Within the Clinical Data Interchange Standards Consortium (CDISC) Study Data Tabulation Model (SDTM), standard domains are split into four main types: special purpose, relationships, trial design and general observation classes. General observation classes cover the majority of observations collected during a study and can be divided among three general classes:
- 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”), coincident with the study assessment period (e.g., “concomitant medications”), or other substances self-administered by the subject (such as alcohol, tobacco, or caffeine).
- The Events class captures planned protocol milestones such as randomisation 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 such as laboratory tests, ECG testing, and questions listed on questionnaires. The class contains a specialisation called Findings About Events or Interventions where additional information about and event or intervention that cannot be include in the parent domain can be collected. An example of a domain containing findings about an intervention is that for skin response.
When creating a custom domain, one should first confirm that there are no published domains available into which the data can be mapped. This can be done by checking against the reserved domain codes listed in the appendices of the current SDTM Implementation Guide or by looking through a relevant Therapeutic Area User Guide if one is available for the indication under investigation. The following is not acceptable when creating custom domains:
- The nature of the data is the same as in another published domain.
- The custom domain is being created due to separation based on time.
- The data have been collected or are going to be used for different reasons. For example, if a lab parameter is collected for efficacy purposes the data must be represented in the LB domain and not in a custom ‘efficacy’ domain. The same applies to pharmacodynamics data that need to be PC and PP because the information comes from measurements of plasma serum.
- Data that were collected on separate CRF modules or pages and together may fit into an existing domain.
- It is necessary to represent relationships between data that are hierarchical in nature. Here, RELREC can be used instead.
Once it is confirmed that the data does not fit with any published domains, it should be determined which of the three general observation classes best fits the topic of the data since the custom domain must fit in to one of these. The next step is to determine a two-letter domain code for the custom domain. This should not be the same as the code for any published or planned domain. The domain codes X-, Y- and Z- are reserved for sponsor use, where the hyphen may be replaced by any letter or number. This domain code then will be the name of the domain and will also be used to replace all prefixes of variables from the class upon which it is based. The following steps can then be followed to create the custom domain:
- Select and include the required Identifier variables (STUDYID, DOMAIN, USUBJID and --SEQ) and any permissible Identifier variables (--GRPID, --REFID and --SPID).
- Include the Topic variable from the identified general observation class (--TRT for interventions, --TERM for events and --TESTCD for Findings).
- Select and include the relevant Qualifier variables from the identified general observation class only. These can be found in sections 2.2.1, 2.2.2 and 2.2.3 of the Study Data Tabulation Model document.
- Select and include the applicable Timing variables. These can be found in Section 2.2.5 of the Study Data Tabulation Model document and relate to all general observation classes.
- Set the order of the variables within the domain: identifiers must be followed by topic variables, qualifiers and finally timing variables. The variables must then be ordered within these roles to match the order of variables given in sections 2.2.1, 2.2.2, 2.2.3, 2.2.4 and 2.2.5 of the Study Data Tabulation Model document. The variable order in the corresponding Define-xml data definitions file must also match the order within the domain.
- 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.
- Ensure that appropriate standard variables are being properly specified by comparing the use of variables in standard domains.
- Ensure that there are no sponsor-defined variables added to the domain. Any sponsor-defined variables should be in the corresponding Supplemental Qualifier dataset.
Variable attributes within the domain and Supplemental Qualifier datasets must conform to the SAS Version 5 transport file conventions. For example, variable names must be no longer than 8 characters, variables labels must be no longer than 40 characters and data value lengths must be no longer than 200 characters. Also, the transport file for any SDTM dataset should not exceed 5 GB in size or domains may need splitting to fulfil this requirement and the split documented in the Data Reviewer’s Guide that accompanies the submission.
- Study Data Tabulation Model, Version 1.4; CDISC Submission Data Standards Team.
- Study Data Tabulation Model Implementation Guide: Human Clinical Trials, Version 3.2; CDISC Submission Data Standards Team.
- Providing Regulatory Submissions In Electronic Format - Standardized Study Data; FDA
- Study Data Technical Conformance Guide; FDA
Authors note: This blog was originally published on 21/07/2011 and has since been updated.
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