Completeness

36

admin — Fri, 20 Mar 2020 15:36:44 +0000

36 admin Fri, 03/20/2020 - 22:36

There is centralized monitoring of the completeness and consistency of information during data collection.

Run-time

Completeness

34

admin — Fri, 20 Mar 2020 15:35:28 +0000

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

Proxy responses for factual questions (such as employment status) are allowed in order to maximize completeness.

Examples

1: Protocol is designed to allow for surrogate to respond to selected questions when patient is too impaired to respond. CRF indicates when surrogate is responding.

Run-time

Completeness

33

admin — Fri, 20 Mar 2020 15:31:47 +0000

33 admin Fri, 03/20/2020 - 22:31

A team of data-curation experts are involved with pre-specified initial and ongoing testing for quality assurance.

Examples

1: Anticipated and unanticipated data quality issues are identified and appropriate solutions/mitigation strategies are devised in a timely manner.

2: There is a process in place for ongoing systematic quality checking and analysis of data during data acquisition to identify unexpected data quality issues in order to remedy them in a timely manner.

Training and Testing

Completeness

15

admin — Fri, 20 Mar 2020 15:05:19 +0000

15 admin Fri, 03/20/2020 - 22:05

There is clear documentation of interdependence of CRF fields, including data entry skip logic.

Examples

1: For data entry: cells for skipped questions are closed.

2: Clear directions on CRFs for when to skip questions.

Design-time

Completeness

10

admin — Fri, 20 Mar 2020 15:01:44 +0000

10 admin Fri, 03/20/2020 - 22:01

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.).

Design-time

Completeness

9

admin — Fri, 20 Mar 2020 14:56:12 +0000

9 admin Fri, 03/20/2020 - 21:56

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.

Design-time

Completeness

2

admin — Fri, 20 Mar 2020 14:41:43 +0000

2 admin Fri, 03/20/2020 - 21:41

There is a robust process for choosing and designing the dataset to be collected that involves appropriate stakeholders, including a data-curation team with appropriate skill mix.

Examples

1: Data governance team should include all the important and key stakeholders such as: data curator, steward, analysts.

2: Subject matter experts should be involved from the outset (study design time) to ensure that the data is sufficient and aligned to fullfill the study objectives.

3: Technical experts should be involved from the outset (study design time) to ensure that the variables to be collected are appropriately specified.

Design-time

Completeness