Completion↦Statistic Methodology↦Data Quality Check↦Procedures
Was betrifft es? Warum ist das wichtig?
A Data Quality Check (DQC), also called statistical data validation, is a process used by statisticians to ensure that data used for the statistical analysis is of high quality.
The DQC is analogous to Central Data Monitoring (CDM), and is systematically performed by a statistician prior to data analysis.
Depending on the scope of data to be validated, DQC may include checks for:
- Completeness of data (e.g. extent of missing data)
- Consistency of dates (e.g. baseline visits are conducted after informed consents have been signed and prior to 3-month study visits)
- Identification of duplicates
- Range of values (e.g. plausibility checks regarding blood values and age range, identification of unexpected outliers)
- Consistency between variables (e.g. a pregnant men or child).
Was muss ich befolgen?
As a SP-INV, ensure that potential incorrect data identified during DQCs are properly investigated and corrected (e.g. outliers such as a participant born in 1862, blood pressure of 10/40)
Wo kann ich Hilfe anfordern?
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
SCTO Research Tools & Resources
- Training Tutorial for Researchers and Statisticians
- Statistic and Methodology - tools and support website
- SCTO Statistic Platform
References
ICH GCP E6(R3) – see in particular
- Principles of ICH GCP – Nr. 9 Clinical trials should generate reliable results
- 3.11.3 Quality control
- 3.16.1 b Sponsor should apply data quality controls
- 3.16.2 c Sponsor should ensure traceability of data transformation and derivations
- 4.2.3 Review of data and metadata
- 4.2.4 Data corrections
- 4.2.6 Finalisation of data sets prior to analysis
ICH Topic E9 – see in particular
- 5.2.1 Full analysis set
- 5.2.2 Per protocol set
- 5.3 Missing values and outliers
- 5.4 Data transformation