How to Use Domain Specifications

Specifies the data type of the variable. Values for in this column are:

• Num for numeric data
• Char for character or alphanumeric data

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.

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.
• 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.

The set of qualifier variables can be further categorized into 5 subclasses:

• Grouping qualifiers are used to group together a collection of observations within the same domain. Examples include --CAT and --SCAT.
• Result qualifiers describe the specific results associated with the topic variable in a Findings dataset. They answer the question raised by the topic variable. Result qualifiers include --ORRES, --STRESC, and --STRESN.
• Synonym qualifiers specify an alternative name for a particular variable in an observation. Examples include --MODIFY and --DECOD, which are equivalent terms for a --TRT or --TERM topic variable, and --TEST, which is an equivalent term for a --TESTCD.
• Record qualifiers define additional attributes of the observation record as a whole (rather than describing a particular variable within a record). Examples include AGE, SEX, SPECIES, and STRAIN in the Demographics (DM) domain and --REASND, --BLFL, --LOC, --SPEC, and --NAM in a Findings domain.
• Variable qualifiers 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. Examples include --ORRESU, --ORNRHI, and --ORNRLO, all of which are variable qualifiers of --ORRES, and --DOSU, which is a variable qualifier of --DOSE.

SDTM

The SDTMIG for Human Clinical Trials is based on the SDTM’s general framework for organizing clinical trial information that is to be submitted to regulatory authorities. The SDTM is built around the concept of observations collected about subjects who participated in a clinical study. Each observation can be described by a series of variables, corresponding to a row in a dataset. Each variable can be classified according to its role. A role determines the type of information conveyed by the variable about each distinct observation and how it can be used. Variables can be classified into 5 major roles: 

• Identifier variables, such as those that identify the study, subject, domain, and sequence number of the record
• Topic variables, which specify the focus of the observation (e.g., the name of a lab test)
• Timing variables, which describe the timing of the observation (e.g., start date and end date)
• Qualifier variables, which include additional illustrative text or numeric values that describe the results or additional traits of the observation (e.g., units, descriptive adjectives)
• Rule variables, which describe the condition to start, end, branch, or loop in the Trial Design Model

The set of Qualifier variables can be further categorized into 5 subclasses:

• Grouping Qualifiers are used to group together a collection of observations within the same domain. Examples include --CAT and --SCAT.
• Result Qualifiers describe the specific results associated with the topic variable in a Findings dataset. They answer the question raised by the topic variable. Result Qualifiers are --ORRES, --STRESC, and --STRESN.
• Synonym Qualifiers specify an alternative name for a particular variable in an observation. Examples include ‑‑MODIFY and --DECOD, which are equivalent terms for a --TRT or --TERM topic variable, and --TEST and ‑‑LOINC, which are equivalent terms for a --TESTCD.
• Record Qualifiers define additional attributes of the observation record as a whole (rather than describing a particular variable within a record). Examples include --REASND, AESLIFE, and all other serious adverse event (SAE) flag variables in the AE domain; AGE, SEX, and RACE in the DM domain; and --BLFL, --POS, --LOC, --SPEC and --NAM in a Findings domain
• Variable Qualifiers 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. Examples include --ORRESU, --ORNRHI, and ‑‑ORNRLO, all of which are Variable Qualifiers of --ORRES; and --DOSU, which is a Variable Qualifier of ‑‑DOSE.

For example, in the observation, "Subject 101 had mild nausea starting on study day 6," the Topic variable value is the term for the adverse event, "NAUSEA". The Identifier variable is the subject identifier, "101". The Timing variable is the study day of the start of the event, which captures the information, "starting on study day 6," whereas an example of a Record Qualifier is the severity, the value for which is "MILD". Additional Timing and Qualifier variables could be included to provide the necessary detail to adequately describe an observation.

Provides additional context for the intended 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 be populated in the variable. Such values are intended to support understanding of the variable 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.  

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, even if all collected values are null, or derived.