In an EDC study, an example of an edit check that would be inefficient to run at data entry is a check:

An edit check for consistency across visits (an inter-visit check) is inefficient to run at the moment of data entry. Such checks require the Electronic Data Capture (EDC) system to query and retrieve data from other electronic case report forms (eCRFs), which may not have been entered yet. This process can be computationally intensive, causing noticeable delays for the user entering the data. To ensure a responsive and efficient user experience, complex validations like cross-visit consistency are typically programmed to run as "batch" or "offline" procedures after the form has been saved, rather than as instantaneous, real-time checks.

A. Against a valid list of values: This is a simple, field-level check (e.g., dropdown menu) that is highly efficient and commonly performed in real-time at data entry.

C. Against a valid numeric range: This is a fundamental, single-field validation (e.g., age must be >18) that is computationally simple and ideal for immediate feedback at data entry.

D. On the format of a date: This is a basic format validation (e.g., DD-MMM-YYYY) on a single data point, which is extremely fast and a standard real-time check at data entry.

1. Society for Clinical Data Management (SCDM). (2019). Good Clinical Data Management Practices (GCDMP). Section 7.4.2, "Edit Check Programming and Testing," discusses the timing and types of edit checks. It implicitly supports the concept that complex checks involving multiple forms or visits are often run post-entry (in batch) to avoid performance issues, whereas simpler checks (range, format, list of values) are run at the time of entry.

2. Prokscha, S. (2017). Practical Guide to Clinical Data Management (3rd ed.). CRC Press. In Chapter 11, "Data Review and Validation," the author distinguishes between checks performed at the time of data entry and more complex checks. It is explained that "checks that compare data across visits... are usually programmed to run in batch mode" to avoid system performance degradation during data entry (p. 164).

3. Krishnankutty, B., Bellary, S., Kumar, N. B. R., & Moodahadu, L. S. (2012). Data management in clinical research: An overview. Indian journal of pharmacology, 44(2), 168–172. This article describes the data validation process, noting that validation includes both simple checks at the time of entry and more complex logical checks run after entry. The latter category includes consistency checks across different time points or visits. (p. 170, Section: Data Validation). https://doi.org/10.4103/0253-7613.93842