For decades, SAS has been the standard tool for statistical analysis in clinical trials and regulatory submissions. Most regulatory workflows, validation frameworks, and quality systems in pharmaceutical companies have been built around SAS. The ecosystem is mature, and many organizations treat “SAS ready” almost as a synonym for “submission ready.”

Today, this is changing.

Across the industry, more and more organizations are introducing R into their biostatistics environments. In some cases, this starts as a carefully controlled pilot for internal analyses or non critical endpoints. In other cases, it is already a strategic transformation with a defined roadmap, formal governance, and long term targets. The direction is clear: R is no longer viewed as a tool only for research or exploration, but increasingly as a central component of modern regulatory analytics.

Why is this happening?

1) Cost, flexibility, and reduced dependency

SAS is powerful, but enterprise licenses are expensive and can create long term dependence on a single vendor. License models can also influence how teams scale, because additional users, compute, or environments can come with extra cost and administrative complexity.

R, as open source software, removes license fees and allows organizations to build analytics environments with much greater flexibility. Teams can run R locally, on servers, in controlled virtual machines, or in cloud setups that match security and compliance needs. This matters not only for budget planning, but also for infrastructure strategy. Many organizations want standardized, automated, and reproducible environments, for example using containers and scripted deployments. R fits naturally into these modern operational approaches.

2) Innovation and faster access to new methods

Many modern statistical methods appear first in R. This includes advanced survival modeling, causal inference and propensity score workflows, Bayesian analyses, real world evidence analytics, and increasingly sophisticated model based techniques used in indirect comparisons and external control arms.

For organizations working close to the methodological frontier, R often provides earlier and broader access to new tools than commercial platforms. Just as importantly, these methods usually come with active academic and open source communities, transparent implementations, and peer reviewed references. That makes it easier to inspect assumptions, understand limitations, and build internal consensus on the right approach for a specific question.

This is not simply “R is better than SAS.” It is about the speed of methodological evolution and the ability to adopt high value methods when they become standard in the literature.

3) Reproducibility, transparency, and modern workflows

Regulatory science is not only about producing correct numbers. It is also about proving how those numbers were produced, with traceability and auditability across time, teams, and systems.

R integrates naturally with modern development practices such as version control, structured code review, reproducible reporting, and automated pipelines. Tools like Quarto or R Markdown support parameterized, reproducible outputs that can be regenerated consistently. Well designed workflows can connect code, data versions, and outputs in a transparent chain. This supports stronger documentation and more reliable quality control, especially as analyses become more complex.

4) The transition comes with real risk

This shift is not without challenges.

SAS has decades of regulatory precedent. Its validated environments and standardized pipelines are well understood by auditors and regulators. R, by contrast, is a flexible ecosystem with many packages and frequent updates. That flexibility is an advantage for innovation, but it can be a risk in a submission context if it is not tightly governed.

To use R responsibly for regulated work, organizations typically need:
A clear validation approach for the computing environment and critical packages. Strict version control for code and dependencies. Defined processes for package updates and impact assessment. Robust quality control standards, including independent programming and automated checks. Documentation that makes analyses inspectable and reproducible months or years later.

In other words, the hurdle is rarely statistical capability. The hurdle is operational maturity and governance.

5) Most organizations are not choosing SAS or R

In practice, most organizations are not choosing between SAS and R. They are choosing SAS and R.

SAS remains dominant for highly standardized tasks such as SDTM, ADaM, and core TFL production where long established processes, templates, and precedent matter. R is increasingly used for exploratory analyses, advanced modeling, real world evidence, and method development. Over time, selected production workflows are migrated to R under controlled conditions, typically starting with clearly scoped deliverables, strong QC, and predefined validation rules.

A common pattern is incremental adoption:

First, R is used for exploratory work and supplementary analyses. Next, R supports advanced methods that are difficult to operationalize elsewhere. Then, organizations move specific production components to R, for example figures, specialized endpoints, or model based outputs. Finally, full production pipelines become feasible once governance, training, and validation frameworks are mature.

Conclusion

The move from SAS to R is therefore not a technical fashion. It is a strategic decision about cost, innovation, regulatory risk, and long term digital maturity.

The key question is no longer whether R will play a central role in regulatory biostatistics, but how well organizations will manage the transition. Those who invest in governance, validation, and reproducible workflows can gain flexibility and methodological speed without compromising regulatory confidence. Those who treat R as “just another tool” without the required discipline risk inconsistency, audit issues, and loss of trust in outputs.

Manuel Pfister