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Visualizing and Validating Metadata Traceability within the CDISC Standards.
Hume, Sam; Sarnikar, Surendra; Becnel, Lauren; Bennett, Dorine.
Affiliation
  • Hume S; Dakota State University, Madison, SD.
  • Sarnikar S; California State University, East Bay, Hayward, CA.
  • Becnel L; Clinical Data Interchange Standards Consortium, Austin, TX.
  • Bennett D; Dakota State University, Madison, SD.
AMIA Jt Summits Transl Sci Proc ; 2017: 158-165, 2017.
Article in En | MEDLINE | ID: mdl-28815125
The Food & Drug Administration has begun requiring that electronic submissions of regulated clinical studies utilize the Clinical Data Information Standards Consortium data standards. Within regulated clinical research, traceability is a requirement and indicates that the analysis results can be traced back to the original source data. Current solutions for clinical research data traceability are limited in terms of querying, validation and visualization capabilities. This paper describes (1) the development of metadata models to support computable traceability and traceability visualizations that are compatible with industry data standards for the regulated clinical research domain, (2) adaptation of graph traversal algorithms to make them capable of identifying traceability gaps and validating traceability across the clinical research data lifecycle, and (3) development of a traceability query capability for retrieval and visualization of traceability information.

Full text: 1 Collection: 01-internacional Database: MEDLINE Type of study: Guideline Language: En Journal: AMIA Jt Summits Transl Sci Proc Year: 2017 Document type: Article Country of publication: United States

Full text: 1 Collection: 01-internacional Database: MEDLINE Type of study: Guideline Language: En Journal: AMIA Jt Summits Transl Sci Proc Year: 2017 Document type: Article Country of publication: United States