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The scientific subject matter of the data and related activities such as data collection, data tabulation, data analysis, and data exchange drive which standards to implement. Implementation of standards starts with determining which standards are appropriate in this guide will be used based on the nature of the data and activities to be supported. After standards are selectedan applicable set or sets of standards have been identified, it is then possible to determine how the data are collected, represented, or exchanged using the standards. 

Standards Sets of 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 is recommended that all TIG guidance is reviewed, both general 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 instructions referenced are:

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Once standards are selected identified 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 using groupings of logically related data called domains. Domains are aligned between collection and tabulation standards to facilitate the transition of collected observations to their representation in tabulation datasets. Standards for analysis are organized in relation to analysis requirements with the structure of tabulation datasets facilitating the generation of analysis datasets.

Mention classes?

Most product-level and subject-level observations collected during the  should be represented according to 1 of the 3 SDTM general observation classes: Interventions, Events, or Findings. 

Standards for collection and tabulation in this guide collect and represent data using groupings of logically related data called domains. Domains Domains are aligned between collection and tabulation standards to facilitate the transition of collected observations to their representation in tabulation datasets. Standards for analysis are organized in relation to analysis requirements with the structure of tabulation datasets facilitating the generation of analysis datasets.Custom domains and corresponding specifications must be created based on the three general observation classes, Interventions, Events, and Findings, described in the SDTM

To use standards for collection and tabulation, compare the nature or role of the data to the scope of a domain. Domain names provide short descriptions of intended scope and may be used to narrow down which domains to consider.   A domain standard may be used when the nature of the data and the domain scope are aligned.Observations will be collected using standardized collection standards fields when applicable and represented as rows in tabulation datasets. Each observation is described by a series of data points which correspond to applicable data collection fields and columns variables in a tabulation dataset. A data collection field and/or tabulation variable may be used when the subject matter of a data point and the scope of a field and/or variable are aligned.  To use standards for analysis, "you need to know how you want to use the dataset"The majority of data for a submission will be in scope for domains based on the three General Observation Classes described in the SDTM with data also in scope for additional domains and datasets based on other SDTM classes and defined datasets. Given this, referring to the SDTM is recommended when using domains as this supports understanding of intended domain scope and also informs how domains may be extended or created using the SDTM.

Standards for data exchange are applicable to all use cases and support sharing of standard CRFs developed using collection standards, tabulation datasets generated using tabulation standards, and analysis datasets designed using analysis standards.

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