Comparing SAS vs R for Clinical Trials Statistical Programming: Will R Replace SAS in 2024?
Among all the professionals in clinical research working with data and statistics, an emerging debate is whether SAS software or R is more suitable and beneficial for statistical programming in clinical trials.
For many years, SAS software has been the “golden standard” for statistical programming and analysis. However, in recent times, R is gradually gaining vast popularity in the drug development realm.
What’s the to-go option for researchers? Will R programming replace SAS in 2024? Let’s find out!
What is Statistical Programming in Clinical Trials?
Statistical programming in clinical trials involves handling various aspects of trial data, including organizing, cleaning, analyzing, and summarizing the data collected during clinical research studies. The key components of clinical trials’ statistical programming include:
- Sample Size Estimation: Determining the number of participants required for a study to ensure statistical power.
- CRF Annotation: Annotating Case Report Forms (CRFs) to ensure that the collected data aligns with the intended analysis.
- SDTM & ADaM Preparation: Converting raw data into standardized formats like Study Data Tabulation Model (SDTM) and Analysis Data Model (ADaM) for regulatory submission and analysis.
- TLFs Generation: Creating Tables, Listings, and Figures (TLFs) to present study findings and results.
- Data Validation and CDISC Compliance: Ensuring data quality, consistency, and adherence to Clinical Data Interchange Standards Consortium (CDISC) standards for regulatory compliance.
- Define.xml Development: Creating metadata that defines and describes the structure and content of datasets.
Using SAS for Clinical Trials Statistical Programming:
By providing a platform that encompasses data management, statistical analysis, and reporting functionalities, SAS plays a vital role in supporting evidence-based decision-making in developing and approving new drugs, treatments, and medical interventions within the healthcare and pharmaceutical industries.
Pros of using SAS:
- Regulatory Acceptance: SAS has a long-established history of acceptance by regulatory agencies like the FDA and EMA. Its use is widely acknowledged and validated for regulatory submissions, simplifying the approval process.
- Robustness and Stability: SAS is known for its stability, reliability, and robustness in handling large datasets and complex analyses. This stability is crucial in clinical trials, where accuracy and consistency are paramount.
- Comprehensive Toolset: SAS provides a comprehensive suite of tools specifically designed for clinical trials. These tools include procedures for sample size estimation, data cleaning, analysis, reporting, and CDISC compliance, streamlining the entire process within a single platform.
- Data Management Capabilities: SAS excels in data management tasks, enabling efficient handling, cleaning, transformation, and standardization of clinical trial data. It supports various data formats and facilitates data integration from diverse sources.
- Quality Control and Validation: SAS offers robust validation procedures, ensuring data quality and integrity. It helps identify and rectify errors or inconsistencies in the data, which is crucial for maintaining high-quality trial results.
- Reproducibility and Documentation: SAS allows for the creation of reproducible analyses and comprehensive documentation.
Cons of using SAS:
- Cost: SAS is a licensed software with associated costs, including licensing fees, maintenance, and additional modules. This expense can be a limiting factor for smaller organizations or research projects with budget constraints.
- Limited Flexibility: Compared to open-source tools, SAS might have limitations in terms of customization and flexibility. Users might face constraints when implementing newer methodologies or adapting to rapidly evolving statistical techniques.
- Compatibility and Integration: Integrating SAS with other systems or software might sometimes pose challenges due to proprietary formats and compatibility issues, especially when working in heterogeneous environments.
Using R Programming Language for Clinical Trials Statistical Programming:
R is a powerful and versatile clinical trial statistical programming tool that is becoming increasingly popular.
Pros of using R programming:
- Cost-effective: R is an open-source software, making it free to download and use. This is particularly advantageous for organizations or projects with budget constraints.
- Community Support: R boasts a vast and active user community. This community contributes to numerous packages, resources, forums, and tutorials, providing extensive support and a wide range of functionalities.
- Flexibility and Customization: R’s open-source nature allows for extensive customization and adaptation. Users can develop and share packages, implement cutting-edge methodologies, and easily incorporate new statistical techniques or algorithms.
- Cutting-edge Statistical Techniques: R is at the forefront of statistical research, often swiftly adopting and implementing the latest statistical methodologies, making it suitable for innovative and advanced analyses.
- Graphics and Visualization: R provides powerful graphical capabilities, allowing for the creation of high-quality visualizations and plots. This is advantageous for data exploration, presentation, and conveying complex findings in clinical trials.
Cons of using R programming
- Documentation and support: These might lack depth compared to commercial software like SAS, impacting timely assistance.
- Regulatory compliance: While gaining acceptance, R could face scrutiny from certain regulatory agencies favoring validated commercial tools.
- Data security Data security concerns could arise due to R’s open-source nature, requiring additional measures for safeguarding sensitive clinical trial data.
Will R Replace SAS in 2024 for Clinical Trials Statistical Programming?
Despite the steep popularity increase of R in clinical trial statistical programming, a complete replacement of SAS by 2024 seems unlikely. SAS’s entrenched position, established as an industry standard and compliant with regulatory requirements, presents a formidable barrier. Its specialized features crafted explicitly for clinical trials—from data management to reporting—reduce the need for extensive custom programming.
Moreover, the significant investments made in SAS expertise and infrastructure across numerous organizations make the transition to R a costly and disruptive prospect. As R continues to gain traction, SAS’s stronghold in clinical trials statistical programming remains steadfast.
What is the To-Go Option For Statistical Programming in 2024?
Choosing between SAS and R in clinical trials statistical programming isn’t a one-size-fits-all decision. Here are our suggestions for either using SAS or R programming language in specific cases:
SAS is a good choice for:
- Large, well-funded studies with strict regulatory requirements.
- Projects needing robust performance and minimal downtime.
- Research teams experienced with SAS and its functionalities.
- Collaboration with partners or agencies accustomed to SAS as the standard.
R programming language is the to-go option for:
- Smaller projects with budget constraints.
- Research prioritizing compelling and interactive data visualizations.
- Teams comfortable with open-source platforms and willing to invest in learning R.
- Projects requiring flexibility and customization beyond standard functionalities.
- Collaboration with researchers and institutions adopting R workflows.