Concordance

28

admin — Fri, 20 Mar 2020 15:26:08 +0000

28 admin Fri, 03/20/2020 - 22:26

For physiological data the methods of measurement and units are defined for all sites.

Examples

1: Uniform collection standards are in place.

2: When uniform measures are not used by hospital units or labs, the CRF provides conversion guidance.

3: Inter-site sampling differences are described in the data dictionary.

Design-time

Concordance

23

admin — Fri, 20 Mar 2020 15:11:24 +0000

23 admin Fri, 03/20/2020 - 22:11

Data collectors are tested and provided with feedback regarding the accuracy of their performance across all relevant study domains.

Examples

1: Random audits of clinical source documents against data entered on hard copy and eCRFs.

2: Double data entry audits of selected forms that are known to be prone to errors.

3: Audit of eCRF and hard copy logs for biospecimens; audit of complex assessments by experts.

4: Training and on-line documentation of how to handle exceptions.

Design-time

Concordance

20

admin — Fri, 20 Mar 2020 15:09:06 +0000

20 admin Fri, 03/20/2020 - 22:09

Data collection methods are documented in study manuals that are sufficiently detailed to ensure the same procedures are followed each time.

Examples

1: Manuals compiled by multidisciplinary team of stakeholders and involve data curation expertise. For example, Imaging studies and biospecimen collection procedures are detailed for study personnel and end-users.

2: Manuals specify how eCRF data is collected, when, by what metrics and under what conditions.

3: CRF fields have links to relevant data manual sections.

Design-time

Concordance

13

admin — Fri, 20 Mar 2020 15:03:52 +0000

13 admin Fri, 03/20/2020 - 22:03

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).

Design-time

Concordance

4

admin — Fri, 20 Mar 2020 14:52:48 +0000

4 admin Fri, 03/20/2020 - 21:52

Data-types are specified for each variable.

Examples

1: Specifications for floating point, integer, factor or free text

Design-time

Concordance

3

admin — Fri, 20 Mar 2020 14:51:57 +0000

3 admin Fri, 03/20/2020 - 21:51

The data ontology is consistent with published standards (common data elements) to the greatest extent possible.

Examples

1: Data base fields conform as appropriate to FITBIR NINDS Common Data Elements for Traumatic Brain Injury; CDISC; CDASH; and Abbreviated Injury Scores.

2: Pre-specified data ontology at design-time.

3: Published data elements used as far as possible. However, when not possible, deviations from published specifications clearly documented and have robust scientific justification.

Design-time

Concordance