight and modern deliverables

R is particularly valuable when rapid insight is needed. During study planning, simulation-based approaches can help assess assumptions, compare design options, and communicate risks. During study conduct, dashboards and exploratory analyses can support data review, centralized monitoring, risk-based quality management, and DMC review. R can also support interactive HTML deliverables and Shiny-based tools that allow clinical teams to explore data dynamically and adaptative reporting. Reusable code and automated workflows can reduce manual effort and improve consistency, making R attractive from both an efficiency and a cost-effectiveness perspective [7].

Transparency, reproducibility, and collaboration

Well-structured R scripts and reproducible documents create a clear link between data, methods, and results. This traceability is essential in clinical research, where outputs must be explainable, reviewable, and reproducible. R’s open-source ecosystem also gives biometrics teams access to a broad range of packages and encourages shared standards through common templates, reusable functions, internal packages, and harmonized workflows. These practices reduce duplication and help build collective expertise across Statistics and Programming.

Using open source within a robust quality framework

Strategic adoption of R does not mean replacing established tools overnight. In many organizations, R will coexist with SAS and other validated systems. The important point is to use each tool where it adds the most value and to apply appropriate governance around open-source workflows, including programming standards, version control, validation principles, documentation, training, and quality review.

Conclusion | Excelya Expertise

R Programming in Clinical Trials: Excelya’s Biostatistics Expertise

At Excelya, our Statistics and Programming team combines methodological expertise with practical R capabilities and a quality-focused approach. This allows us to support sponsors with complex study designs, advanced statistical analyses, exploratory work, automation, standardized clinical data workflows, and modern reporting solutions. Internal knowledge sharing, reusable tools, peer review, and training help us apply R consistently and pragmatically across projects.

Ultimately, R becomes strategic when it helps biometrics teams make better decisions, work more efficiently, and communicate results with greater clarity. At Excelya, we position R not as a replacement for every existing solution, but as a powerful addition to the biometrics toolbox and a catalyst for innovation, collaboration, and high-quality delivery for our sponsors.

References

Sources and Supporting Evidence

[1]

Straub, B., 2024. Piloting into the Future: Publicly Available R-based Submissions to the FDA. In: PharmaSUG 2024 Proceedings, Paper SS-344. PharmaSUG Conference 2024. Available at:
PharmaSUG 2024 Proceedings, Paper SS-344 [Accessed 14 September 2026].

[2]

pharmaverse, n.d. sdtm.oak: SDTM Data Transformation Engine. Available at:
pharmaverse sdtm.oak documentation [Accessed 14 September 2026].

[3]

pharmaverse, n.d. admiral: ADaM in R Asset Library. Available at:
pharmaverse admiral documentation [Accessed 14 September 2026].

[4]

Sjoberg, D.D., Whiting, K., Curry, M., Lavery, J.A. and Larmarange, J., 2021. Reproducible summary tables with the gtsummary package. The R Journal, 13(1), pp. 570–580. Available at:
The R Journal: gtsummary package.

[5]

Miller, E., Stackhouse, M., Tarasiewicz, A. and contributors, 2026. Tplyr: A Traceability Focused Grammar of Clinical Data Summary. Package Reference Manual, Version 1.4.1. Available at:
Tplyr package reference manual [Accessed 14 September 2026].

[6]

Chang, W., Cheng, J., Allaire, J.J., Sievert, C. and contributors, 2026. shiny: Web Application Framework for R. Package Reference Manual, Version 1.14.0.9000. Posit Software, PBC. Available at:
Shiny package reference manual [Accessed 14 September 2026].

[7]

Higgins, P.D.R., n.d. Chapter 40: Quarto is a Next-Generation RMarkdown. In: Reproducible Medical Research with R. Available at:
Reproducible Medical Research with R: Quarto [Accessed 14 September 2026].

About the Author

Artemis Toumazi

Senior Biostatistician at Excelya

Senior Biostatistician with over 12 years of experience in clinical research, statistical design, analysis, and reporting. Her expertise spans Phase I to IV trials, SAP development, statistical reporting, SAS/R programming, and methodological support, with contributions to Bayesian modeling research.

Frequently Asked Questions

What are the benefits of R in biostatistics?

R offers advanced statistical modelling, flexible data visualisation, reproducible analyses and automated reporting. It helps clinical research teams improve efficiency, maintain transparency and produce consistent statistical outputs.

Can R support regulatory submissions?

Yes. R can support regulatory submissions by generating clinical datasets, statistical analyses, tables, listings and figures. Packages such as admiral, sdtm.oak and Tplyr support standardised workflows and traceability, while appropriate validation and quality controls help ensure regulatory compliance.

What are the benefits of R in biostatistics?

How does Excelya use R in clinical research?

Excelya’s Statistics and Programming team uses R alongside its biostatistics expertise to support complex study designs, advanced statistical analyses, automation and clinical reporting. Through reusable tools, peer review and internal knowledge sharing, Excelya helps sponsors improve efficiency and maintain high-quality clinical data workflows.