SAS® Life Science Analytics Framework
Drive speed to market with more efficient clinical trials.
Get to Know SAS® Life Science Analytics Framework
Only SAS delivers a single, open, cloud-native statistical computing environment for clinical research with embedded analytic tools, support for data standards and optional integrated analytic applications. Our analytic foundation for clinical research helps you modernize and deliver new therapies to market faster to improve health.
Manage and analyze information in a collaborative platform, streamline processes and more efficiently deliver trial results to regulatory authorities.
Faster time to market & cost containment
Provides cloud-native, single solution for clinical analysis and submission with everything you need for validation, regulatory compliance, versioning, audit trails and documentation support. Allows for automation through workflows, support for current and future integrations, and implements and properly manages data standards and controlled terminology.
Broader access to programming talent
Gives users the flexibility to program in SAS, R and Python.
Solid framework for traditional & emerging trial designs
Supports new, decentralized and hybrid trial models, including automation, decision support and activity tracking.
Improved efficiency & reduced errors in data aggregation & preparation
Includes central hub for all incoming data, automated data quality analysis, better data management and analytical data preparation. Provides impact analysis for every job and full mapping of data source, data manipulations and a final destination for data. Includes advanced search functionality to improve discoverability and productivity.
Rigorous statistical analysis & regulatory controls
Combines regulatory compliance and control features with seamless development and execution of SAS programs to reduce risk.
Maximum value from existing or new analytics applications
Integrates analytic applications – including those you have already developed or from SAS – for a variety of business needs. Ability to pull data directly from existing EDC systems for quicker access to data and potential cost savings.
Expanded access to & collaboration with clinical data
Includes a centralized shared workspace, clinical data repository and analytics platform providing access for all authorized global team members. Everyone works within the single platform.
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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