Conformance to Standards

Conformant collection and representation of tobacco product 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	DM is created.	For studies that assess the impact of tobacco products on the health of individuals, DM is required even if no other datasets are generated.
4	Following conventions for dataset naming.	The dataset name is standardized per naming conventions and per controlled terminology where applicable.
5	Following guidance for dataset record structure.	Dataset content is aligned with the record structure specified per the domain specification.
6	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. Permissible variables used to collect data are included in the dataset, even when no data for those variables were collected.
7	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.
8	Following conventions for variable labels.	The labels for variables in the dataset are standardized per domain specifications and other applicable guidance.
9	Following guidance for variable types.	The variables in the dataset are standardized for either numeric or character values as specified per the domain specification.
10	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	ADaM fundamental principles are followed.	Datasets and associated metadata clearly and unambiguously communicate the content and source of the datasets supporting the statistical analyses performed in a clinical study. Datasets and associated metadata provide traceability to show the source or derivation of a value or a variable (i.e., the data's lineage or relationship between a value and its predecessor(s)). The metadata identify when and how analysis data have been derived or imputed. Datasets are readily usable with commonly available software tools. Datasets are associated with metadata to facilitate clear and unambiguous communication. Ideally the metadata are machine-readable. Datasets have a structure and content that allow statistical analyses to be performed with minimal programming. Such datasets are described as "analysis-ready." Datasets contain the data needed for the review and re-creation of specific statistical analyses. It is not necessary to collate data into analysis-ready datasets solely to support data listings or other non-analytical displays.
2	ADSL is created.	For studies that assess the impact of tobacco products on the health of individuals, ADSL and its related metadata are required even if no other analysis datasets are generated.
3	ADaM datasets follow the normative data found in the TIG.	Datasets follow the ADaM fundamental principles defined in the ADaM Model document and adheres as closely as possible to the TIG variable naming and other conventions.
4	Traceability principles are followed.	In the ADaM Model document, it is assumed that the original data sources for ADaM datasets are SDTM datasets, even when ADaM datasets are derived from other ADaM datasets. ADaM has features that enable traceability from analysis results to ADaM datasets and from ADaM datasets to SDTM datasets. These conventions must be followed for ADaM datasets with a CLASS value of BASIC DATA STRUCTURE, OCCURRENCE DATA STRUCTURE, and SUBJECT LEVEL ANALYSIS DATASET. Other analysis datasets should follow this convention where practical and feasible.

Tabulation and analysis dataset conformance can be formally evaluated in relation to defined sets of conformance rules. The CDISC TIG Conformance Rules Version 1.0 is available via the CDISC website, <placeholder, link pending> and includes rules for both tabulation and analysis datasets. Conformance rules for tabulation datasets assess the conformance of dataset structures and contents to the TIG tabulation standards. Conformance rules for analysis datasets assess the conformance of dataset construction to the TIG analysis standards.

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Conformance to Standards