SDTM DM Domain Creation Project

Overview

This project demonstrates the creation of the SDTM DM (Demographics) Domain following CDISC SDTM standards used in clinical trial data submissions. The DM dataset serves as the parent domain for all subject-level clinical trial data and contains essential demographic and study participation information for each subject.

The project includes data mapping, derivations, standardization, validation checks, and generation of a submission-ready DM dataset using SAS programming.

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Project Objectives

• Create a CDISC-compliant SDTM DM dataset.
• Perform raw-to-SDTM mapping.
• Derive standard DM variables according to SDTM guidelines.
• Apply controlled terminology and standard formats.
• Generate one record per subject.
• Prepare data suitable for regulatory submissions and downstream ADaM development.

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Technologies Used

• SAS Base
• SAS SQL
• CDISC SDTM
• Clinical Trial Data Standards
• Data Validation Techniques

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DM Variables Implemented

Identifier Variables

• STUDYID
• DOMAIN
• USUBJID
• SUBJID
• SITEID

Demographic Variables

• SEX
• AGE
• AGEU
• RACE
• ETHNIC
• COUNTRY
• BRTHDTC

Study Reference Dates

• RFSTDTC
• RFENDTC

Treatment Variables

• ARM
• ARMCD

Project Workflow

1. Create raw clinical data.
2. Perform data cleaning and standardization.
3. Derive subject-level identifiers.
4. Convert dates into ISO 8601 format.
5. Create demographic variables.
6. Derive treatment assignment variables.
7. Generate study reference dates.
8. Perform quality control checks.
9. Create final SDTM DM dataset.

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Sample Derivations

USUBJID

USUBJID = catx("-", STUDYID, SITEID, SUBJID);

AGE

AGE = int((RFSTDTC - BRTHDTC)/365.25);

Key SDTM Concepts Demonstrated

• Special Purpose Domains
• Subject-Level Data Structure
• One Record Per Subject
• Controlled Terminology
• ISO 8601 Date Standards
• Variable Derivations
• Traceability
• Regulatory Submission Standards

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Learning Outcomes

Through this project, I gained practical experience in:

• SDTM implementation
• Clinical SAS programming
• CDISC standards
• Dataset mapping
• Variable derivations
• Clinical trial data processing
• Regulatory data preparation
• Quality control and validation

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References

• CDISC SDTM Standards
• CDISC SDTM Implementation Guide

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Author

Shreya Mishra

Aspiring Clinical SAS Programmer | SDTM & ADaM Enthusiast | Clinical Data Standards Learner

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Project Status

Completed ✓

This project was developed for hands-on practice of SDTM implementation, clinical data standardization, and clinical SAS programming concepts commonly used in pharmaceutical and CRO environments.