Get to Know SAS® Life Science Analytics Framework
Efficiently transform, analyze and report on clinical trial data. Develop new therapies faster by giving everyone access to powerful pharma analytics.
Empower all stakeholders with approachable analytics.
Drive global collaboration among internal team members, consultants, contractors and development partners by putting easy-to-use pharma analytics in the hands of knowledge workers in areas such as preclinical operations, clinical operations and medical affairs.
Increase flexibility with seamless, open source integration.
Your hiring managers need the ability to hire the best programming talent available. SAS Life Science Analytics Framework fully embraces open source, enabling you to expand your hiring pool by giving users the flexibility to program in SAS, R or Python. Programmers are able to consume R data frames inside the solution.
Streamline and automate clinical research processes to gain instant insight.
Workflow capabilities aid project management oversight and support process enablement to lower costs while increasing the speed and efficiency of clinical research. The framework supports multiple analyses with different team members, access rights and context-specific privileges. You can assign tasks and track progress for each analysis activity and deliverable for a single study or your entire portfolio. Easily deploy workflows on a per-deliverable basis, whether it be a table, listing or figure. And automate clinical process activities using process orchestration capabilities, such as scheduled job initiation and completion notification.
Build confidence and trust with SAS' proven experience.
SAS is widely accepted as the gold standard for providing statistical capabilities to determine the safety and efficacy of medicines in clinical research. The model-driven approach for CDISC standards governance and enhanced study metadata management drive efficiency from study setup to submission.
Expand information management.
A fully integrated environment spans from operational data systems (such as eCRF), electronic health records, sensors and wearables, omics data, biomarker data, etc., through standardization, analysis and reporting, and post-approval meta-analysis. End-to-end management of clinical data means less time spent on operational data management activities and more time spent on exploring, monitoring data quality, and executing advanced analytics and statistics.
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