What is it? Why is it important?
Quality Data (QD) is reproducible data. In other words, data has been collected and managed in such a way that other researchers obtain similar results when repeating the study. QD means that the data is described on how it is:
- Generated (e.g. through blood analysis, questionnaires, medical examinations)
- Structured (e.g. through the set-up of a study database and data files (CRF), use of standard export files and procedures, applied server)
- Put into context (e.g. data was generated through some medical intervention)
- Accessed (e.g. data is access protected based on user access/login rights)
- Processed (e.g. data is explained based on its metadata, variable specifications, coding/classifications, automated processing, statistical analysis)
Providing information on data background and creation, allows data to be understood and interpreted by other researchers. This grants confidence regarding study results and its conclusions.
Example of important information needed to provide confidence in the quality of study data:
- The context or circumstances during data collection (e.g. observational- or interventional study)
- Methods used for data collection (e.g. blood tests, questionnaires, fitness tests)
- The structure and organisation of data files (e.g. selection of an applicable CDMS, use of an electronic eCRF, data stored on an external server, use of back-up files)
- Data validation and quality (e.g. based on a risk-based approach with applicable SOPs, DMP, SAP, ongoing quality checks such as data monitoring)
- Data manipulation based on the use and analysis of raw data (e.g. automated processing)
- Data confidentiality, database access and use conditions (e.g. restricted access to study data, use of personal password and login, identity protection by using codes rather than participant identifiers such as name, date of birth, address)
What do I need to do?
Aspects to describe are:
- The handling of raw data including any automated processing thereof (e.g. in a SOPs)
- The metadata of the study data
- The statistical approach of the study (e.g. to be documented in the SAP)
- The implementation of a risk-based Quality Management System that ensures the quality and integrity of your study data
For further details, include in the DMP applicable references to other quality relevant documents (e.g. SOP, WI, Quality Assurance and Quality Control aspects, risk-based QMS). In addition, inlcude main data management aspects in the study protocol.
Where can I get help?
Your local CTU↧ can support you with experienced staff regarding this topic
Basel, Departement Klinische Forschung, CTU, dkf.unibas.ch
Lugano, Clinical Trials Unit, CTU-EOC, www.ctueoc.ch
Bern, Clinical Trials Unit, CTU, www.ctu.unibe.ch
Geneva, Clinical Research Center, CRC, crc.hug.ch
Lausanne, Clinical Research Center, CRC, www.chuv.ch
St. Gallen, Clinical Trials Unit, CTU, www.kssg.ch
Zürich, Clinical Trials Center, CTC, www.usz.ch
ICH GCP E6(R2) – see in particular guidelines
- 5.1 for QA and QC5.5. Trial Management, data handling, and record-keeping
ISO 9001:2015 – Quality Management Systems – Requirements