Representation

46

admin — Fri, 20 Mar 2020 15:44:34 +0000

46 admin Fri, 03/20/2020 - 22:44

A comprehensive data dictionary is available for end users.

Examples

1: Database platform has feature for export of CRF field definitions and schema of table relations.

2: Dictionary has annotations as needed to provide guidance on coding decisions.

3: Dictionary cites primary sources where appropriate for uniform data collection (e.g., NINDS TBI CDEs)

Post-collection

Representation

44

admin — Fri, 20 Mar 2020 15:43:22 +0000

44 admin Fri, 03/20/2020 - 22:43

The version lock-down of the database for data entry is clearly specified.

Examples

1: Criteria in place for closing and locking eCRFs in the database.

Post-collection

Representation

43

admin — Fri, 20 Mar 2020 15:42:58 +0000

43 admin Fri, 03/20/2020 - 22:42

Known/emergent issues with the data dictionary are documented and reported in an accessible manner.

Run-time

Representation

35

admin — Fri, 20 Mar 2020 15:35:59 +0000

35 admin Fri, 03/20/2020 - 22:35

Automated variable transformations are documented and tested before implementation and if modified.

Examples

1: For example, testing is carried out if fields such as ‘years of education’ and ‘level of education’ are automatically combined into a new variable summarising educational status.

2: Coding is tested on a subset of data, ideally by multiple members of the study team.

3: There is a centralised log / version management of all transformations used.

Run-time

Representation

27

admin — Fri, 20 Mar 2020 15:25:32 +0000

27 admin Fri, 03/20/2020 - 22:25

Data are provided in a form that is unambiguous to researchers.

Examples

1: Table and study form fields relationships are clearly identified in the data dictionary.

Design-time

Representation

19

admin — Fri, 20 Mar 2020 15:08:25 +0000

19 admin Fri, 03/20/2020 - 22:08

Internationalisation is undertaken in a robust manner, and translation and cultural adaption of concepts (e.g. assessment tools) follows best practice.

Examples

1: Systematic approach to translation and cross-cultural validation of study material in multi-language settings.

2: Psychometric transformations considered.

Design-time

Representation

17

admin — Fri, 20 Mar 2020 15:07:22 +0000

17 admin Fri, 03/20/2020 - 22:07

The data entry tool does not perform rounding or truncation of entries that might result in precision-loss.

Examples

1: Where decimal places are to be limited, it should be clear to the user and prevent them from entering the value rather than a truncation performed with the user being unaware.

Design-time

Representation

14

admin — Fri, 20 Mar 2020 15:04:27 +0000

14 admin Fri, 03/20/2020 - 22:04

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.

Design-time

Representation

8

admin — Fri, 20 Mar 2020 14:55:24 +0000

8 admin Fri, 03/20/2020 - 21:55

There is no duplication in the data set: data has not been entered twice for the same participant.

Examples

1: Duplication identification process in place to assure single entry of codes assigned to unique subjects by institution across repository data sets (e.g., blood samples, imaging sequences and other assessments).

2: Audits are conducted to reconcile record linkages and assure no duplicates are entered (e.g., image sequence files not submitted twice; subject CRF only entered once in database; or biospecimen have unique IDs at different timepoints and only entered once).

3: For data across multiple platforms assure same subject ID is used in the database. For example, Global unique identifier (GUID).

Design-time

Representation

7

admin — Fri, 20 Mar 2020 14:54:48 +0000

7 admin Fri, 03/20/2020 - 21:54

Each individual has a unique identifier.

Examples

1: The GUID (Global Unique IDentifier) or the GUPI (Generated Unique Patient Identifier) is a method of ensuring that patients are not duplicated across datasets.

Design-time

Representation