CDISC/ADM

Course Overview

This course provides a detailed understanding of the CDISC Analysis Data Model (ADaM) — the standard used for creating analysis-ready datasets that bridge SDTM data and statistical outputs. Participants will learn the principles of ADaM dataset design, variable derivation, metadata documentation, and validation workflows essential for regulatory submission.

Learning Objectives

• Understand the CDISC data flow from SDTM to ADaM and its role in statistical analysis.

• Learn the key ADaM dataset structures: ADSL, BDS, and OCCDS.

• Develop ADaM datasets from SDTM using SAS programming.

• Document metadata and define derivations in Define.xml.

• Ensure ADaM dataset traceability and regulatory compliance.

• Validate ADaM datasets using Pinnacle 21 and CDISC rules.

Course Modules

Module 1: Overview of CDISC ADaM

• ADaM objectives and its place in the CDISC standard flow

• Differences between ADaM and SDTM

• Introduction to ADaM Implementation Guide (ADaM IG)

Module 2: ADaM Dataset Structures

• ADSL (Subject-Level Analysis Dataset)

• BDS (Basic Data Structure) — ADVS, ADLB, ADQS, etc.

• OCCDS (Occurrence Data Structure) — ADAE, ADCM

Module 3: ADaM Metadata and Traceability

• Defining metadata and variable derivations

• Linking SDTM → ADaM → TFL

• Documenting derivations in Define.xml

Module 4: Hands-on Programming Practice

• Using SAS to derive ADaM datasets

• Example: Generating ADSL from SDTM.DM, EX, and AE

• Creating BDS datasets (ADLB, ADVS, ADQS)

• Implementing parameter and visit derivations

Module 5: Validation and Regulatory Submission

• ADaM validation principles

• Using Pinnacle 21 for compliance check

• Common ADaM findings and error resolution

Key Features

• 💻 Real-world SAS programming practice

• 📘 Alignment with latest ADaM IG and CDISC standards

• 🧠 Focus on traceability and submission readiness

• ✅ Validation workflow demonstration using Pinnacle 21

Who Should Attend

• Clinical data programmers familiar with SDTM

• Statistical programmers aiming to create analysis datasets

• Graduate students in Biostatistics or Data Science

• Professionals involved in regulatory submissions

Duration

Approx. 12 hours (including coding practice and validation exercises)

What You’ll Gain

After completing this course, you will be able to independently design and create ADaM datasets, ensure data traceability, and support statistical analyses in regulatory submissions. You will gain the confidence to deliver compliant and analysis-ready data packages for FDA and global health authority review.