Southern California Clinical and Translational Science Institute
Translating Science into Solutions for Better Health

Monitoring and Auditing Bootcamp: 4. Sponsor Audits: Data Handling and Reports

​In this series, we will discuss what a sponsor does with data after a clinical trial finishes at a site, focusing on audits.

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Course Syllabus/Topics

  1. Auditing Personnel
    • Auditor should be independent from people directly involved in conduct of studies to ensure there is no bias
  2. Quality Control (QC) vs. Quality Assurance (QA)
    • Quality control includes activities which control the quality of products by finding defects
    • Quality assurance is focused on preventing defects
  3. Data Audits- Data Management Activities
    • The purpose of a data audit is to assure confidence in quality and completeness of integrity of data.
    • Data audits are conducted before a product is given approval by a regulatory agency to be commercially sold.
    • Data management activities include:
      • CRF management and tracking
      • Data entry
      • Data validation
      • QC procedures
      • Query management
      • Database locking
      • Transfer of records and files
      • Data handling reports.
    • Data verification ensures consistency before and after the transformation process.
    • An audit plan should address the extent and nature of sampling data.
    • Verification activities include ensuring consistency between data on case report forms (CRFs) and databases
      • May be good to focus on CRFs that have been queried 
  4. Report Audits- Traceability of Data
    • Clinical Study Report (CSR) audit assess whether a report is fair and accurate.
    • Adheres to ICH E3 Guidance on Structure and Content of Clinical Study Reports.
    • There should be documentation of justifications for deviations from the protocol or any other changes.
    • The report conclusions should be an accurate representation of the results.
    • The key to a successful data audit is traceability.
  5. CDISC Standards:
    • SDTM- Submission Data Tabulation Model
    • ADaM- Analysis Data Model

Recommended Reading and Resources

Study Data Standards for Industry