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Domain specifications guide implementation of the CDISC SDTM to build datasets for representation of collected or derived data. TIG domain specifications are provided in the following sections:
All domain specifications begin with the name of the domain and the expected record structure for the resulting dataset. A domain specification table follows the domain name and expected record structure to describe the domain's variables and their attributes. Assumptions specific to the domain are also provided to further guide implementation.
Domain specification tables are structured to present one row for each variable with columns to describe expected attributes of the variable in resulting datasets. The order of variables in the domain specification table reflects the expected order of variables in the resulting dataset. The columns in a domain specification table are:
| Specification Table Column | Purpose |
|---|
| Variable Name | Specifies the name of the variable in the resulting dataset. |
| Variable Label | Specifies a descriptive label for the variable. |
| Type | Specifies the data type of the variable. Values for in this column are: - Num for numeric data
- Char for character or alphanumeric data
|
| Controlled Terms, Codelist, or Format | Specifies any controlled terminology or formats applicable to the variable. Values in this column are: - A single, required controlled term for the variable represented by the controlled term in double quotes. This value indicates the controlled term must be populated in the variable for all records in the resulting dataset.
- Names of controlled terminology codelists with allowable terms to populate the variable. Codelist names are specified in paratheses. Multiple codelist names indicate the variable is subject to one or more of the codelists.
- Names of formats to be applied to values in the variable.
|
| Role | Specifies the role of the variable in the resulting dataset including information conveyed by the variable in the context of a data record and how the variable can be used. Values in this column are: - Identifier for variables which identify the study, subject, domain, pool identifier, and sequence number of the record.
- Topic for variables which specify the focus of the data record.
- Grouping Qualifier for variables which are used to group together a collection of observations within the same domain.
- Result Qualifier for variables which describe the specific results associated with the topic variable in a Findings dataset.
- Synonym Qualifier for variables which specify an alternative name for a particular variable in an observation.
- Record Qualifier for variables which define additional attributes of the observation record as a whole, rather than describing a particular variable within a record
- Variable qualifiers for variables which are used to further modify or describe a specific variable within an observation and are only meaningful in the context of the variable they qualify.
- Rule variables express an algorithm or executable method to define start, end, and branching or looping conditions in the Trial Design Model datasets.
- Timing for variables which describe the timing of the observation.
|
| CDISC Notes | Provides additional context for the intended use of the variable and may include: - A description of the purpose of the variable and/or what the variable means.
- Guidelines for variable use including rules for when or how the variable should be populated, or how the contents should be formatted.
- Example values which could appear in the variable. Such values are intended to support understanding and are not intended to influence decisions regarding data to collect and subsequently represent in the variable. For guidance on the selection of data to collect, please refer to the appropriate regulatory authority.
|
| Core | Specifies expectations for inclusion of the variable in the resulting dataset. Values in this column are: - Req for variables which are Required and must be included in the resulting dataset and cannot be null for any record. Such variables are basic to the identification of a data record or are necessary to make the record meaningful.
- Exp for variables which are Expected to be included in the resulting dataset, even if all values are null. Such variables are considered necessary to make the data record useful in the context of the domain.
- Perm for variables for which it is Permissible to include or exclude the variable from the resulting dataset. Permissible variables must be included in the resulting dataset when data appropriate for the variable have been collected or derived, even if all values are null.
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