Course Content
Foundations of Clinical Research Data Management
An introductory course covering how clinical research data is collected, managed, cleaned, and prepared for analysis while ensuring quality and regulatory compliance.
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Introduction to clinical data management
01:20
Introduction to clinical research
Why Data Management Matters.
Importance of data management
00:30:00
The Clinical Research Lifecycle
The Role of the Clinical Data Manager
Key Characteristics of High-Quality Data
Characteristics of High-Quality Data.
Data Flow in Clinical Research
Core Principles of Responsible Clinical Data Management
Introduction to FAIR Data Principles
Data Protection, Ethics, and Confidentiality
Chapter Summary
Protocol Translation and Case Report Form (CRF) Design
Focuses on translating study protocols into structured data requirements and designing clear, accurate Case Report Forms (CRFs) that support efficient and compliant data collection.
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Understanding the Research Protocol
Translating Objectives into Data Requirements
Primary and Secondary Outcomes
Developing a Data Collection Matrix
Introduction to CRFs in Clinical Research
Designing variables, Data Elements and study design structure.
Clinical Data Documentation and Version Control Systems
Common CRF Design Mistakes
Practical Example: Protocol-to-CRF Translation
Summary
Database Design in REDCap
The REDCap database design phase transforms well-designed CRFs into a functional electronic data capture system.
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Introduction to REDCap Database and Architecture
REDCap Project Creation
Designing Instruments
Database Design Specifications
Security and Access Controls
Development vs Production
Best Practices for REDCap Database Design
summary
Clinical Research Data Management Course
  1. Finalize CRFs before building.
  2. Use consistent naming conventions.
  3. Apply validation wherever possible.
  4. Use branching logic to simplify forms.
  5. Minimize free-text fields.
  6. Implement role-based permissions.
  7. Test extensively before production.
  8. Maintain version control.
  9. Document every change.
  10. Align all variables with protocol objectives.

 

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