Design-time

32

admin — Fri, 20 Mar 2020 15:29:21 +0000

32 admin Fri, 03/20/2020 - 22:29

There is automated entry of the results of biospecimen samples

Examples

1: Vial labels IDs entered into bio-repository logs via bar code scanner.

Design-time

Correctness

31

admin — Fri, 20 Mar 2020 15:28:44 +0000

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

Biospecimen assay accuracy, precision, repeatability, detection limits, quantitation limits, linearity and range are defined. Normal ranges are determined for each assay.

Examples

1: Normal ranges are determined for each assay.

2: Assays are standardised as far as possible.

3: Assay substrate are defined (e.g. ionised vs. unionised fractions, whole blood vs serum, total vs protein bound as appropriate).

4: Normal ranges should consider/be customised to pathology or subject group in question.

Design-time

Correctness

30

admin — Fri, 20 Mar 2020 15:27:48 +0000

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

Biospecimen preparation techniques are standardised.

Examples

1: Clear Biospecimen Standard Operating Procedure (SOP) for sample preparation.

2: Lab SOP aligned with agreed sample handling standards.

3: Adequate training of lab personnel across sites.

4: Published / national / international standards should be specified and adhered to where possible.

5: Audit trail of sample processing and storage.

6: Protocol deviations filed when protocol not followed.

7: Audits of time intervals for drawing, processing and freezing are within protocol windows for each specimen type.

Design-time

Correctness

29

admin — Fri, 20 Mar 2020 15:26:58 +0000

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

Imaging acquisition techniques are standardised (e.g. magnetic resonance imaging).

Examples

1: Phantom standardization of MRI scanners across study sites.

2: Appropriate assurance of quality and comparability between sites and within sites and assurance of ongoing compliance over time.

3: SOP aligned with agreed imaging acquisition standards.

4: Adequate training of technical personnel across sites.

Design-time

Correctness

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

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

26

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

26 admin Fri, 03/20/2020 - 22:24

There is a clear audit chain for any data processing that takes place after entry, and this should have a mechanism for version control if it changes.

Examples

1: Change logs maintained and audited.

2: Workflows and ETL (extract / transform / load) processes are described.

Design-time

Correctness

25

admin — Fri, 20 Mar 2020 15:12:30 +0000

25 admin Fri, 03/20/2020 - 22:12

Assessors are blinded to treatment allocation or predictor variables where appropriate and such blinding is explicitly recorded.

Examples

1: Therapists administering the Functional Independence Measure (FIM) tool do not know who was given an experimental treatment or a placebo.

Design-time

Correctness

24

admin — Fri, 20 Mar 2020 15:12:00 +0000

24 admin Fri, 03/20/2020 - 22:12

Data collection that requires specific content expertise is carried out by trained and/or certified investigators.

Examples

1: Certification processes specified in protocols.

Design-time

Correctness

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