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• The timing of collected observations (e.g., all vital signs measurements will be represented in the Vital Signs (VS) domain irrespective of when measurements occurred)
• How collected data are used (e.g., all vital signs measurements will be represented in VS and a custom "safety" domain will not be created for measurements used to assess safety)
• Data collection methodology 

  Jira
  showSummary false server Issue Tracker (JIRA) serverId 85506ce4-3cb3-3d91-85ee-f633aaaf4a45 key TOBA-619

  (methodology (e.g., all vital signs measurements will be represented in VS irrespective of whether measurements were recorded using separate CRFs, a medical device like a fitness tracker, or an electronic diary)

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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 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 (Interventions, Events, or Findings) that best fits the data given the topic of the observations.
4. 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. Additional Identifiers may be added as needed.
   2. Include the Topic variable from the SDTM general observation class (e.g., --TESTCD for Findings). 
   3. Include the relevant Qualifier variables from the identified SDTM general observation class.
   4. Include the applicable SDTM Timing variables. In general, the domain must have at least 1 timing variable.
5. Determine the 2-character domain code. JirashowSummaryfalseserverIssue Tracker (JIRA)serverId85506ce4-3cb3-3d91-85ee-f633aaaf4a45keyTOBA-616
   
   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.
6. Apply the domain code to the appropriate variables in the domain by replacing all variable prefixes (shown in the SDTM 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 convey their meaning in the context of the data in the newly created domain. Use title case for all labels.
9. Ensure that appropriate standard variables are being properly applied by comparing their use in the custom domain to their use in related TIG domains.
10. Place any non-SDTM variables

    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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