Clinical trials play a crucial role in the development and evaluation of new medical treatments and interventions, providing valuable insights into the safety and efficacy of drugs, medical devices, and therapeutic interventions. However, the data generated from clinical trials is often complex and requires rigorous statistical analysis to draw meaningful and accurate conclusions. Statistical analysis in clinical trials involves the application of statistical methods to analyze the data collected during a trial and aid researchers in making informed decisions and drawing accurate conclusions based on the observed outcomes. This crucial step of clinical research also allows the data management team of a sponsor or contract research organization (CRO) to quantify the effects of interventions, assess the variability and reliability of results, and determine the statistical significance of the findings.
In the field of clinical research and biostatistics, two programming languages, R and SAS, have been widely used for biostatistical analysis and data management. Both languages have advantages and disadvantages, and biopharma companies often find themselves comparing R and SAS to determine which one is better suited for their specific research needs. Read on as we review the basics of R and SAS, consider the benefits and drawbacks of each program, and delve into ongoing initiatives to promote open-source languages in clinical trials.
R and SAS in Clinical Trial Research: Reviewing the Basics
R: An open-source statistical software
R is an open-source programming language and software environment that is specifically designed for statistical computing and graphics. It was developed by statisticians and data scientists to provide a flexible and comprehensive tool for data analysis and visualization. R offers a wide range of statistical techniques, including descriptive statistics, hypothesis testing, regression analysis, time series analysis, and machine learning; it also provides a rich set of functions and methods for data manipulation, data visualization, and statistical modeling. R has a vibrant community of users who contribute to the development of new packages and provide support through online forums and resources, particularly because its open-source nature makes it accessible to a wide range of users, including researchers, students, and professionals. Given that it can be freely downloaded and installed on various operating systems, such as Windows, macOS, and Linux, R’s popularity has grown rapidly in recent years, and it is now widely used in academia, research institutions, and industries, including finance, healthcare, and marketing.
SAS: A comprehensive statistical software suite
SAS (Statistical Analysis System) is a proprietary software suite developed by the SAS Institute for advanced analytics, business intelligence, and data management. SAS offers a comprehensive set of tools and capabilities for data analysis, statistical modeling, and reporting, as well as provides an integrated environment for data preparation, data exploration, statistical analysis, and result interpretation. SAS also offers powerful data management capabilities, allowing users to import, clean, and transform data from various sources. It provides a comprehensive set of data manipulation functions, as well as tools for data quality assessment and data integration. SAS’s data management features enable users to efficiently manage large and complex datasets, making it suitable for industries with extensive data requirements, such as clinical research.
Biostatistics with R versus SAS: Benefits and Drawbacks
One of the key advantages of SAS is its robustness due to its long history of being used in industry, and it has been thoroughly evaluated and validated. As a result, SAS has been a primary option to provide a stable and reliable platform for conducting statistical analyses in clinical research. Additionally, this software offers a wide range of specialized procedures and modules specifically designed for clinical research, making it a long-preferred choice for researchers working in this field before R became available. However, one significant drawback of SAS is its high cost; CROs, sponsors, and researchers need to purchase licenses, which can be quite expensive annually, especially for smaller research institutions and startups. This cost barrier has led to the exploration of alternative options, such as R. Since R is an open-source language and provides comparable analytical capabilities to SAS at no cost, it has gained traction in recent years, particularly in the pharmaceutical industry and research institutions. The open-source nature of R allows for continuous development and innovation, with a vast community of users contributing to its growth, resulting in a wide range of packages and libraries specifically tailored for clinical research and biostatistics.
Growing Adoption of Open-Source Languages in Clinical Research
Although the R language is arguably more powerful without the exorbitant costs associated with obtaining a SAS license, the latter has still remained the industry standard for years and is deeply entrenched in the clinical research field. There has been hesitation on the part of some sponsors in adopting open-source languages like R, in large part due to a lack of precedence, but it has been gaining movement in recent years. This was especially encouraged by the FDA’s push for the industry to migrate towards R, resulting in a growing recognition of the advantages and capabilities that R offers. Contrary to popular belief, the FDA does not require the use of any specific software for clinical trial research and their ICH E9 guidance document on statistical principles for clinical trials does not explicitly discuss specific statistical software requirements. This further supports the notion that R is a viable option for conducting clinical trial research and that sponsors have the flexibility to choose the programming language that best suits their needs.
Furthermore, several organizations and initiatives are actively promoting and supporting the use of R in clinical research. One such initiative is the R Consortium, a leading group that collaborates closely with the FDA, which provides guidance, examples, and resources to facilitate the adoption of R in the industry. Their collaboration with regulatory agencies ensures that R-based submissions meet the necessary standards and requirements, giving researchers and organizations the confidence to use R in their clinical research. Pharmaverse is another organization dedicated to promoting the adoption of R in clinical research. Their mission is to overcome the barriers associated with open-source languages and provide the necessary support and resources for sponsors to confidently use R in their research. By creating a collaborative environment and sharing success stories, Pharmaverse aims to inspire and empower researchers to embrace the potential of R in their clinical research endeavors.
The use of open-source languages, especially R, has become increasingly popular in the pharmaceutical industry and research institutions. Although there may be some industry hesitation to fully embrace R, the availability of resources and support from organizations like the R Consortium and Pharmaverse is bridging this gap. Researchers and organizations are starting to recognize the advantages and capabilities that R offers, and its adoption in clinical research is on the rise.
Vial CRO Supports the Movement to Leverage R Software
At Vial, we support the movement to leverage R in clinical trial research and encourage researchers and sponsors to explore the capabilities of this versatile language. By leveraging the power of R, researchers can conduct comprehensive statistical analyses, generate high-quality TLFs, and contribute to the advancement of clinical trial research. When comparing R and SAS for clinical trial research, it is evident that R offers several advantages over SAS. It is a powerful and open-source language, providing free and democratized access to researchers. The FDA, in collaboration with various organizations such as the R Consortium and Pharmaverse, is actively promoting the use of R in clinical trial research.
Despite the predominance of SAS, initiatives like these are working towards overcoming the barriers and empowering researchers and sponsors to embrace the potential of R in their clinical trial research. With the ongoing support from a growing number of organizations and initiatives supporting this shift in the industry, the adoption of R in clinical research is expected to continue growing, further solidifying its position as a valuable tool in the field.
Vial is a full-service CRO that also recognizes the role of technology in the future of clinical research and is paving the way for modernized clinical research through digital innovation. Trusted by leading sponsors, our specialized teams deliver shorter study timelines, quality affordable services, and a clinical trial experience that puts you first.
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