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Study phase:
Dimension:
14
There are mechanisms in place to enforce / ensure that time-sensitive data is entered within allotted time windows.
Examples

1: Database has flags for identification of when CRF completion or a study visit is overdue.

2: Automated weekly messages sent to coordinators listing overdue eCRFs.

3: Central study staff responsible for monitoring overdue CRFs. 

Study phase:
Dimension:
13
Database rule checks are in place to identify conflicts in data entries for related or dependent data collected in different CRFs or sources.
Examples

1: Scheduled queries are run for data checks across tables/CRFs for variables prone to conflict.

2: There should be rules to ensure that incompatible choices are excluded both within a data element (e.g. cannot be both male and female) and between related elements (e.g. male cannot be pregnant).

Study phase:
Dimension:
12
Free text avoided unless clear scientific justification and (e.g. qualitative) analysis plan specified and feasible.
Examples

1: ‘Other’ checkbox included in field response options as appropriate with free field text for description.

2: Free-text fields set as containing PHI to avoid inadvertent export/release of personal information. 

Study phase:
Dimension:
11
Range and logic checks are in place for CRF response fields that require free entry of numeric values. Permissible values and units of measurement are specified at data entry.
Examples

1: Avoid free text fields when possible.

2: eCRF has automated error flags to prompt immediate alert to review entries that don’t pass validation with option to override when appropriate. 

Study phase:
Dimension:
10
Missingness is defined and is distinguished from ‘not available’, ‘not applicable’, ‘not collected’ or ‘unknown.’ For optional data, ‘not entered’ is differentiated from ‘not clinically available’ depending on research context.
Examples

1: Definitions are agreed upon at design.

2: Codes are defined as appropriate to study settings: missing data from hospital record (e.g. not recorded) versus missing data from a study appointment (e.g. subject did not return etc.). 

Study phase:
Dimension:
9
Data that is mandatory for the study is enforced by rules at data entry and user reasons for overriding the error checks (queries) are documented in the database.
Examples

1: Mandatory elements require a value or explanation for reason missing.

2: Curation team is responsible for reviewing and accepting or rejecting explanations.

3: Data completeness for key variables is checked against pre-specified study design goals and minimum standards for data completeness in key areas are met.

4: Quality control is in place to ensure that completed clinical measurements or investigations such as imaging meet the specifications in the study protocol.

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