Conformance to Standards

Conformant collection and representation of tobacco product study data is ensured by full adherence to standards per this guide. Conformance to standards 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 ensured by:

Num	Conformance	Implementation
1	Following best practices for CRF design.	The design of the CRF follows both recommendations for creating data collection instruments and recommendations for CRF design.
2	Following Data Collection Variable naming conventions.	Data Collection Variable naming conventions are applied in the operational database as specified.
3	Following standard wording for Question Text or Prompts.	The wording of CRF questions is standardized per specified Question Text or Prompts for the data collection fields.
4	Following Core designations.	All HR (Highly Recommended) and applicable R/C (Recommended/Conditional) data collection fields are present in the CRF and/or operational database.
5	Following guidance for CDISC Controlled Terminology.	Controlled terminology is used as specified to collect the data using the CRF.
6	Presenting validated QRS questions and reply choices as validated in the CRF. In some cases, this may result in CRFs that do not conform to CDASH best practices. The use of such questionnaires in their native format does not affect conformance.	All QRS questions and reply choices are presented as validated in the CRF.
7	Aligning data collection variables values and target tabulation variables values when collection and tabulation variable names are the same. Minimal processing, such as changing case when mapping a data collection variable value into a tabulation variable, does not affect conformance.	Data output by the operational database into a tabulation dataset variable requires minimal processing when the data collection and tabulation variable names are the same.

For tabulation datasets, conformance to standards is minimally ensured by:

Num	Conformance	Implementation
1	Representing all collected, assigned, and relevant derived data in applicable datasets.	All data generated per scientific and regulatory requirements are included in tabulation datasets.
2	Using domain specifications in this guide wherever applicable.	A dataset is created using a domain specification in this guide when the scientific nature or role of the data is within the scope of a domain. Domains are extended or custom domain specifications are only used when data are different in nature and are not in scope for domains in this guide.
3	Following conventions for dataset naming.	The dataset name is standardized per naming conventions and per controlled terminology where applicable.
4	Following guidance for dataset record structure.	Dataset content is aligned with the record structure specified per the domain specification.
5	Following Core designations.	All Required and Expected tabulation variables are included as columns in the dataset. Required tabulation variables are populated for all records in the dataset.
6	Following conventions for variable naming.	The names of variables in the dataset are standardized per domain specifications and other applicable guidance. Controlled terminology for domain prefixes is used as specified for variable naming.
7	Following conventions for variable labels.	The labels for variables in the dataset are standardized per domain specifications and other applicable guidance.
8	Following guidance for variable types.	The variables in the dataset standardized for either numeric or character values as specified per the domain specification.
9	Populating variable values in alignment with this guide.	All variables in the dataset are populated as expected per this guide including per general and domain-specific guidance, controlled terminology, and formatting.

For analysis datasets, conformance to standards is minimally ensured by:

Num	Conformance	Implementation
1

Tabulation and analysis dataset conformance can be formally evaluated by comparing dataset content in relation to CDISC Conformance Rules.

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Conformance to Standards