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Demand for greater patient-centricity, the shift toward precision medicine and global disruptions have contributed to the need for modernization and decentralization of clinical trials. To support this modernization, we need robust analytics solutions for a clinical trials revolution.

That’s where SAS comes in.

Learn how AI from SAS can transform the way you do clinical trials.

Getting a new medicine to market is a huge endeavour, often involving dozens of stakeholders from pharmaceuticals companies to clinical research organisations, healthcare providers, and regulators. Clinical trials are one of the most complex parts of the process, and that complexity is increasing year on year.

Explore how you can drive speed to market with more efficient clinical trials, using the SAS Life Science Analytics Framework – a modern platform, built on cloud-native technology and open API integration, with full support for open-source statistical programming languages and data science frameworks.

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5 key technical areas for clinical trial management

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The SAS Life Science Analytics Framework delivers everything your organization needs to cover the five main technical areas that are fundamental for clinical trials management:

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Why SAS?

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SAS has been working with life sciences companies to develop and refine solutions for clinical trials management for more than 20 years. The Life Science Analytics Framework represents the sum of everything we’ve learned while working with pharmaceuticals companies, clinical research organisations and healthcare providers during that period.

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Unlocking key business benefits

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With the SAS Life Science Analytics Framework, your business can:

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  • Reduce time to market and lower costs by accelerating clinical trials management
  • Automate foundational capabilities to support traditional and emerging clinical trial types
  • Improve efficiency and reduce errors in data aggregation and preparation
  • Ensure rigorous statistical analysis in an environment built for regulatory reporting
  • Extract maximum value from new and existing analytics applications and investments
  • Broaden access to programming talent from the SAS, R and Python communities
  • Expand access to clinical data and streamline collaboration across the ecosystem
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