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Certificate Program

CDISC ADaM Data Development

Program Objective

This certificate course is designed to equip learners with a practical understanding of CDISC ADaM (Analysis Data Model) standards used for statistical analysis in clinical trials. Participants will learn how to derive ADaM datasets from SDTM data, apply traceability, create analysis-ready structures, and generate regulatory-compliant outputs. Through hands-on exercises and real-world case studies, learners will gain the skills required to support biostatistics and regulatory submissions using validated ADaM datasets.

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 SAS Programmers and Biostatisticians

  • Clinical Data Scientists and CDISC Specialists

  • Clinical Research Professionals transitioning into analysis programming

  • Life Sciences, Pharmacy, Biotechnology, and Statistics graduates

  • Professionals involved in regulatory submission data workflows

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Pre-requisite

  • Basic understanding of clinical trials and clinical data management

  • Working knowledge of SDTM datasets (recommended)

  • Familiarity with statistics and statistical programming (SAS/R) is helpful

  • Computer literacy and basic data handling skills

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Learning Delivery

  • Live interactive sessions or pre-recorded LMS modules

  • Weekly assignments and hands-on activities

  • Downloadable datasets, derivation guides, and templates

  • Access to Q&A forum and instructor support

  • Project evaluation with individual feedback

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Tools & Technologies Used

  • CDISC ADaM Implementation Guide (IG v1.2 or latest)

  • Pinnacle 21 Community for validation

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

  • SDTM and raw datasets (mock trial data)

  • SAS (preferred) or R for dataset derivation and processing

  • ADRG templates and sample documentation

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Assessment & Certification

  • Module-wise quizzes and derivation exercises

  • Final capstone project evaluation

  • Minimum 70% score required for successful completion

  • Certificate of Completion issued by IDDCR Global Institute

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Learning Outcome

By the end of the course, learners will:

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  • Understand ADaM standards and their role in regulatory submissions

  • Derive analysis-ready ADSL and BDS datasets from SDTM

  • Ensure traceability and compliance using ADaM metadata

  • Generate Define.XML and ADRG for submission readiness

  • Validate ADaM datasets using industry-standard tools

Turn Clinical Data into Trusted Evidence with ADaM

  • Regulatory Submission Standard
    ADaM is required by global regulators (FDA, EMA, PMDA) for submitting statistical analysis datasets. Without ADaM-compliant datasets, a clinical trial cannot proceed to review or approval.

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  • Foundation for Statistical Analysis
    ADaM datasets are analysis-ready, traceable, and structured for efficient generation of TLFs (Tables, Listings, Figures). They reduce errors, ensure consistency, and speed up statistical programming.

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  • Critical Skill for Biostatistics & Programming Roles
    Proficiency in ADaM is a must-have for clinical SAS programmers, biostatisticians, and regulatory data specialists. It shows you're qualified to support advanced statistical reporting.

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  • Career Differentiator in CDISC Workflow
    Understanding ADaM makes you a complete CDISC professional, complementing your knowledge of SDTM, Define.XML, and submission readiness. It enhances your value in CROs, pharma, and global trials.

Course Contents

Module 1: Introduction to ADaM and Regulatory Context

  • Overview of CDISC ADaM and its role in clinical trials

  • Regulatory guidance and FDA/EMA expectations

  • Differences between SDTM and ADaM

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Module 2: ADaM Principles and Standards

  • Fundamental principles: traceability, analysis-readiness, metadata

  • Types of ADaM datasets: ADSL, BDS, OCCDS

  • Role of metadata and define.xml in ADaM

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Module 3: Building ADSL (Subject-Level Analysis Dataset)

  • Variables included in ADSL

  • Deriving population flags, treatment variables, dates

  • Mapping from SDTM to ADSL

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Module 4: BDS (Basic Data Structure) Datasets

  • Introduction to BDS structure

  • Creating ADaE, ADLB, ADVS datasets

  • Deriving analysis variables, flags, and parameters

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Module 5: OCCDS and Other ADaM Structures

  • OCCDS (Occurrence Data Structure) overview

  • Examples: ADAE, ADCM, ADMH

  • Time-to-event and repeated measures handling

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Module 6: Derivations and Traceability

  • Best practices for derivation logic

  • Maintaining traceability from SDTM to ADaM

  • Documenting derivations and source mapping

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Module 7: Define.XML and Reviewer Documentation

  • Introduction to Define-XML for ADaM

  • Analysis Data Reviewer’s Guide (ADRG)

  • Preparing a submission-ready data package

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Module 8: Validation and Compliance Checks

  • Pinnacle 21 validation for ADaM datasets

  • Common errors and how to resolve them

  • Quality control and audit readiness

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Module 9: Capstone Project

  • Create ADSL and BDS datasets from sample SDTM data

  • Generate define.xml and ADRG

  • Validate datasets and prepare submission package

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Critical Skill for SAS Programmers & Biostatisticians

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Professionals trained in ADaM are in high demand by CROs and pharmaceutical companies for clinical programming and biostatistics roles.

Master ADaM Standards

Become ADaM-Ready for Submission Trials

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