SCDM Data Reconciliation and Validation 1 — Questions and Answers
Question 1: What is the primary purpose of data reconciliation in clinical data management?
- To ensure consistency and completeness of data across multiple sources (Correct answer)
- To generate statistical reports for regulatory submission
- To train data entry personnel on EDC systems
- To archive completed case report forms
Correct answer: To ensure consistency and completeness of data across multiple sources
Data reconciliation ensures that data from different sources (e.g., EDC, lab systems, IRT) are consistent, complete, and in agreement before database lock.
Question 2: During external data reconciliation, a discrepancy is found between the EDC system and the central lab data. What is the appropriate first step?
- Immediately correct the EDC record to match the lab data
- Delete the discrepant record from both systems
- Investigate the source of the discrepancy using original source documents (Correct answer)
- Report the discrepancy to the FDA without further review
Correct answer: Investigate the source of the discrepancy using original source documents
Investigating the discrepancy against source documents is the correct first step to determine which record is accurate before making any changes.
Question 3: Which type of validation check verifies that a date entered in a CRF falls within the acceptable range of the study period?
- Cross-form validation
- Range check (Correct answer)
- Consistency check
- Completeness check
Correct answer: Range check
Range checks verify that a data value falls within predefined acceptable minimum and maximum boundaries, such as valid dates within a study timeline.
Question 4: What does a 'discrepancy management' process in clinical data management primarily involve?
- Eliminating all data queries before study start
- Identifying, documenting, resolving, and closing data queries (Correct answer)
- Randomizing subjects to treatment arms
- Archiving all CRFs after database lock
Correct answer: Identifying, documenting, resolving, and closing data queries
Discrepancy management is the systematic process of identifying data issues via queries, tracking them, obtaining site responses, and closing them once resolved.
Question 5: In clinical data management, a 'hard edit' (or hard stop) in an EDC system means:
- The system allows data entry to proceed after acknowledging a warning
- The system prevents data entry from being saved until the error is corrected (Correct answer)
- The system automatically corrects the data without user intervention
- The system sends a query to the site monitor for review
Correct answer: The system prevents data entry from being saved until the error is corrected
Hard edits prevent the user from saving or progressing past a data entry point until the entered value meets the validation criteria.
Question 6: Lab data reconciliation typically requires comparing EDC-entered lab values against which reference?
- Investigator brochure
- Protocol-specified statistical analysis plan
- Listings or data transfers from the central or local laboratory (Correct answer)
- IRB approval documentation
Correct answer: Listings or data transfers from the central or local laboratory
Lab data reconciliation compares the lab values recorded in the EDC against the data provided by the central or local laboratory in data transfer files or listings.
Question 7: Which metric is commonly used to measure the quality of data during validation activities?
- Protocol deviation rate
- Error rate (number of errors per 100 fields entered) (Correct answer)
- Patient enrollment velocity
- Investigator site activation timeline
Correct answer: Error rate (number of errors per 100 fields entered)
Error rate, typically expressed as errors per 100 or 1,000 fields, is a standard metric for assessing data quality and entry accuracy.
SCDM Data Reconciliation and Validation 1
What is the primary purpose of data reconciliation in clinical data management?