Plausibility

Study phase:
Run-time
Dimension:
Plausibility
40
Reliability checks have been performed on variables that are critical to research hypotheses, to ensure that information from multiple sources is consistent.
Examples

1: Database rule checks in place to identify conflicts in data entries for related or dependent data collected in different CRFs or sources.

2: Inter-rater scoring of MRIs is done independently by 3 experts on 10 cases per month. 

Study phase:
Run-time
Dimension:
Plausibility
38
Systematic and timely measures are in place to assure ongoing data accuracy.
Examples

1: Schedule of standard reports for data quality checks.

2: Ad hoc reports for identification of new issues. 

Study phase:
Run-time
Dimension:
Plausibility
37
Individual data elements should be checked for missingness. This should be done against pre-specified skip-logic / missingness masks. This should be performed throughout the study data acquisition period to give accurate ‘real time’ feedback on completion status.
Examples

1: Rules in place to check whether the apparently missing element should be completed or not for a particular study stratum or time point.

Study phase:
Design-time
Dimension:
Plausibility
18
Extract / transform / load software for batch upload of data from other sources such as assay results should flag impossible and implausible values.
Examples

1: ETL process logged.

2: Permissible value ranges in the ETL software are study specific rather than relying on institution permissible value logic. 

Study phase:
Design-time
Dimension:
Plausibility
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. 

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