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Standards in this guide are aligned with both use cases and activities. Given this, determining which standards to use may begin by selecting standards for the use case and activity to be supported. It recommended that all guidance is reviewed, both high-higher level and detailed, prior to implementing standards. For ease of use, the table below presents use cases, activities, and corresponding sections in this guide which provide detailed instructions for implementation. Detailed sections instructions referenced are:

• Section x.x, Standards for Collection, to guide development and use of case report forms (CRFs) by implementing the CDISC CDASH Model.
• Section x.x, Standards for Tabulation, to guide organization of data collected, assigned, or derived for a study by implementing SDTM.
• Section x.x, Standards for Analysis, to specify the principles to follow in the creation of analysis datasets and associated metadata by implementing the ADaM.
• Section x.x, Standards for Data Exchange, to support sharing of structured data between parties and across different information systems by implementing specified standard specifications standards and resources. 

he CDASH Model aligns with and is structured similarly to the SDTM. The CDASH Model organizes data into classes, which represent meaningful groupings of data in clinical research. It defines CDASH metadata for identifier variables, timing variables, general observation class variables (Events, Interventions, and Findings), domain-specific variables, and special-purpose domain variables.

The current CDASH Model represents Identifier, Timing, and Domain-specific variables in the metadata as an Observation Class. This does not align with the SDTM. This will be revised in a future version of the CDASH Model.

Once standards are selected based on the use case and activity, the scientific subject matter of the data, its role, and analysis needs will determine where data belong, i.e., how the data are collected, represented, or exchanged using the standards. Standards for collection and tabulation collect and represent data in groupings of logically related data called domains. Domains are aligned between collection and tabulation standards to facilitate the transition of collected observations to representation in tabulation datasets. Standards for analysis focus on analysis requirements

are used to design customized datasets is cucustomizable per analysis requirements.

Analysis needs drive the selection of analysis standards, where analysis requirements are supported by both the structure and the contents of the resulting dataset. 

Standards for collection, tabulation, and analysis collect and represent data by common topics with standards for collection and tabulation grouping logically related data in domains and the design of analysis datasets customizable per analysis requirements. The scientific subject matter of the data further drives the selection of collection and tabulation standards, where the nature of the data must be within the scope of a domain for the domain to be used. Analysis needs drive the selection of analysis standards, where analysis requirements are supported by both the structure and the contents of the resulting dataset. Standards for data exchange are applicable to all use cases and support sharing of standard CRFs, tabulation datasets, and analysis datasets.

Standards for collection and tabulation group logically related data in domains while  All standards are designed to flow together - The table below describes 

The logic of the relationship may pertain to the scientific subject matter of the data or to its role in the trial. 

Domains

he CDASH Model aligns with and is structured similarly to the SDTM. The CDASH Model organizes data into classes, which represent meaningful groupings of data in clinical research. It defines CDASH metadata for identifier variables, timing variables, general observation class variables (Events, Interventions, and Findings), domain-specific variables, and special-purpose domain variables.

The current CDASH Model represents Identifier, Timing, and Domain-specific variables in the metadata as an Observation Class. This does not align with the SDTM. This will be revised in a future version of the CDASH Model.

Connections between standards

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A domain is defined as a collection of logically related observations with a common topic. The logic of the relationship may pertain to the scientific subject matter of the data or to its role in the trial. 

Each domain dataset is distinguished by a unique, 2-character code that should be used consistently throughout the submission. This code, which is stored in the SDTM variable named DOMAIN, is used in 4 ways: as the dataset name, as the value of the DOMAIN variable in that dataset, as a prefix for most variable names in that dataset, and as a value in the RDOMAIN variable in relationship tables (see Section 8, Representing Relationships and Data).

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