SDTM Theory and Application for Medical Devices

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Course Description: SDTM Theory and Application for Medical Devices (Full Version; abbreviated versions below)

The Study Data Tabulation Model (SDTM) and associated Implementation Guides are intended to guide the organization, structure, and format of standard human, animal and medical device trial tabulation datasets. Tabulation datasets are electronic listings of individual observations from a study that comprise the essential data reported from a study. Where required, they form part of regulatory submissions for new products. They are also a critical part of supporting the interoperability of data from pre-clinical, clinical, device and other related studies, thereby enabling the reuse of data in multiple venues.

This course provides a solid grounding in the principles of SDTM and its application to medical device studies. It covers the SDTM model, the Implementation Guide for Human Clinical Trials (which contains most of the SDTM documentation), and the Implementation Guide for Medical Devices. Examples and activities throughout the course focus on how medical device data can be represented in SDTM. Topics include: 

  • SDTM Model
  • General Rules and Assumptions 
  • Representing Demographics 
  • General Observation Classes
  • Findings About
  • Timing and Grouping Variables
  • Creating Custom Domains and Variables
  • Relationships
  • Trial Design Datasets
  • Device-specific Domains

This is the “Full” SDTM for Medical Devices course, meaning that it covers SDTM generally as well as specifically targeting its use in medical devices. It is intended for people who are unfamiliar with SDTM. When delivered in the classroom, this is a 2 day course. Delivered virtually, it consists of five 3-hour sessions over five days.

Learning Outcomes

At the end of this course, you will be able to:

  • Describe the SDTM model and implementation guide, the relationships between them and their relationships to other CDISC standards
  • Use the SDTM model, implementation guide and controlled terminology to create SDTM-conformant datasets for a study
  • Model custom variables and domains using the model, implementation guide and controlled terminology
Continuing Education Units from 'Full' 2-day Version (CEUs)

Learners will receive 1.4 CEUs for successfully completing this course.

Course Description: SDTM Theory and Application for Medical Devices Used in Humans (Abbreviated 0.5-day Version)

The Study Data Tabulation Model (SDTM) and associated Implementation Guides are intended to guide the organization, structure, and format of standard human, animal and medical device trial tabulation datasets. Tabulation datasets are electronic listings of individual observations from a study that comprise the essential data reported from a study. Where required, they form part of regulatory submissions for new products. They are also a critical part of supporting the interoperability of data from pre-clinical, clinical, device and other related studies, thereby enabling the reuse of data in multiple venues.

This course is intended for individuals who are already familiar with SDTM and wish to learn how to represent device-specific data in SDTM structures. It is based on the SDTM Implementation Guide for Medical Devices, and covers the relationship of the Med Dev IG to the other IGs, the overall assumptions in the IG, and the 7 device domains. Its examples and use cases are drawn from devices used in humans, particularly implantable devices such as pacemakers. The information is applicable to most kinds of devices, with the exception of in vitro diagnostics and other device studies that generally do not included subject data.

This is the “Abbreviated ” SDTM for Medical Devices in Humans course, meaning that it focuses on the SDTM IG for Medical Devices, and does not cover general principles or structures also used in non-device studies. It is intended for people who are familiar with SDTM. When delivered in the classroom, it is a half-day course. Delivered virtually, it consists of one 3.5 hour session.


Course Description: SDTM Theory and Application for Medical Devices in in vitro Diagnostics (Alternate abbreviated 0.5-day Version)

The Study Data Tabulation Model (SDTM) and associated Implementation Guides are intended to guide the organization, structure, and format of standard human, animal and medical device trial tabulation datasets. Tabulation datasets are electronic listings of individual observations from a study that comprise the essential data reported from a study. Where required, they form part of regulatory submissions for new products. They are also a critical part of supporting the interoperability of data from pre-clinical, clinical, device and other related studies, thereby enabling the reuse of data in multiple venues.

This course is intended for individuals who are already familiar with SDTM and wish to learn how to represent device-specific data in SDTM structures. It is based on the SDTM Implementation Guide for Medical Devices, and covers the relationship of the Med Dev IG to the other IGs, the overall assumptions in the IG, and the 7 device domains. Its examples and use cases focus on devices used in studies that typically do not collect human subject data, such as in vitro diagnostics.

This is the “Abbreviated ” SDTM for Medical Devices in IVDs course, meaning that it focuses on the SDTM IG for Medical Devices, and does not cover general principles or structures also used in non-device studies. It is intended for people who are familiar with SDTM. When delivered in the classroom, it is a half-day course. Delivered virtually, it consists of one 3.5 hour session.

Continuing Education Units (CEUs) for either ½-day SDTM for Medical Devices Course

Learners will receive 0.4 CEUs for successfully completing this course.

Successful Course Completion
  1. In order for a learner to successfully complete a course:
    1. An education representative or learner will complete training registration.
    2. A learner must attend course for 80% of total course time. Specific attendance requirements are posted in course information pages.
    3. A learner will complete final course assessment with a score of at least 80% correct.
    4. A learner will complete summative assessment surveys
  2. Remediation: The following requirements will be in effect if learner does not successfully complete all parts of training:
    1. If learner registers for course but fails to complete formative assessment in required time, learner will be notified that they must transfer registration to later date.
    2. If learner completes formative assessment but does not meet attendance requirement, learner will be notified that they must re-attend course in full.
    3. If learner meets attendance requirement in full but fails content assessment, learner is given a maximum of two additional re-attempts before being required to re-attend the course in full. Learner will be notified and will receive a certificate of attendance after the third failed assessment attempt.
    4. If learner successfully completes content assessment but fails to complete summative assessment, learner will be notified and CEUs will be on hold until summative assessment is completed.

Course Length

Course Type

Private
Public
Virtual Classroom

Audience

Biostatistician
CRF Designer
Data Manager
Programmer

Industry

Academic Institution
BioTech
Clinical Laboratory
Consulting
CRO
Government
Healthcare Provider
Medical Device
NPO
Pharmaceutical
Technology Service Provider