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Certificate Program

CDISC SDTM Data Development

Program Objective

This course provides a structured and practical approach to understanding and developing CDISC-compliant SDTM datasets for clinical trials. Learners will gain expertise in clinical data standards, SDTM structure, mapping specifications, controlled terminology, and validation tools. The program focuses on hands-on learning using sample clinical datasets, guiding participants through the process of transforming raw clinical data into regulatory-ready SDTM datasets required for submissions to agencies like the FDA and EMA.

Mode: Online (Instructor-led)
Duration: 36 Hours (6 Weeks, 6 hours per week)
Certificate: Issued by IDDCR Global Institute

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Designed For

  • Clinical Data Managers and Programmers

  • Clinical Research Associates (CRAs) and Clinical Operations Professionals

  • Biostatisticians and Statistical Programmers

  • Life Sciences, Pharmacy, Biotechnology, and Medical Graduates

  • Professionals transitioning into regulatory data standards and clinical data science

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Pre-requisite

  • Basic understanding of clinical trials and data management

  • Familiarity with clinical data (e.g., CRFs, databases)

  • Some exposure to data programming (SAS/R) is helpful but not mandatory

  • Computer literacy and attention to data structure and integrity

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Learning Delivery

  • Live virtual sessions OR LMS-based video content

  • Weekly practical exercises with real-time feedback

  • Downloadable mapping specs, datasets, and guides

  • Interactive discussion forum and instructor support

  • Final project submission and feedback

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Tools & Technologies Used

  • CDISC SDTM Implementation Guide (IG v3.2 or latest)

  • Pinnacle 21 Community for validation

  • Mock CRFs, raw datasets (CSV/XLS), and mapping specs

  • (Optional) SAS or R for dataset development

  • Define-XML creation tools (e.g., Define-XML Editor, OpenCDISC Define.xml Generator)

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Assessment & Certification

  • Module-wise quizzes and practical mapping assignments

  • Capstone project with evaluation criteria

  • Minimum 70% score required for certification

  • Certificate of Completion issued by IDDCR Global Institute

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Learning Outcome

By the end of the course, learners will be able to:

 

  • Understand and apply CDISC SDTM standards in clinical trials

  • Develop SDTM-compliant datasets from raw clinical data

  • Work with controlled terminology and metadata

  • Validate datasets using Pinnacle 21 and prepare submission-ready packages

  • Create Define.XML and reviewer-friendly documentation

SDTM is not optional—it's essential.

SDTM is not optional—it’s essential for anyone working in clinical data science. As a globally accepted standard by regulatory authorities like the FDA and EMA, SDTM ensures that clinical trial data is structured, validated, and ready for submission. Mastering SDTM allows professionals to transform raw data into compliant, reviewable datasets—critical for drug approvals. It’s a must-have skill that bridges the gap between data management, regulatory compliance, and impactful contributions to global healthcare outcomes.

Course Contents

Module 1: Introduction to CDISC & SDTM Standards

  • Overview of CDISC, FDA mandates, and global regulatory landscape

  • Relationship between CDASH, SDTM, and ADaM

  • SDTM development lifecycle

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Module 2: SDTM Fundamentals & Domain Architecture

  • SDTM domains overview: General Observation Classes

  • Structure of Findings, Events, and Interventions domains

  • Variables, variable roles, and controlled terminology

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Module 3: Understanding Metadata & Define.XML

  • Role of metadata in SDTM

  • Review of SDTM IG (Implementation Guide) and Annotated CRFs

  • Introduction to Define.XML and its structure

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Module 4: Raw Data to SDTM Mapping

  • Mapping specifications development process

  • Variable derivation and value-level metadata

  • Dataset structure, label, length, and coding logic

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Module 5: Core SDTM Domains

  • DM (Demographics), AE (Adverse Events), EX (Exposure), MH (Medical History)

  • LB (Laboratory Data), VS (Vital Signs), CM (Concomitant Medications), DS (Disposition)

  • Examples of mapping and variable derivations

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Module 6: Controlled Terminology and Validation

  • CDISC Controlled Terminology (CT) usage and tools

  • FDA/PMDA validation expectations

  • Validation using Pinnacle 21 Community and understanding issues

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Module 7: SDTM Dataset Programming (Hands-on)

  • Hands-on mapping using mock data

  • Creating SDTM datasets in .xpt format

  • Common programming issues and best practices

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Module 8: Quality Control, Review, and Submission Readiness

  • Review and QC of SDTM datasets

  • Review of Define.XML, Reviewer's Guide, and submission package

  • Preparing for regulatory submission

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Module 9: Project

  • Develop SDTM datasets from mock CRF and raw data

  • Create Define.XML and validate using Pinnacle 21

  • Project evaluation and feedback session

Fact

High Demand for SDTM Expertise

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Pharmaceutical companies and CROs actively hire professionals skilled in SDTM mapping and validation for clinical data management and statistical programming roles.

Start Your SDTM Learning Journey

Advance Your Clinical Data Career – SDTM Program Applications Open

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