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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 one 1 topic is identified, then more than one 1 domain may be needed.
      1. In such cases, consider whether topics are hierarchical in nature, where data for one 1 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 e.g., Pharmacokinetics Concentrations (PC) and Pharmakinetics Pharmacokinetics Parameters (PP) is an established domain pair). Records 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 Qualifiers applicable to the data (e.g., --METHOD, --SPEC) as appropriate. 
3. Look for a domain within this guide Look for an existing, relevant domain model to serve as a prototype. If no existing model no domain seems appropriate, choose the general observation class class in the SDTM (Interventions, Events, or Findings) that best fits the data by considering given 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.

These general rules apply when determining which variables to include in a custom domain:

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1. observations.
2. 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 variables for a custom domain is to:
   1. Include applicable Identifier variables. Identifier variables STUDYID, USUBJID, DOMAIN, and --SEQ are required in all domains based on the general observation classes.

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1. 1. Additional Identifiers may be added as needed.

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• 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.

1. 1. Include the Topic variable from the SDTM general observation class (e.g., --TESTCD for Findings). 
   2. Include the relevant Qualifier variables from the identified SDTM general observation class.
   3. Include the applicable SDTM Timing variables. In general, the domain must have at least 1 timing variable.
2. 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.
3. Apply the

Creating a New Domain - SDTMIG v3.4 - Wiki (cdisc.org)

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 requitements of the study. To create a custom domain,

1.  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.
2. 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.
3. 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:
4. Select and include the required identifier variables (e.g., STUDYID, DOMAIN, USUBJID, --SEQ) and any permissible Identifier variables from the SDTM. 
5. Include the topic variable from the identified general observation class (e.g., --TESTCD for Findings) in the SDTM. 
6. 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.
7. Select and include the applicable timing variables in the SDTM.
8. 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
9. domain code to the appropriate variables in the domain
. Replace
10. by replacing all variable prefixes (shown in the
models
11. SDTM as “--“) with the domain code.
Set the
12. Set the order of variables consistent with the order defined in the SDTM for the
general observation
13. general observation class.
14. Adjust the labels of the variables only as appropriate to
properly
15. convey
the
16. their meaning in the context of the data
being submitted
17. 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)
18. .
19. Ensure that appropriate standard variables are being properly applied by comparing their use in the custom domain to their use in
standard
20. related TIG domains.

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

22. Place any non-
standard (
23. SDTM
)
24. 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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1. Jira
   showSummary false server Issue Tracker (JIRA) serverId 85506ce4-3cb3-3d91-85ee-f633aaaf4a45 key TOBA-795

   in a Supplemental Qualifier dataset. 

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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 requi ements of the study. To create a custom domain,

1. 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.
2. 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.
3. 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.