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Which metric will identify edit checks that may not be working properly?
The best metric to identify malfunctioning or ineffective edit checks is the count by edit check of the number of times the check fired. This allows data managers to assess whether specific edit checks are performing as intended.
According to the GCDMP, Chapter: Data Validation and Cleaning, edit checks are programmed logic conditions that identify data inconsistencies or potential errors during data entry. A properly functioning edit check should trigger only when data falls outside acceptable or logical limits. If an edit check fires too frequently or not at all, it may indicate a logic error in the check's programming or configuration.
By analyzing counts by individual edit checks, data managers can:
Identify checks that never trigger (potentially inactive or incorrectly written),
Detect overactive checks (poorly designed parameters causing excessive false positives), and
Optimize system performance and review efficiency.
This metric supports continuous improvement in data validation logic and contributes to cleaner, higher-quality clinical databases.
Reference (CCDM-Verified Sources):
SCDM Good Clinical Data Management Practices (GCDMP), Chapter: Data Validation and Cleaning, Section 6.2 -- Edit Check Design and Performance Metrics
FDA Guidance: Computerized Systems Used in Clinical Investigations -- Section on Validation of Electronic Data Systems