Development↦Data Handling↦Data Quality↦Requirement
What is it? Why is it important?
In order to ensure data quality and transparency a description should be given regarding how data is:
- Generated (e.g. blood analysis, questionnaires, medical examinations)
- Structured (e.g. database set-up, data files, export files, applied server)
- Put into context (e.g. medical intervention)
- Accessed (e.g. users and relative rights)
- Processed (e.g. calculations, classifications, statistical analysis)
Providing information on data, allows data to be understood and interpreted by other researchers or users. Thus, knowledge regarding the background and creation of data grants confidence regarding study results and its conclusions.
Overall DM is described in a DMP, with additional documents providing further details.
Quality data 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.
Here some important aspects that need to be documented:
- The context or circumstances during which data is collected (e.g. an 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 as a result of data analysis from raw data (e.g. automated processing of raw data such as calculations or classifications)
- 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?
Generate a DMP by describing the data of your study including how it will be managed during study set-up, implementation, and completion.
Aspects to describe are:
- How raw data is handled including any automated processing thereof (study SOPs)
- How study data is handled during and after study completion (to be documented in the DMP)
- The metadata of your study data
- The statistical approach of the study (to be documented in the SAP)
- Implemented data quality aspects to ensure high quality data (based on a risk-based approach)
In addition, document main data management aspects in the applicable section of your study protocol.
For more information refer to Statistics and Quality and Risk in this Trial Guide.
Any delegated data management tasks must be documented in the delegation log of your study. In addition, ensure that staff is appropriately trained to perform these tasks so that data management processes are correctly carried out (e.g. provision of DM SOPs, inclusion of quality checks).
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
Swissethics – see in particular
- protocol templates – Templates Swiss federal legal act
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