In this free webinar, learn how to validate R for clinical use while maintaining appropriate controls. The featured speakers will discuss how R and SAS can be used together without undermining auditability and traceability. Attendees will gain insight into practical governance steps to manage package updates, versioning and reproducibility. The speakers will also discuss how validation practices support compliant clinical analytics outputs.
TORONTO, Feb. 5, 2026 /PRNewswire/ — Life sciences companies face increasing pressure to expand clinical analytics beyond traditional SAS workflows to support more flexible analyses, richer visualizations and faster access to evolving statistical methods. Using open-source languages such as R alongside the commercial SAS language provides access to distinct capabilities and methodologies enabled by the rapid evolution of open-source technology. This webinar will explore how to adopt R alongside SAS while maintaining the validation controls required for compliant clinical use.
With frequent changes and the ability for anyone to contribute, open-source languages rapidly evolve. However, these also introduce significant challenges for validation, a critical component of any technology used for the development and analysis of clinical data.
In this webinar, the featured speakers will outline a practical approach to validating R as an open-source technology. With this approach, attendees will be able to balance rapid adoption of evolving open-source technologies with appropriate controls to ensure that analytic outputs are valid and compliant for clinical use.
Register for this webinar to learn how clinical analytics can be validated when R and SAS are used together for compliant clinical data analysis.
Join experts from d-wise, an Instem Company, Phil Loucks, Commercial Director; and Jonathon Hollahan, Deputy General Manager, for the live webinar on Thursday, February 26, 2026, at 2pm EST (11am PST).
For more information, or to register for this event, visit How to Safely Harness R and SAS for Cutting-Edge and Validated Clinical Analytics.
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