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CDISC

CDISC, which stands for Clinical Data Interchange Standards Consortium, is a global, non-profit organization that develops standards to streamline clinical research and improve the quality, consistency, and interoperability of clinical data. These standards are widely used in the pharmaceutical, biotechnology, and medical device industries, as well as by regulatory agencies like the FDA and EMA.

Key Points About CDISC:

1. Purpose:

  • Facilitate the collection, organization, and analysis of clinical trial data.
  • Improve efficiency in the drug development process.
  • Support regulatory submissions with standardized, high-quality data.

2. Core Standards:

CDISC has developed several frameworks and models, each addressing specific aspects of the clinical data lifecycle:

  • SDTM (Study Data Tabulation Model): Defines how to format and submit collected clinical trial data for regulatory review.
  • ADaM (Analysis Data Model): Focuses on how to structure data to support statistical analyses.
  • CDASH (Clinical Data Acquisition Standards Harmonization): Guides the standardization of case report forms (CRFs) and data collection.
  • SEND (Standard for Exchange of Nonclinical Data): Addresses the submission of nonclinical (preclinical) study data.
  • ODM (Operational Data Model): Facilitates data interchange between systems, such as EDC (Electronic Data Capture) platforms.

3. Benefits:

  • Reduces the time and effort required to collect, analyze, and share data.
  • Ensures consistency and comparability across studies and organizations.
  • Simplifies the regulatory submission process and increases the likelihood of approval.
  • Promotes data sharing and reuse in research, enabling collaborative advancements.

4. Global Adoption:

CDISC standards are endorsed by many health authorities and have become a regulatory requirement in several regions for submission of clinical trial data.

By standardizing how clinical trial data is structured and exchanged, CDISC helps stakeholders focus on generating insights and advancing medical research rather than dealing with data inconsistencies or inefficiencies.