SAS Clinical Data Integration organizes, standardizes and manages clinical research data and metadata.
It provides the foundation you need to define analysis data sets and support strategic analyses such as cross-study and advanced safety analysis. With SAS, you can gain both speed and efficiency by automating repeatable clinical trial data integration tasks.
Increase operational efficiency while lowering costs.
- Automate repeatable tasks to free up resources for higher value-added tasks.
- Increase your capacity to handle additional trials, as well as more complex global trials.
- Write and validate less code, and potentially reuse code for future trials.
- Scale clinical studies without adding expensive, hard-to-find headcount.
- Manage and reuse information stored in a common repository to reduce both development and maintenance time.
- Support adaptive trials through rapid access to clinical data.
Drive top-line growth.
- Leverage existing clinical data to make new marketing claims or discoveries.
- Speed data preparation for medical publications.
- Automate the migration of acquired data assets through data standards.
- Support and automate data aggregation and standardization for ongoing clinical trials.
Ensure the proper use of standards.
- Validate both the structure and content of data for conformance to CDISC SDTM or ADaM.
- Standardize data to CDISC SDTM, SEND or ADaM using prebuilt data models and processes.
- Visually convert legacy data to standard data.
Deliver consistent, trusted and verifiable clinical information.
- Aggregate information from virtually any hardware platform or operating system.
- Address potential issues before they affect your study by automating data quality and data transformation routines.
- Build and document work with a user-friendly GUI interface.
- Reduce the need to write unique code for each study.
- Get new team members up to speed quickly on work done by others.
- Integrates clinical, operational and safety data from multiple sources.
- Prepares uniform, consistent data for analysis.
- Enhances data quality to ensure trustworthy analytical conclusions.
- Supports data standards and performs adherence checks.