SAS to R Overview
The pharmaceutical sector is experiencing a notable trend toward embracing open-source programming languages for statistical analysis. Major pharmaceutical firms such as Roche, Biogen, GSK, Janssen, and others are dedicating substantial resources to transitioning their clinical coding from SAS to R. This move involves significant investments in both personnel and technological infrastructure. R’s application extends from conducting internal analyses to its emerging role in submissions to the FDA and various regulatory bodies.
SAS and R
SAS has been a long-standing leader in biostatistical analysis, offering a powerful suite of software that can handle large datasets and complex analyses. However, R is an open-source programming language and software environment supported by an extensive ecosystem and vibrant community. The CRAN central repository, which contains more than 20,000 curated and tested packages, is a unique open-source institution and has gained immense popularity for statistical computing and graphics.
Reasons Behind the Shift
- Cost-Effectiveness – R’s open-source nature makes it a cost-effective alternative to SAS, which is a commercial software. In a bid to reduce costs, pharmaceutical companies are adopting R, especially for research and development where costs can skyrocket.
- Advanced Statistical Techniques – R is continuously updated with the latest statistical techniques and methodologies due to its open-source community. This allows users to implement the latest methods in their research, an essential aspect in the ever-evolving field of pharmaceuticals.
- Flexibility and Customization – R supplies greater flexibility and customization options compared to SAS. Users can write their own functions, share scripts, and change existing packages, making it a preferred choice for specific, complex analyses that are common in pharmaceutical research.
- Growing Community and Support – The R community is rapidly growing, offering extensive support through forums, free packages, and tutorials. This community-driven support is invaluable for troubleshooting and learning, making R an attractive choice for many organizations.
- Integration with Other Technologies – R integrates well with other data science languages and tools, offering a seamless experience in data analysis workflows. This integration is vital in today’s data-driven world where various tools and languages are used in conjunction.
Industry Impact
The migration from SAS to R is not just a shift in software but stands for a broader move towards open-source tools in scientific research. As more companies adopt R, there’s a collective enhancement in the capabilities of pharmaceutical research, driving innovation and efficiency in the industry.
SAS to R Conclusion
The migration from SAS to R within the pharmaceutical industry underscores a shift towards more cost-effective, flexible, and innovative statistical analysis. As this trend continues, it is expected to foster a more collaborative and advanced research environment, helping the industry and patients alike. This move is a testament to the industry’s commitment to adopting the best tools for life-saving research and development.
ProCogia has helped a number of customers migrate their SAS statistical analyses to R. Our end-to-end solutions start with understanding your SAS code and requirements, followed by converting the code to R, testing and deploying. ProCogia’s team has expertise in converting a wide range of SAS code, including data manipulation, statistical analysis and macro programming.
To learn more about how ProCogia can help you with your SAS to R migration, please reach out to us for a conversation.
