CDISC Data Conversions – SDTM and ADaM

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An established methodology for converting clinical data into a regulatory submission or data warehouse ready format

CDISC data standardization facilitates clinical data pooling, standard reporting, and data transfers with partners.

Additional important reasons for converting clinical study data into a CDISC format are :

  • To comply with the Food and Drug Administration (FDA) submission requirements
  • To prepare clinical data into a data warehouse ready format to maximize its usability

FDA Submissions

The FDA has embraced CDISC SDTM and ADaM as the standard data model for submitting tabulation data in an electronic format. FDA reviewers are requesting sponsors to use the SDTM model to vastly improve their review efficiency and accuracy. The analysis datasets can be prepared in the ADaM format to ensure traceability between the analysis domains and the SDTM domains.

The Business & Decision Life Sciences data conversion process uses established techniques for converting clinical study data to the CDISC format and the creation of metadata (define.xml) for regulatory submission.

The deliverables include:

  • SDTM annotated CRFs
  • SDTM datasets (v3.1.3)
  • ADaM analysis datasets (v1.0)
  • Pooled analysis datasets (ISS/ISE)
  • Define.xml (CRT-DDS) metadata and Define.pdf
  • CDISC controlled terminology
  • Data Stewards’ report
  • Data Handling report
  • FDA reviewers’ Guide
  • eCTD structure

Warehouse Preparation

Sponsors are realizing the added value of a global data standard across all systems and processes. Study data and metadata are typically supplied by third party providers and the data may currently exist in different, non-compatible formats.

To maximize the usability and value of existing data, Business & Decision Life Sciences helps sponsors to convert all existing data into a data warehouse-ready, CDISC standard structure.

Benefits

  • Maximizes the value of clinical study data

  • Avoids delays in FDA submissions

  • Reduces CDISC conversions cycle times and costs

  • Avoids investing programming and validation efforts by outsourcing the data conversion work

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