Can R Replace SAS in Regulatory Clinical Submissions?
- IDDCR Global Team
- Jan 18
- 3 min read
For decades, SAS has been the gold standard for statistical analysis and reporting in clinical trials. However, with the rapid rise of R programming—an open-source, highly flexible statistical language—the clinical research industry is asking an important question:
Can R replace SAS in regulatory clinical submissions?
The short answer is: not entirely—yet. The practical answer is: R is increasingly becoming a powerful and accepted companion to SAS.
This article explores the current regulatory landscape, the role of CDISC standards (SDTM & ADaM), validation challenges, and why a hybrid SAS + R workflow is emerging as the most realistic and future-ready model.
1. Current Regulatory Landscape: Where Do Regulators Stand?
Global regulatory agencies such as US Food and Drug Administration (FDA) and the European Medicines Agency (EMA) do not mandate the use of a specific statistical software for clinical trial analysis. Instead, they focus on:
Data integrity
Traceability
Reproducibility
Validation of results
Compliance with CDISC standards
Key Reality
SAS is widely accepted because of its long-standing use, mature validation frameworks, and regulator familiarity.
R is accepted if the sponsor can demonstrate:
Proper validation
Controlled programming environment
Reproducible outputs
In fact, the FDA itself uses R internally for data analysis and visualization—an important signal of growing confidence in R.
2. CDISC, SDTM, ADaM & the Role of R
Understanding CDISC Standards
Regulatory submissions require datasets structured according to CDISC standards, primarily:
SDTM (Study Data Tabulation Model) – submission-ready raw data
ADaM (Analysis Data Model) – analysis-ready datasets supporting TLFs
Traditionally, SAS dominates SDTM and ADaM development due to:
Extensive use of PROC SQL, DATA steps
Availability of industry-validated macros
Familiarity among reviewers
Can R Handle SDTM and ADaM?
Yes—technically and practically, R can:
Read and write XPT (transport) files
Manipulate SDTM and ADaM structures
Perform complex derivations
Generate TLFs aligned with ADaM
Popular R packages support this ecosystem:
haven – for SAS XPT files
dplyr, tidyr – data manipulation
ggplot2 – advanced visualizations
rmarkdown – reproducible reporting
The Catch
While R can generate SDTM/ADaM-compliant datasets:
Industry-wide standard R macros are still evolving
Reviewer familiarity with SAS outputs remains higher
Most sponsors still prefer SAS-generated SDTM/ADaM for submission
3. Validation Challenges with R in Regulatory Submissions
Validation is where R faces its biggest hurdle.
Why SAS Has an Advantage
SAS software is commercially validated
Version control is centralized
Extensive historical acceptance by regulators
Challenges with R
R is open-source, which means:
Frequent package updates
Dependency management complexity
Need for user-defined validation frameworks
How Organizations Address This
Sponsors and CROs using R typically:
Lock R versions and packages
Use containerized environments (e.g., Docker)
Perform Installation Qualification (IQ), Operational Qualification (OQ), and Performance Qualification (PQ)
Maintain detailed documentation and SOPs
Key Insight: R is acceptable—but validation responsibility lies entirely with the organization.
4. The Rise of the Hybrid SAS + R Workflow Model
Rather than replacing SAS, the industry is moving toward a hybrid model that leverages the strengths of both tools.
Typical Hybrid Workflow
Clinical Activity | Preferred Tool |
SDTM development | SAS |
ADaM development | SAS |
Primary TLFs for submission | SAS |
Exploratory analysis | R |
Data visualization | R |
Dashboards & insights | R |
Reproducible reports | R Markdown |
Why This Model Works
Meets regulatory expectations
Reduces programming cost
Enables innovation and advanced analytics
Improves efficiency and insight generation
Many leading CROs and sponsors now expect programmers to be SAS + R proficient, not one or the other.
5. So, Can R Replace SAS?
The Honest Answer
R cannot fully replace SAS today for regulatory submissions
R can strongly complement SAS
Future submissions may increasingly include R-generated outputs
The Strategic Answer
Organizations that invest early in R capability—alongside SAS—will be:
More agile
More cost-efficient
Better prepared for AI-driven clinical research
6. What This Means for Clinical Research Professionals
If you are a:
SAS Programmer
Clinical Data Manager
Biostatistician
Clinical Data Scientist
Learning R is no longer optional—it is a strategic career move.
The future belongs to professionals who can:
Work within regulatory frameworks
Combine traditional compliance with modern analytics
Speak both SAS and R fluently
Conclusion
R may not replace SAS overnight—but it is reshaping how clinical trials are analyzed, visualized, and understood. The most successful organizations and professionals will not choose between SAS or R—they will master SAS and R together.
Comments