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If data cannot be represented in a domain dataset defined in this guide, then a custom domain may be used. A custom domain may only be created only when data are different in nature and do not fit into a domain described in this guide. It is recommended that a specification with the dataset name, record structure, and a domain table specification be drafted prior to support implementation, but this is not required.
Custom domains and corresponding specifications may be created based on the three general observation classes, Interventions, Events, and Findings, described in the SDTM. In most cases, the choice of observation class appropriate to a specific collection of data can be easily determined according to these descriptions in the SDTM. The majority of data, which typically consists of measurements or responses to questions, usually at specific visits or time points, will fit the Findings general observation class.
These general rules apply when determining which variables to include in 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.
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- 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
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- three bullets.
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- The SDTM allows for the inclusion of
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- non-SDTM variables using the Supplemental Qualifiers special-purpose dataset structure
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- 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
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- study includes a data item that would
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- be represented in a Permissible variable, then
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- that variable must be included in the SDTM dataset, even if null
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- .
- If a study did not include a data item that would be represented in a Permissible variable, then that variable should not be
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New domain specifications may be created when domain specifications in this guide don't exist
The General Observation Classes - SDTMIG v3.4 - Wiki (cdisc.org)
Most subject-level observations collected during the study should be represented according to 1 of the 3 SDTM general observation classes: Interventions, Events, or Findings. The lists of variables allowed to be used in each of these can be found in the SDTM.
- The Interventions class captures investigational, therapeutic, and other treatments that are administered to the subject (with some actual or expected physiological effect) either as specified by the study protocol (e.g., exposure to study drug), coincident with the study assessment period (e.g., concomitant medications), or self‑administered by the subject (e.g., use of alcohol, tobacco, or caffeine).
- The Events class captures planned protocol milestones such as randomization and study completion, and occurrences, conditions, or incidents independent of planned study evaluations occurring during the trial (e.g., adverse events) or prior to the trial (e.g., medical history).
- The Findings class captures the observations resulting from planned evaluations to address specific tests or questions (e.g., laboratory tests, ECG testing, questions listed on questionnaires).
In most cases, the choice of observation class appropriate to a specific collection of data can be easily determined according to these descriptions. The majority of data, which typically consists of measurements or responses to questions, usually at specific visits or time points, will fit the Findings general observation class. Additional guidance on choosing the appropriate general observation class is provided in Section 8.6.1, Guidelines for Determining the General Observation Class.
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- included
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Datasets Other than General Observation Class Domains - SDTMIG v3.4 - Wiki (cdisc.org)
The SDTM includes 4 types of datasets other than those based on the general observation classes:
- Domain datasets with subject-level data that do not conform to 1 of the 3 general observation classes. These include Demographics (DM), Comments (CO), Subject Elements (SE), and Subject Visits (SV), and are described in Section 5, Models for Special-purpose Domains.
- Trial Design Model (TDM) datasets, which represent information about the study design but do not contain subject data. These include datasets such as Trial Arms (TA) and Trial Elements (TE) and are described
- in
- the
- Study Reference datasets include Device Identifiers (DI) and Non-host Organism Identifiers (OI). These provide structures for representing study-specific terminology used in subject data. These are described in Section 9, Study References.
Datasets and Domains - SENDIG v3.1.1 - Wiki (cdisc.org)
When determining which general-observation class domain model is appropriate for reporting specific observations, refer to the domain definition included in the Assumptions section for each domain model (see Section 6, Domain Models Based on the General Observation Classes).
- SDTM
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The SDTM lists only the name, label, and type, with a set of brief CDISC guidelines that provide a general description for each variable.
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- dataset
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In general, all domains based on the 3 general observation classes should have at least 1 timing variable. In the events
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- .
Custom domain specifications and, by extension, custom datasets should be designed in the following steps:
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