Versions Compared

Old Version 6

changes.mady.by.user Christine Connolly

Saved on

compared with

New Version 7

changes.mady.by.user Christine Connolly

Saved on

Key

  • This line was added.
  • This line was removed.
  • Formatting was changed.

If a specification does not exist in this guide to 




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,

  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.





SDTM - The SDTM Standard Domain Models

Final domains will be published only in an SDTM implementation guide (this guide or another implementation guide, e.g., SDTMIG for Medical Devices). Therapeutic-area standards projects and other projects may develop proposals for additional domains. Draft versions of these domains may be made available in the CDISC wiki in the SDTM Draft Domains space (https://wiki.cdisc.org/display/SDD/SDTM+Draft+Domains+Home).

...

  • 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.
  • Any 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 3 bullets. The SDTM allows for the inclusion of a sponsor's non-SDTM variables using the Supplemental Qualifiers special-purpose dataset structure, described in Section 8.4, Relating Non-standard Variable Values to a Parent Domain. As the SDTM continues to evolve, certain additional standard variables may be added to the general observation classes. 
  • 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. Refer to the Define-XML standard (available at https://www.cdisc.org/standards/data-exchange/define-xml) for additional details on how to manage no data availability.
    • 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 and should not be declared in the Define-XML document.


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,

  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.



Figure. Creating a New Domain