Development↦Data Management↦Data Quality↦Quality Assurance
What is it? Why is it important?
Data Quality Assurance (DataQA) includes a set of activities, processes, and implemented measures to ensure that study data is of high-quality. High-quality data grants confidence that obtained study results are reliable and credible
In order to comply with DataQA goals, the SP-INV must implement a risk-based strategy that guarantees that study data is correctly collected, stored, shared, and analysed. As a consequence, applicable data management processes and guidelines should be in place.
What do I need to do?
As a SP-INV:
- Identify potential risks that might negatively affect the quality of your study data (e.g. documentation errors, data input errors, incompatible software updates, untrained staff)
- Evaluate and prioritise risks based on likelihood of occurrence and impact on data quality
- Based on your risk assessment (RAF) define applicable risk control-measures
- Train staff on data mangement procedures and the implementation of risk-control-measures
- Define a surveillance plan in order to check the efficacy of risk-control measures during study conduct, including the emergence of new risks (e.g. process verification and training)
- Document your risk assessment including preventive measures in the study Risk Assessment form
More
Example of risk-control-measures
- Personalise CDMA set-up in order to support ongoing data quality (e.g. variable specification)
- Perform system validation and functionality testing upon CDMS updates
- Implement planned monitoring visits during study conduct in order to check if data entered in the study database corresponds to the source data
- Ensure ongoing system maintenance and security, including data backup and recovery due to system failure
- Define data export/import processes that control for unwanted data alterations, data loss, and a risk to data confidentiality
Where can I get help?
Your local Research Support Centre↧ can assist you with experienced staff regarding this topic
Basel, Departement Klinische Forschung (DKF), dkf.unibas.ch
Lugano, Clinical Trials Unit (CTU-EOC), ctueoc.ch
Bern, Department of Clinical Research (DCR), dcr.unibe.ch
Geneva, Clinical Research Center (CRC), crc.hug.ch
Lausanne, Clinical Research Center (CRC), chuv.ch
St. Gallen, Clinical Trials Unit (CTU), h-och.ch
Zürich, Clinical Trials Center (CTC), usz.ch
References
ICH GCP E6(R2) – see in particular guidelines
- 5.1 for Quality assurance and control
- 5.5. Trial Management, data handling, and record-keeping
ISO 9001:2015 – Quality Management Systems – Requirements