SAS to R Migration: Time for Innovative Change in Clinical Research
Articles, Clinical Research, R Programming

SAS to R Migration: Time for Innovative Change in Clinical Research

Author Pavan Vipparthi
Published October 4, 2023

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With clinical studies growing in complexity, the volume of data generated has grown exponentially. The huge swathes of data and the intricacies in handling and managing it is pushing companies to root for SAS to R migration in clinical research.

Ever since the rise of open-source tools, the tide is slowly shifting to R for the numerous advantages it has over traditional proprietary software. Many top pharmaceutical companies have already run pilot programs to submit R-based regulatory submissions.

But is it a sensible choice for the life sciences industry to consider SAS to R migration? Let’s delve deeper.

What is the Role of SAS in Clinical Research?

SAS is a crucial programming solution in any clinical trial to efficiently manage the vast amounts of data and make them submission ready. After the cleaning of the study data, a trial report should be created and submitted to regulatory bodies for approval of the medical intervention. To analyze the data and create clinical trial reports, life sciences companies have traditionally used SAS.

All About SAS to R Migration in Clinical Research

What is SAS?

SAS is a programming language that is commonly used for statistical analysis and data visualization. The software is capable of reading the data from spreadsheets and databases, while generating results in RTF, HTML and PDF formats.

SAS allows users to maintain and produce tables, listings, figures, and graphs besides the clinical trial data management. Moreover, majority of the regulatory authorities across the world have SAS as the standard for data analysis and clinical programming. In general terms, SAS is a very flexible platform that enables its users to manipulate, analyze, process and create reports on data.

SAS has a wide range of applications across industries, as listed below:

  • Life Sciences
  • Financial Management
  • Corporate Intelligence
  • Customer Relationship Management
  • IT Management

How is SAS used in Clinical Research?

Most of the life sciences organizations utilize SAS for clinical trial data management for the below purposes:

  • Analyzing data
  • Protocol designing
  • Recruiting patients/investigators
  • Clinical trial management
  • Regulatory compliance
  • Health data management
  • Regulatory submissions

The Emergence of R

In recent years, there has been a surge in the use of R in various industries, including healthcare and life sciences. R is an open-source programming language and is made freely available to the public. This approach fosters innovation, encourages community contributions, and reduces the cost of software acquisition and maintenance.

What is R Programming?

Wikipedia defines R as “a programming language for statistical computing and graphics supported by the R Core Team and R Foundation for Statistical Computing.”

Considered the counterpart of SAS, the open-source programming language resembles C++ and contains an extensive catalogue of graphical and statistical methods such as machine learning algorithm, time regression, linear regression, and statistical inference.

The Need for SAS to R Migration

While SAS has been the industry-standard software for statistical programming and data analysis, the emergence of R as a potential alternative has gained traction in the clinical research community. R offers a wide range of packages and libraries specifically designed for statistical analysis and data visualization, making it an attractive option for clinical researchers. The SAS to R migration brings several benefits, including cost savings, collaboration opportunities, and enhanced functionality.

What are the Differences between SAS and R Programming?

SAS and R programming are different in many aspects. Understanding the key differences might help life sciences firms make their decision on SAS to R migration.  Here are a few key differences:

  • Ease of Learning
  • Graphical Capability
  • Statistical Ability
  • Cost
  • File Sharing
  • Customer Support

Ease of Learning: The user-friendly interface of SAS makes it relatively easier, for even new users, to utilize it. Whereas R comes with a higher learning curve that might seem overwhelming and complicated to new users.

Graphical Capability: R has advanced graphical capabilities because of various inbuilt packages like RGIS, ggplot, and latis. SAS has a very limited graphical support.

Statistical Ability: SAS comes with a package that has all the required types of statistical analysis and techniques. Whereas R enables its users to submit their own packages.

Cost: R programming is an open-source tool, meaning it is free to use. On the other hand, SAS is an expensive commercial platform.

Data Management: R facilitates easier and simpler file sharing for seamless collaboration with anyone. SAS generated files can only be used on the software, making it difficult to share files and collaborate.

Interoperability: One of the major differences between R and SAS is interoperability. R users can take advantage of the interoperability tools with the help the R packages. SAS does not support interoperability.

Custom Application Development: While both R and SAS have custom application development options, R is favored for its ‘Shiny’ framework and for being opensource. SAS offers a comprehensive suite of tools that are suitable for enterprise-level applications.

Data Science: Both R and SAS overlap in many areas of data science. R has a lot of opensource libraries for data visualization, statistical analysis, machine learning libraries, etc. while SAS does not have any.

Customer Support: SAS has a dedicated customer care to address any issues. Being an opensource software, R does not have customer care, but many popular online technology communities and forums support it by posting solutions to various R-related problems.

The Benefits of R in Clinical Research

The adoption of R in clinical research brings forth a multitude of advantages over traditional software like SAS. Some of the key benefits of R that compel clinical researchers to cheer for SAS to R migration are:

  • Interactive Visualization and Data Analysis
  • Automation and Data Manipulation
  • Cost Savings and Collaboration
  • Custom Application Development
  • Data Science Applications

Let’s explore these benefits of R programming in detail.

Interactive Visualization and Data Analysis

Traditional tabulation methods often fall short in uncovering intricate patterns and relationships within complex clinical datasets. R’s interactive visualization capabilities, coupled with its robust statistical analysis functions, empower researchers to explore data more effectively. Through tools like R Shiny, web-based applications can be developed to create dynamic and interactive visualizations, enabling end-users to gain deeper insights into the data.

Automation and Data Manipulation

In the era of big data, automation and data manipulation are crucial for efficient analysis. R provides powerful capabilities for automating repetitive tasks, allowing researchers to handle large datasets with ease. Its extensive library of functions and packages enables complex data manipulations, transformations, and statistical computations, facilitating the extraction of valuable insights from clinical data.

Cost Savings and Collaboration

One of the primary advantages of R is its cost-effectiveness. Unlike SAS, which requires expensive licenses, R is an open-source tool that can be freely downloaded and used. This significantly reduces the financial burden on pharmaceutical companies and research organizations, allowing them to allocate resources to other critical areas. Furthermore, R’s open-source nature promotes collaboration and knowledge sharing within the research community. Developers and statisticians can contribute to the improvement of existing packages and the creation of new ones, creating a rich ecosystem of resources that benefits the entire industry.

Custom Application Development

Custom application development is among the top advantages of R. Data scientists and analysts can create tailored tools with RShiny to improve their analyses, enhance data exploration and streamline workflows.

Data Science Applications

R is a favorite among the data scientists and analysts for its rich ecosystem of packages for data visualization, statistical analysis, and machine learning. Besides tackling different data challenges, R can help reduce costs, and create interactive applications, making it a powerful ally for data analysis and research.

Leveraging R for Clinical Programming

The SAS to R migration in clinical research holds immense potential for unlocking the power of open-source tools. As open-source technologies continue to evolve and mature, their adoption in the clinical research industry is expected to increase.

The advantages of R make it a compelling choice for clinical programmers and data analysts. By leveraging R’s extensive functionality and the user-friendly interface, clinical researchers can enhance their productivity, improve decision-making processes, and gain deeper insights.

In conclusion, the SAS to R migration in clinical research is driven by the need for cost-effective, collaborative, and powerful tools. By embracing R and other open-source frameworks, pharmaceutical companies, life sciences firms and research organizations can unlock new possibilities and drive innovation in clinical research.

Inductive Quotient Analytics (IQA) is a fast-growing ClinTech company with strong expertise in SAS to R migration. Our R programmers are extremely proficient with SAS to R migration and handled several global clinical studies effectively. Visit www.inductivequotient.com or talk to us at hello@inductivequotient.com to know more. 

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