Regulatory Science Symposium: Monitoring and Auditing Session 4: Sponsor Audits: Data Handling and Reports (2016)

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

Regulatory & Quality Sciences
Study & Site Management
Research & Study Conduct
Nancy Pire-Smerkanich, DRSc, MS

Assistant Professor, USC Mann Dept. of Regulatory and Quality Sciences; Associate Director, Regulatory Knowledge and Support

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

Acknowledgement

Accompanying text created by Amelia Spinrad | Regulatory Knowledge Support Specialist | spinrad@usc.edu

NIH Funding Acknowledgment: Important - All publications resulting from the utilization of SC CTSI resources are required to credit the SC CTSI grant by including the NIH funding acknowledgment and must comply with the NIH Public Access Policy.