In this section, conformance refers to whether implementation of standards per this guide meets the requirements of the standards. Conformant collection and representation of tobacco product study data is ensured by full adherence to standards per this guide. Conformance to standards may be minimally is assessed by confirming implementation of standards per this guide and by evaluating tabulation and analysis data in relation to conformance rules.
For data collection using CRFs, conformance to standards is minimally assessed by confirming adherence to the following described in Section x.x, indicated by:
| Metadataspec |
|---|
Guidance| Conformance | Description |
|---|
| 1 | Adherence to TIG Core designations |
are followed| . | All HR (Highly Recommended) and applicable R/C (Recommended/Conditional) data collection fields are present in the CRF and/or operational database. | | 2 | Adherence to specified use of CDISC Controlled Terminology |
is used in alignment with this guide| . | Specified controlled terminology must be used to collect the data in the CRF. | | 3 | Best practices for CRF design are followed. |
Specified | The design of the CRF follows |
recommendations in Section x.x, Creating Data Collection Instruments and Section x.x,CRF Design.4 | The wording of CRF questions is standardized. | both recommendations for creating data collection instruments and CRF design. | | 4 | Adherence to Question Text or Prompts. |
Question Text or Prompts are used to |
ask the question per associated guidance.standardize the wording of CRF questions. | | 5 |
Naming conventions for data collection variables are used in the operational database. | | Adherence to Data Collection Variable naming conventions. | Data Collection Variable |
Variable naming conventions are used in the operational database as specified in this guide. | | 6 | Data output by the operational database into a tabulation dataset variable requires minimal processing when the data collection variable and tabulation variables names are the same. |
should requires no additional processing if the CDASH and SDTM variable names are the same.
- An SDTM data programmer should be able to assume that data in an SDTMIG variable is SDTMIG-compliant. Minimal processing (e.g., changing case) does not affect conformance. This helps to ensure a quality deliverable, even if the programmer is unfamiliar with data capture practices.
should requires no additional processing if the CDASH and SDTM variable names are the same.
- An SDTM data programmer should be able to assume that data in an SDTMIG variable is SDTMIG-compliant. Minimal processing (e.g., changing case) does not affect conformance. This helps to ensure a quality deliverable, even if the programmer is unfamiliar with data capture practices.
| | 7 | Validated questionnaires, ratings, or scales present the questions and reply choices as validated. | - his must be followed to maintain the validity of a validated instrument. (See Section 8.3.12, QRS - Questionnaires, Ratings, and Scales).
- In some cases, this may result in CRFs that do not conform to CDASH best practices; however, restructuring these questionnaires should not be done because it could invalidate them.
- The use of such questionnaires in their native format should not be considered to affect conformance to CDASH.
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Guidelines for Tabulation Datasets
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