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Updated: Feb 24, 2026

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Visualizing and Validating Metadata Traceability within the CDISC Standards.

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Summary

New methods improve traceability in regulated clinical research, ensuring analysis results link back to source data. This enhances data integrity and compliance with FDA requirements for electronic submissions using CDISC standards.

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Area of Science:

  • Clinical research informatics
  • Data management
  • Regulatory science

Background:

  • The Food & Drug Administration (FDA) mandates the use of Clinical Data Information Standards Consortium (CDISC) data standards for electronic submissions of regulated clinical studies.
  • Traceability, the ability to link analysis results back to original source data, is a critical requirement in regulated clinical research.
  • Existing solutions for clinical research data traceability offer limited capabilities in querying, validation, and visualization.

Purpose of the Study:

  • To develop metadata models for computable traceability and visualizations compatible with CDISC standards in regulated clinical research.
  • To adapt graph traversal algorithms for identifying traceability gaps and validating data across the clinical research lifecycle.
  • To create a query capability for retrieving and visualizing traceability information.

Main Methods:

  • Development of domain-specific metadata models to support computable traceability.
  • Adaptation of graph traversal algorithms to analyze and validate data lineage.
  • Implementation of a query system for accessing and visualizing traceability data.

Main Results:

  • Successfully developed metadata models enabling computable traceability and visualizations aligned with CDISC standards.
  • Adapted graph traversal algorithms to effectively identify traceability gaps and validate data across the clinical research data lifecycle.
  • Created a functional query capability for retrieving and visualizing complex traceability information.

Conclusions:

  • The developed methods and tools enhance traceability in regulated clinical research, addressing limitations of current solutions.
  • The approach supports data integrity, regulatory compliance, and improved data analysis through enhanced querying and visualization.
  • This work provides a foundation for more robust and efficient management of clinical research data traceability.