43
Known/emergent issues with the data dictionary are documented and reported in an accessible manner.
DATA ACCESS QUALITY & CURATION FOR OBSERVATIONAL RESEARCH DESIGNS
42
Data irregularities are reported back to data collectors in a systematic and timely process. There is a standard operating procedure for data irregularities to be reported back to the data collectors and for documentation of the resolution of the issue.
Examples
1: Queries in place to identify the following types of scenarios: For 9 of 10 study sites a mandatory and critical variable is only missing in 5% of the subjects. For site 10 data is found to be missing in 30% of the subjects. For a single site study, several personnel have been collecting longitudinal study data with low numbers of missing data. A pattern of increasing numbers of missing data has been identified with a newly hired data collector.
2: Variables known to be problematic are regularly monitored by curation team and delays in site resolution of issues are reported to governing committee.
3. Aggregate data are examined for inconsistencies such as lower average mortality in patients with GCS 3 than GCS 4-5.
41
Scoring of tests is checked. Scoring is performed automatically where possible.
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.
39
Source data validation procedures are in place to check for agreement between the original data and the information recorded in the database.
Examples
1: Site visits for audit of subject hospital records against CRF and eCRF entries.
2: Submission of redacted hospital records for audit against eCRF entries.
3: Independent double entry of any data transferred to the database from paper form, either for full dataset or for representative subset.
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.