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These general rules apply when determining which variables to include in The overall process for creating a custom domain :

• The Identifier variables, STUDYID, USUBJID, DOMAIN, and --SEQ are required in all domains based on the general observation classes. Other Identifiers may be added as needed.
• In general, the domain must have at least one timing variable. Timing variables are permissible for use in any submission dataset based on a general observation class except where restricted by specific domain assumptions.
• Any additional Qualifier variables from the same general observation class may be added to a domain model except where restricted by specific domain assumptions.
• Sponsors may not add any variables other than those described in the preceding three bullets. The SDTM allows for the inclusion of non-SDTM variables using the Supplemental Qualifiers special-purpose dataset structure.
• Standard variables must not be renamed or modified for novel usage. Their metadata should not be changed.
• A Permissible variable should be used in an SDTM dataset wherever appropriate.  
  • If a study includes a data item that would be represented in a Permissible variable, then that variable must be included in the SDTM dataset, even if null.
  • If a study did not include a data item that would be represented in a Permissible variable, then that variable should not be included in the SDTM dataset.

Custom domain specifications and, by extension, custom datasets should be designed in the following steps:

1. Confirm that none of the domains published in this guide will fit the need.
2. If a domain does not exist in this guide: 
   • Establish a domain of a common topic; that is, where the nature of the data is the same rather than by a specific method of collection (e.g., electrocardiogram). Group and separate data within the domain using --CAT, --SCAT, --METHOD, --SPEC, --LOC, and so on, as appropriate. Examples of different topics are: microbiology, tumor measurements, pathology/histology, vital signs, and physical exam results.
   • Do not create separate domains based on time; rather, represent both prior and current observations in a domain (e.g., CM for all non-study medications). Note that Adverse Events (AE) and Medical History (MH) are an exception to this best practice because of regulatory reporting needs.
   • How collected data are used (e.g., to support analyses and/or efficacy endpoints) must not result in the creation of a custom domain. For example, if blood pressure measurements are endpoints in a hypertension study, they must still be represented in the Vital Signs (VS) domain, as opposed to a custom “efficacy” domain. Similarly, if liver function test results are of special interest, they must still be represented in the Laboratory Tests (LB) domain.
   • 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.

This section describes the overall process for creating a custom domain, which must be based on 1 of the 3 SDTM general observation classes. The number of domains submitted should be based on the specific requirements of the study. To create a custom domain,

is as follows:

1. Establish a common topic or 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 more than 1 topic is identified, then more than 1 domain may be needed.
      1. In such cases, consider whether topics are hierarchical in nature, where data for 1 topic must be observed before data for a second topic can be observed. If a hierarchical relationship between topics exists, then paired domains will be created (e.g., Pharmacokinetics Concentrations (PC) and Pharmacokinetics Parameters (PP) is an established domain pair). Relationships between 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., --CAT, --SCAT) and identify other Qualifiers applicable to the data (e.g., --METHOD, --SPEC) as appropriate.
3. Look for a domain within this guide to serve as a prototype. If no domain seems appropriate, choose the general observation class in the SDTM
4. Confirm that none of the existing published domains will fit the need. A custom domain may only be created if the data are different in nature and do not fit into an existing published domain.
   • Establish a domain of a common topic; that is, where the nature of the data is the same rather than by a specific method of collection (e.g., electrocardiogram). Group and separate data within the domain using --CAT, --SCAT, --METHOD, --SPEC, --LOC, and so on, as appropriate. Examples of different topics are: microbiology, tumor measurements, pathology/histology, vital signs, and physical exam results.
   • Do not create separate domains based on time; rather, represent both prior and current observations in a domain (e.g., CM for all non-study medications). Note that Adverse Events (AE) and Medical History (MH) are an exception to this best practice because of regulatory reporting needs.
   • How collected data are used (e.g., to support analyses and/or efficacy endpoints) must not result in the creation of a custom domain. For example, if blood pressure measurements are endpoints in a hypertension study, they must still be represented in the Vital Signs (VS) domain, as opposed to a custom “efficacy” domain. Similarly, if liver function test results are of special interest, they must still be represented in the Laboratory Tests (LB) domain.
   • Data that were collected on separate CRF modules or pages may fit into an existing domain (e.g., as separate questionnaires into the QS domain, prior and concomitant medications in the CM domain).
   • 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.
5. Check the SDTM Draft Domains area of the CDISC wiki (https://wiki.cdisc.org/display/SDD/SDTM+Draft+Domains+Home) for proposed domains developed since the last published version of the SDTMIG. These proposed domains may be used as custom domains in a submission.
6. 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 given the topic of the observation. As observations.
7. Select variables for the domain from the SDTM. Selection of variables must align with SDTM usage restrictions. As illustrated in the following figure, the general approach for selecting for selecting variables for a custom domain is to:
   Select and include the required identifier variables (e.g., STUDYID
   1. 1. Include applicable Identifier variables. Identifier variables STUDYID, USUBJID, DOMAIN,
   USUBJID,
   2. 1. and --SEQ
   ) and any permissible Identifier variables from the SDTM. 
   3. 1. are required in all domains based on the general observation classes. Additional Identifiers may be added as needed.
      2. Include the
   topic
   4. 1. Topic variable from the
   identified
   5. 1. SDTM general observation class (e.g., --TESTCD for Findings)
   in the SDTM
   6. 1. . 
   Select and include
   7. 1. Include the relevant
   qualifier
   8. 1. Qualifier variables from the identified SDTM general observation class
   in
   9. 1. .
      2. Include the applicable SDTM
   . Variables belonging to other general observation classes must not be added.
   10. 1. Timing variables. In general, the domain must have at least 1 timing variable.
   11. Determine the 2-character domain code.
       
       1. To eliminate the risk of using a name that CDISC later determines to have a different meaning, domain codes beginning with the letters X, Y, and Z have been reserved for the creation of custom domains. Any letter or number may be used in the second position. The use of codes beginning with X, Y, or Z is optional, and not required for custom domains.
   12. Apply the
   13. Select and include the applicable timing variables in the SDTM.
   14. 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.
   Apply the 2-character
   15. domain code to the appropriate variables in the domain
   . Replace
   16. by replacing all variable prefixes (shown in the
   models
   17. SDTM as “--“) with the domain code.
   Set the
   18. Set the order of variables consistent with the order defined in the SDTM for the
   general observation
   19. general observation class.
   20. Adjust the labels of the variables only as appropriate to
   properly
   21. convey
   the
   22. their meaning in the context of the data
   being submitted
   23. 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)
   24. .
   25. Ensure that appropriate standard variables are being properly applied by comparing their use in the custom domain to their use in
   standard
   26. related TIG domains.

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

   28. Place any non-
   standard (SDTM) variables
   29. SDTM variables

       Jira
       showSummary false server Issue Tracker (JIRA) serverId 85506ce4-3cb3-3d91-85ee-f633aaaf4a45 key TOBA-795

       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.
   30.  

   Excerpt Include
   Figure.Creating a New Domain Figure.Creating a New Domain nopanel true

    

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