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1. Establish a common topic or common topics for the data. The common topic or topics will reflect a collection of logically related observations based on the scientific subject matter of the data.
   1. If
it is necessary to represent relationships between data that are hierarchical in nature (e.g., a parent record must be observed before child records), then establish a domain pair (e.g., MB/MS, PC/PP). Note: Domain pairs have been modeled for microbiology data (MB/MS domains) and pharmacokinetics (PK) data (PC/PP domains) to enable dataset-level relationships to be described using RELREC. The domain pair uses DOMAIN as an identifier to group parent records (e.g., MB) from child records (e.g., MS) and enables a dataset-level relationship to be described in RELREC. Without using DOMAIN to facilitate description of the data relationships, RELREC, as currently defined, could not be used without introducing a variable that would group data like DOMAIN.
2. 1. more than one topic is identified, then more than one domain may be needed.
      1. In such cases, consider whether topics are hierarchical in nature where data for one topic must be observed before data for a second topic can be observed. If a hierarchical relationship between topics exists, then a paired domains will be created (an example of an established domain pair is Pharmacokinetics Concentrations (PC) and Pharmakinetics Parameters (PP)). Records in paired domains may then be represented in the Related Records (RELREC) dataset as appropriate.
   2. Categorize data within the domain using Grouping Qualifier variables (e.g.,
   Group and separate data within the domain using
   1. --CAT, --SCAT) and identify other qualifiers applicable to the data (e.g., --METHOD, --SPEC
   , --LOC, and so on,
   1. ) as appropriate.
   Examples of different topics are: microbiology, tumor measurements, pathology/histology, vital signs, and physical exam results.
   1.  

1. Look for an existing, relevant domain model to serve as a prototype. If no existing model seems appropriate, choose the general observation class (Interventions, Events, or Findings) that best fits the data by considering the topic of the observation. As illustrated in the following figure, the general approach for selecting variables for a custom domain is:
   1. Select and include the required identifier variables (e.g., STUDYID, DOMAIN, USUBJID, --SEQ) and any permissible Identifier variables from the SDTM. 
   2. Include the topic variable from the identified general observation class (e.g., --TESTCD for Findings) in the SDTM. 
   3. Select and include the relevant qualifier variables from the identified general observation class in the SDTM. Variables belonging to other general observation classes must not be added.
   4. Select and include the applicable timing variables in the SDTM.
   5. Determine the domain code, one that is not a domain code in the CDISC Controlled Terminology SDTM Domain Abbreviations codelist (see  https://datascience.cancer.gov/resources/cancer-vocabulary/cdisc-terminology). If it is desired to have this domain code be part of CDISC Controlled Terminology, submit a request at https://ncitermform.nci.nih.gov/ncitermform/?version=cdisc. The sponsor-selected, 2‑character domain code should be used consistently throughout the submission. AD, AX, AP, SQ, and SA may not be used as custom domain codes.
   6. Apply the 2-character domain code to the appropriate variables in the domain. Replace all variable prefixes (shown in the models as “--“) with the domain code.
   7. Set the order of variables consistent with the order defined in the SDTM for the general observation class.
   8. Adjust the labels of the variables only as appropriate to properly convey the meaning in the context of the data being submitted in the newly created domain. Use title case for all labels (title case means to capitalize the first letter of every word except for articles, prepositions, and conjunctions).

   9. Ensure that appropriate standard variables are being properly applied by comparing their use in the custom domain to their use in standard domains.

   10. Describe the dataset within the Define-XML document. See Section 3.2, Using the CDISC Domain Models in Regulatory Submissions — Dataset Metadata.

   11. Place any non-standard (SDTM) variables in a Supplemental Qualifier dataset. Mechanisms for representing additional non-standard qualifier variables not described in the general observation classes and for defining relationships between separate datasets or records are described in Section 8.4, Relating Non-standard Variable Values to a Parent Domain.

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