ADaM Bundle (Modules 1-10)

Modules:

• ADAM001: Introduction to ADaM

• ADAM002: ADaM Traceability, Rules and Best Practices

• ADAM003: Design Concepts in ADaM

• ADAM004: Subject Level Analysis Dataset

• ADAM005: Introduction to the Basic Data Structure

• ADAM006: BDS Concepts

• ADAM007: BDS Concepts, Rules and Illustration

• ADAM008: Occurrence Data Structure - OCCDS​

• ADAM009: ADaM Compliance and Metadata Introduction​

• ADAM010: ADaM Metadata​

ADaM Module 1

This module covers the goals and mission, and introduces the fundamental principles of the Analysis Data Model, ADaM.

Learning Outcomes:

• Describe the goals and mission of the ADaM standard

• Explain how ADaM helps FDA reviewers

• Describe the fundamental principles of ADaM

• Successfully locate the ADaM documentation

Language: English | Video Duration: 00:37:27 | CEUs: 0.1

ADaM Module 2

This module defines and illustrates the concept of traceability, and presents the ADaM rules and best practices for creating variables and datasets.

Learning Outcomes:

• Discuss the definition and methods of creating traceability in ADaM datasets

• Understand the rules and best practices for naming variables and datasets in ADaM

• Recognize best practices for determining which ADaM datasets should be created and included in a submission

Language: English | Video Duration: 00:44:30 | CEUs: 0.1

ADaM Module 3

This module describes the treatment concepts from an analysis perspective with an emphasis on the types and timing of treatments.

Learning Outcomes:

• Explain how ADaM describes the treatment concepts from an analysis perspective.

• Identify the treatment and the timing involved in ADaM datasets.

• Describe how periods of time are defined for the desired analyses, including the appropriate treatment and analysis periods and analysis phases.

Language: English | Duration: 00:52:20 | CEUs: 0.1

ADaM Module 4

This module discusses the ADaM data structure Subject Level Structure and how the dataset is used to provide a source for denominators for populations of interest, stratification variables, and other important subject subgroups.

Learning Outcomes:

• describe the three defined classes of ADaM data structures

• compare the Subject Level, Basic Data and Occurrence ADaM data structures.

Language: English | Video Duration: 00:34:22 | CEUs: 0.1

ADaM Module 5

This module discusses the Basic Data Structure or B-D-S, which is designed with the majority of analyses in mind. The structure is one or more records per subject per analysis parameter, per analysis time point, if applicable.

Learning Outcomes:

• Apply the principles of the Basic Data Structure to ADaM data sets.

• Summarize the concepts of parameter and the associated value in ADaM Basic Data Structure.

Language: English | Video Duration: 00:39:49 | CEUs: 0.1"

ADaM Module 6

This module discusses why we have analysis concepts instead of using SDTM variables.

Learning Outcomes:

• Explain how ADaM supports a specific set of analyses and generally includes both SDTM and derived information

• Recognize situations when new records should be created when developing ADaM datasets

• Correctly identify when an indicator variables is required when developing ADaM datasets

Language: English | Video Duration: 00:46:24 | CEUs: 0.1

ADaM Module 7

This module describes key BDS elements that support traceability in data analysis and summarizes BDS rules. This module also outlines best practices for analyzing and creating time to events of interest in clinical studies.

Learning Outcomes:

• Accurately describe key BDS elements in order to support traceability in data analysis.

• Correctly apply the BDS rules outlined in the training when creating ADaM datasets.

• Effectively analyze and calculate time to events of interest in clinical studies.

Language: English | Video Duration: 01:04:50 | CEUs: 0.1

ADaM Module 8

This module describes the Occurrence Data Structure (OCCDS) and compares it to the ADSL and BDS structures. The module uses examples to illustrate how learners can identify and employ Occurrence Data variables to build OCCDS datasets.

Learning Outcomes:

• Correctly identify required and permissible OCCDS variables in order to build an OCCDS class dataset.​

• Correctly identify SDTM, ADSL, and OCCDS variables and explain how each is used in OCCDS datasets.​

• Design OCCDS datasets that achieve traceability by using data and metadata.

Language: English | Video Duration: 21:02 | CEUs:0.1

ADaM Module 9

This module provides an overview of what is meant by 'ADaM Compliance' and how this concept is assessed. The module also outlines the benefits of both objective and subjective assessments.

Learning Outcomes:

• Understand the difference between subjective and objective compliance​

• Accomplish compliance to the ADaM standard​

• Understand the importance of ADaM compliance

Language: English | Video Duration: 06:36 | CEUs: 0.1"

ADaM Module 10

This module provides a high-level overview of how ADaM metadata benefit communication and consistency among analysis dataset creators. The module describes analysis dataset, variable, parameter value-level and results metadata, and how these metadata can be used along with other materials for submission.

Learning Outcomes:

• accurately identify key concepts related to ADaM metadata;​

• describe other material relevant to submission, in addition to ADaM metadata;​

• successfully navigate through an ADaM define.xml for a given study.

Language: English | Video Duration: 31:23 | CEUs: 0.1