Conformance to Standards

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 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 standardized tabulation datasets.
2	Following conventions for dataset naming.	The dataset name is standardized per naming conventions and per controlled terminology where applicable.
3	Following guidance for dataset record structure.	The dataset record structure is aligned with the structure specified per the applicable domain specification.
3	Following domain specifications in this guide wherever applicable.	The dataset
4		Using specified standard domain names and prefixes per controlled terminology where applicable
5		Following SDTM-specified controlled terminology and format guidelines for variables, when provided
6	Following conventions for variable names.	The names of variables are standardized per tabulation guidance for all variables in the dataset.
7	Following conventions for variable labels.	The labels for variables are standardized per tabulation guidance for all variables in the dataset.
9		Conforming to guidance in CDISC Notes column and general and domain-specific assumptions.

8	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.

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

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

<Add references to conformance rules here>

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Conformance to Standards