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

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

Study Data Standards for Industry

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