SDTM






SDTM provides a standard for organizing and formatting data to streamline processes in collection, management, analysis and reporting. Implementing SDTM supports data aggregation and warehousing; fosters mining and reuse; facilitates sharing; helps perform due diligence and other important data review activities; and improves the regulatory review and approval process. SDTM is also used in non-clinical data (SEND), medical devices and pharmacogenomics/genetics studies.



Statement:

For domains based on a general observation class, determining the SDTM class is the most important modeling decision point. Identifying the appropriate domain is dependent on understanding the general observation class.

Rationale:

The SDTM is a metadata model and SDTMIG domains classified as Interventions, Events, Findings, or Findings About are instantiations of an SDTM general observation class.

Key Benefits:
  • Consistent representation of concepts in all domains in the same general observation class
    • Users who become familiar with the SDTM root variable definitions understand a variable's meaning in SDTMIG domains.
    • SDTMIG domains based on the same SDTM general observation class can be combined to look across topics (e.g., Medical History, Adverse Events, Clinical Events).
  • Data repositories based on the conceptual model support warehousing standard and custom domains.
  • Efficient creation of new or custom domains based on an SDTM general observation class
  • Data represented in a custom domain can be easily migrated to a newly published domain of the same general observation class.
Statement:

Every variable must have a clear definition to achieve structural standardization.

Rationale:

To be effective, concept definitions must not be ambiguous.

Key Benefits:
  • A stakeholder who becomes familiar with the SDTM root variables and their definitions should understand their meaning in all IG domains.
    • Implementers of IG domains know which variables to use.
    • Users of IG domains know where to find data.
  • Users of standardized study data should be able to find data without having to understand study-specific data collections or conventions.
Statement:

Every data element (i.e., clinical study data element, nonclinical endpoint) should have a clear definition to achieve semantic standardization.

  • A clinical study data element is a single observation associated with a subject in a clinical study. A data element in an eCRF represents the smallest unit of observation captured for a subject in a clinical investigation (C142437). Examples include birth date, white blood cell count, pain severity measure, and other clinical observations made and documented during a study.
  • A nonclinical endpoint is a defined variable intended to reflect an outcome of interest that is analyzed to address a particular research question.
Rationale:

To be effective, concept definitions must not be ambiguous.

Key Benefits:
  • A stakeholder who becomes familiar with CDISC Controlled Terminology should understand the meaning of a value within a record.
    • Implementers of IG domains know what values to represent.
    • Users of IG domains know what values they will find in the data.
Statement:

A defined concept (i.e., clinical study data element, nonclinical endpoint) should be represented in the same domain.

Rationale:

Consistency and predictability in the data representation aid in both the development and the review process.

Key Benefits:
  • Data are easy to find using SDTMIG domain definitions, assumptions, and examples.
  • Users of standardized study data should be able to find data without having to understand study-specific data collections or conventions.
Statement:

SDTM domains represent collected or received data that have been standardized to facilitate review and reporting. Standardization must not change the original meaning of the data.

Rationale:

Review is easier and more meaningful when data are in standardized format.

Key Benefits:
  • Facilitates comparison of data collected in different formats
  • Supports simple analyses using SDTM datasets
Statement:

Be mindful of the impact of modeling changes to the user community. Minimize unnecessary or unproductive changes.

Rationale:

Change is costly and disruptive for end users, though some changes are necessary to correct an error/problem or to evolve the standard.

Key Benefits:
  • Supports design stability and usefulness