How SAS Supports Real World Evidence
SAS helps you effectively leverage real world evidence at every stage of the product life cycle – from finding new areas for discovery, to developing drugs and therapies more efficiently, to better understanding product and drug safety, effectiveness, adherence, and economic and societal value. Stay competitive and get better, safer therapies to market faster. Navigate the challenges of managing and analyzing vast amounts of clinical data from disparate sources. Foster collaboration. And respond quickly to regulatory inquiries.
Expanded access to real world data
- Directly interact with real world data from multiple sources and stakeholders.
- Enable comparisons across populations using appropriate data models enriched with industry ontology.
- Reduce the time it takes to build patient cohorts and speed time to insight.
Flexible cohort development
- Quickly determine clinical study feasibility based on the number of patients meeting criteria.
- Interactively extract patient populations using an intuitive interface, which reduces the required time and resources.
- Identify patient populations without coding. Complex queries can go beyond simple subsetting to selecting criteria with multiple temporal relationships and Boolean logic.
Complete analytical toolkit
- Access an analytics library of methodologies that includes simple descriptive statistics, predictive analytics and machine learning methods.
- Compare outcome variables of interest across or within patient cohorts.
- Use out-of-the-box SAS models, or create your own models using SAS or open source.
- Robust cohort generator.
- Interactive visualizations.
- Powerful descriptive and predictive models, machine learning and AI.
- Event stream processing and IoT analytics at the edge.
How did a top-five global pharmaceutical company speed time to insight from six weeks to hours?
SAS helped the pharmaceutical company:
- Reduce the time it took to receive a report on areas of interest for future clinical R&D directions.
- Adopt more robust capabilities for utilizing real world data to support the product life cycle.
- Gain the ability to scale over time as analytic requirements increase.