This metric directly isolates the performance of each individual edit check. A count of zero for a check that is expected to fire indicates it may be programmed incorrectly or the logic is flawed (a false negative). Conversely, an unusually high count for a specific check compared to others suggests it might be poorly designed, overly sensitive, or generating excessive false positives. By monitoring the raw firing count per check, data managers can efficiently identify and investigate specific validation rules that are not functioning as intended, ensuring data quality and system integrity.

B. Count by site of the number of times any edit check fired: This metric assesses a clinical site's data entry quality or adherence to protocol, not the functionality of a specific edit check.

C. Average number of edit check identified discrepancies per form: This metric evaluates the complexity or data quality issues related to a specific form (eCRF), not the performance of an individual edit check.

D. Average number of times each edit check has fired: While similar to option A, a raw count is a more direct and immediate indicator of a non-firing (count=0) or over-firing check than an average, which can mask such issues.

1. Society for Clinical Data Management (SCDM). (2023). Good Clinical Data Management Practices (GCDMP).

Section 5.3, "Data Validation," emphasizes the need to specify, program, and test edit checks. Monitoring the performance of these checks once the system is live is a critical component of ongoing data validation. A metric tracking the firing count of each check is a standard method for this monitoring, as it directly evaluates if the programmed logic is working as specified and tested.

2. Prokscha, S. (2011). Practical Guide to Clinical Data Management (3rd ed.). CRC Press.

Chapter 10, "Data Cleaning," Section "Query Metrics," discusses the importance of generating metrics on query rates. The text explains that reviewing queries "by check ID" (the unique identifier for an edit check) is essential for identifying problematic checks that may be firing too often due to flawed logic, thus allowing for their revision.

3. Tudur Smith, C., et al. (2012). The role of data monitoring committees in clinical trials. Clinical Trials, 9(5), 652-659. https://doi.org/10.1177/1740774512457215

This article discusses data quality monitoring. While focused on DMCs, the principles apply to CDM. It highlights the need to review "the rates of... queries" (p. 655). Analyzing the rate or count for an individual edit check is a fundamental method to detect systemic errors, which could stem from a malfunctioning check, thereby ensuring the integrity of the trial data.