With more than 18 years of experience in the clinical research industry, I have worked extensively with SAS. However, in recent times, R has emerged as a groundbreaking tool in data analysis. In this article, I compare the use of SAS and R in clinical development, aiming to determine which tool might be the best fit to meet our requirements.

In clinical trials, data analysis is crucial for evaluating the safety and efficacy of new treatments. Both SAS and R offer powerful capabilities, but they have distinct features and benefits that can impact their utility in different contexts.

SAS: The Gold Standard

SAS has long been the gold standard in the clinical trial industry. Thanks to SAS Institute Inc., for the discovery of this powerful tool that has been a pioneer in statistical analysis, data management, and reporting for decades. Some of its advantages include:

Reliability and robustness: SAS has a strong foothold in the pharmaceutical industry and regulatory agencies like the FDA. It offers a wide range of pre-built procedures and functions tailored for clinical trials, making it a reliable choice for standardized analyses and regulatory submissions.

It provides strong compliance with data security and audit trails, which is crucial for maintaining regulatory standards. SAS also has an extensive history, indicating that it’s well-integrated into regulatory submission processes, providing consistency and a proven track record of compliance.

User-friendly technology with comprehensive support: For someone with a programming background, SAS’s user-friendly interface and extensive documentation make it accessible and easy to learn. The software provides a more guided experience, which can be advantageous for users who prefer a structured approach. SAS also offers professional support, training, and a comprehensive knowledge base. This comprehensive support can be invaluable for organizations needing guaranteed assistance and formal training.

The robustness, reliability, and extensive validation have made SAS indispensable for clinical trials. However, there are some challenges in its application:

High cost: SAS is a commercial product with a significant licensing fee. This can be a barrier for smaller organizations or those with limited budgets. Additionally, we often need the right expertise to fully leverage the potential of SAS, and the cost of training and maintaining this expertise can add to the overall expense.

Flexibility: While SAS offers a wide range of pre-built procedures and functions tailored for clinical trials, it is less flexible for custom or non-standard analyses.

R: The Flexible Innovator

R is an open-source programming language and software environment designed by statisticians and researchers for statistical computing and graphics. While its uptake has been slower in our industry, it is quickly becoming a popular tool for data analysis and submissions. Some of the advantages of using R include:

Cost-efficiency: Being an open-source programming language, R makes an attractive option for many users. Its zero-cost barrier is particularly appealing for academic researchers, startups, and non-profits. The open-source nature also allows users to contribute to and benefit from a large repository of packages and extensions.

Flexibility and customization: R offers a rich ecosystem of packages and libraries, making it highly adaptable and customizable for various analytical needs. Its vast collection of packages allows users to perform a broad array of statistical techniques and specialized analyses, particularly useful for innovative or unconventional methods that are not supported by standard software packages.

Cutting-edge innovations: R is becoming increasing useful in clinical development, especially for exploratory analyses and academic research. Its ability to perform rigorous statistical analyses and the increasing number of validation tools are helping it find its footing in the regulatory environment.

Community support: R benefits from a vibrant and active community of users and developers. A large number of forums, mailing lists, and online resources provide extensive support and collaboration opportunities.

While R is gaining traction in the clinical trial industry, there are some challenges in its application as well:

Learning difficulties: R has a steeper learning curve, especially for those unfamiliar with programming. While it has the ability to write custom scripts, they require a higher level of expertise.

Data handling and regulatory concerns: As an open-source tool, R’s data handling practices might raise concerns regarding the sensitive data required in clinical trials. It is also yet to establish itself in the regulatory environments.

Lack of formal support: The absence of formal support can be a downside for organizations needing dedicated assistance, relying instead on the user community for troubleshooting and guidance.

Final Takeaway

Both SAS and R have their place in the clinical trial industry. SAS is a proven, reliable choice with strong regulatory backing, while R offers flexibility, cost efficiency, and cutting-edge innovations. The decision ultimately depends on the specific needs of the trial, the budget, and the expertise available. By understanding the strengths and limitations of each tool, clinical trial professionals can make an informed choice that best aligns with their project goals and requirements.

I believe that leveraging both SAS and R and finding ways to integrate them can provide the most effective solution for clinical trial data analysis. Each tool brings unique strengths to the table and using them in tandem can optimize the analytical capabilities needed to drive successful clinical outcomes.

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