SAS for Real World Evidence Features List

Data management

  • Ability to collect internal or external data from point-of-care systems, electronic health records, insurance claims, patient-reported outcomes, trusted third-party data providers and others.
  • Built-in capabilities for cleansing, standardizing, loading and integrating real-world data prior to using it.
  • Intuitive interface for profiling, integrating and moving data stored in Hadoop without having to code.
  • Process automation maps data to a common data model and refreshes cohorts and outputs as new data arrives.

Cohort discovery

  • Interactive drag-and-drop cohort creation for identifying research cohorts without coding.
  • Ability to run complex queries that go beyond simple subsetting to selecting criteria with multiple temporal relationships and Boolean logic.
  • Progressive patient counts and graphics for showing the effects of each inclusion/exclusion criterion on the patient population to determine study feasibility.
  • Reusable, editable cohort queries for improving productivity and efficiency.

Advanced analytics

  • Prebuilt analytical models, including predictive models for health costs, utilization and outcomes.
  • Analytics library of methodologies includes simple descriptive statistics, predictive analytics and machine learning methods.
  • Ability to use third-party analytics and visualization tools on defined cohorts.
  • Intuitive interface for including SAS or R programs and driving parameters.

Data visualization & exploration

  • Explore, visualize and report on real-world data sources to generate insights to support decisions on treatment regimens, gaps in care, reimbursement, formulary access or support clinical development decisions.
  • Predefined cohort characterization.
  • Ad hoc exploration capabilities.
  • Advanced visual analytics for understanding therapeutic areas, medical products or devices, and longitudinal effects of therapies on patients.

Speed & agility

  • Point-and-click interface for navigating and exploring massive data sources with little or no lag time.
  • High-performance analytics run calculations on millions of rows of data in seconds, rather than minutes, hours or days.