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Mapping data to the SDTM (Study Data Tabulation Model) DM (Demographics) domain is an important step in clinical data management that entails converting raw demographic data acquired during a clinical trial into a standardised SDTM format. The following are some quick remarks on the SDTM DM domain mapping process:

  1. DM Domain Function:

SDTM’s DM domain is intended to collect demographic information about research participants.

It contains information such as subject IDs, gender, age, race, and other variables that serve to characterise the research population.

  • Source of the data:

Data for the DM domain are frequently derived via case report forms (CRFs), electronic health records (EHRs), or other data gathering methods employed during the clinical study.

  • Mapping Procedure:

Mapping entails finding and specifying the source data variables that correlate to the SDTM DM domain variables.

SAS programmers and data managers collaborate to develop a mapping strategy that outlines how each source variable will be translated to the relevant DM domain variables.

  • Important Variables in the DM Domain:

STUDYID: This is the clinical trial’s unique identification.

DOMAIN: The domain name, which is “DM” for Demographics, is specified.

USUBJID: The study’s unique subject identifier.

SEX: The gender of the participant (e.g., Male, Female).

AGE: The participant’s age at a certain moment in time.

RACE: The racial or ethnic background of the participant.

ETHNICITY: The ethnicity of the participant.

BRTHDTC: The participant’s date of birth.

COUNTRY: The participant’s country of residence or nationality.

  • Compliance with SDTM Standards:

Mapping to the DM domain must follow CDISC (Clinical Data Interchange requirements Consortium) SDTM requirements.

SDTM provides specified variables, variable kinds, and formats for the DM domain, and mapping must verify compliance with these standards.

  • Transformation of data:

Following the creation of the mapping plan, SAS programming or other data transformation tools are used to turn the source data into SDTM-compliant DM datasets.

This frequently entails establishing new variables or altering existing ones to conform to SDTM standards.

  • Quality assurance and validation:

Following mapping and transformation, the SDTM DM datasets are subjected to stringent quality control procedures to guarantee correctness and consistency.

Validation tests are performed to ensure that the data complies with CDISC standards and is suitable for analysis and submission.

  • Documentation:

The mapping process must be documented for openness and regulatory compliance.

The mapping design, data translation logic, and any deviations or clarifications made during the process are all detailed documented.

  • Iterative Methodology:

When dealing with complicated or huge datasets, DM domain mapping may be an iterative procedure.

Responses from regulatory authorities or stakeholders may need changes to the mapping and transformation process.