Development↦Data Management↦Data Quality↦Requirement
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)
- 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 calculations, 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.
More
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, storage location, 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 processing of raw data (e.g. automated calculations)
- Data confidentiality, database access and use conditions (e.g. restricted access to study data, use of personal password and login, identity protection by usin participant codes)
What do I need to do?
As a SP-INV, as applicable generate a DMP that describes your study data and how it will be managed during study set-up, implementation, and completion.
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. statistical analysis plan (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
References
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