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Domain specifications are instructions for implementing the CDISC SDTM to build datasets for representation of collected, assigned, or derived data. Domain specifications for the TIG are provided in the following sections:

All domain specifications begin with the expected name and record structure for the resulting dataset. A domain specification table follows 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 a domain specification table reflects the expected order of variables in the resulting dataset.


Metadata tables are instructions for implementing the CDASH model to develop CRFs and the underlying database or other data collection structure. Metadata tables for the TIG are provided in:

Metadata tables are organized with one table per domain and are structured to present one or more rows for each data collection field with columns to describe attributes for each field. More than one row will be present in the metadata table when there is more than one data collection scenario for a data collection field. When more than one option is applicable in a single cell, semicolons are used to separate descreet options.

Each column in a metadata table provides guidance which focuses one or more of following aspects of data collection. 

  • CRF and System Design to guide development of the CRF(s) and the underlying database structure in conformance with the standard.
  • Tabulation Dataset Generation to guide development of programs to generate tabulation datasets from compliant data. link between CDASH and SDTM
  • Data Collection Field Implementation to guide use of fields and facilitate creation of study-level documentation to support expected field completion.

The columns in a metadata table are: 








Metadata tables are organized by domain - specification naming



The columns in a domain specification table are: use the in the spec

ColumnPurpose/ description??? 
Variable NameSpecifies the name of the variable in the resulting dataset.
Variable LabelSpecifies 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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