Conduct↦Data Management↦Data Entry↦Data Integrity
What is it? Why is it important?
Data Integrity stands for data accuracy. It is attained through the implementation of ALCOA principles, which ensure that study data is:
- Attributable: tracks who performs when (e.g. date and time) a given task / data point
- Legible: ensures that data-sets and/or documents are legible
- Contemporaneously: provides timestamps over when tasks or data points are performed, and when these are modified
- Original: provides information in its original non-edited form
- Accurate: provides data free of errors
Additional ALCOA principles include:
- Complete: generated data is retained in its original form. An audit trail documents any data modifications
- Consistent: provides consistent data irrespective of its source of access
- Enduring: stores data for long periods of time
- Available: ensures data and long-term stored data is retrievable whenever needed
What do I need to do?
As a SP-INV, Site-INV, Project Leader, Study-Staff, implement ALCOA principles and operational processes that safeguard Data Integrity. Data Integrity concerns should be addressed over the entire lifecycle of the data, from data collection until analysis, and as applicable destruction.
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(R3) – see in particular principle and guidelines
- Principle 9.3 Clinical trials should generate reliable results
- Principle 10: Roles and responsibilities in clinical trials should be clear and documented appropriately
- Art. 2.12.1 Investigator must ensure integrity of data
- 3.16.1 Data handling
- 4. Data governance – investigator and sponsor
ICH E8(R1) – see in particular guideline
- 5.6 Statistical analysis
- 5.7 Study Data