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CDISC models implemented for standardization of in this guide to standardize data collection, data tabulation, and creation of analysis datasets for each use case category are summarized in the table below.

Data for 

Aside from a limited number of special-purpose domains, all subject-level SDTM datasets are based on 1 of the 3 general observation classes. When faced with a set of data that were collected and that "go together" in some sense, the first step is to identify SDTM observations within the data and the general observation class of each observation. Once these observations are identified at a high level, 2 other tasks remain:

• Determining whether the relationships between these observations need to be represented using GRPID within a dataset, as described in Section 8.1, (SENDIG v3.1.1) Relating Groups of Records Within a Domain Using the --GRPID Variable, or using RELREC between datasets, as described in Section 8.3, (SENDIG v3.1.1) Supplemental Qualifiers - SUPP-- Datasets
• Placing all the data items in 1 of the identified general observation class records, or in a SUPP-- dataset, as described in Section 8.5, (SENDIG v3.1.1) Relating Findings To Multiple Subjects - Subject Pooling

All models implemented as part of this guide collect and represent data by common topics with:

• CDASH and SDTM organizing related data points in groupings called domains; and 
• ADaM dataset design customized to support analysis requirements.

ADD MORE TEXT HEREIn practice, considering the representation of relationships and placing individual data items may lead to reconsidering the identification of observations, so the whole process may require several iterations.