Clinical Research Data Management Course – CLIREN Learning Management System

About Course

This 12-week instructor-led course provides practical, hands-on mentorship and training in clinical research data management using open-source tools. It teaches principles of data management and concepts of data science required to support clinical research projects. It focuses on the end-to-end data lifecycle – from protocol translation and CRF development to database design, validation, data quality control, monitoring, cleaning, visualizations, curation, analysis, report writing and data archival.

Participants will gain competencies in applying Good Clinical Practice (GCP), FAIR Data Principles, and Data Protection standards, with an emphasis on using REDCap for database design and R for data management and visualization.

By the end of the course, participants will be able to:

Design clinical databases in REDCap aligned with study protocols.
Develop data management plans and workflow documentation.
Conduct data validation, cleaning, and quality checks.
Analyze and visualize clinical data using R.
Generate automated reports and dashboards for study monitoring.

Apply ethical and regulatory frameworks for secure data handling.

The Clinical Research Data Management Course provides a practical foundation for managing high-quality research data throughout the clinical research data lifecycle. It combines core data management principles with hands-on application of tools such as REDCap and R, helping learners understand not only how to manage data, but also why good data management is essential for credible and ethical research.
Understand the clinical research data lifecycle, from study planning and data collection through cleaning, analysis, reporting, archiving and close-out.
Apply principles of data governance, ethics, privacy, confidentiality and regulatory compliance.
Design effective data collection systems and electronic Case Report Forms (eCRFs).
Build and manage research databases using REDCap.
Use R for practical clinical data management, including importing, cleaning, transforming, validating and summarising datasets.
Create data visualisations and monitoring dashboards to support research oversight and decision-making.
Apply recognised Good Clinical Practice and clinical data management standards in day-to-day research activities.
The course places particular emphasis on practical application. Learners work with realistic clinical research scenarios and datasets rather than learning data management only as theory. The course also introduces the use of R and Shiny for monitoring and visualisation; for example, learners develop monitoring outputs covering enrolment, missing outcomes and open data queries.

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.

Welcome to this course
Introduction to clinical data management
The Evolution of Clinical Data Management
Why Data Management Matters.
The Clinical Research Lifecycle
The Role of the Clinical Data Manager
Key Characteristics of High-Quality Data
Data Flow in Clinical Research
Introduction to Good Clinical Practice
Introduction to FAIR Data Principles
Data Protection, Ethics, and Confidentiality
Chapter Summary
Quiz

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.

Understanding the Research Protocol
Translating Objectives into Data Requirements
Primary and Secondary Outcomes
Developing a Data Collection Matrix
Introduction to CRFs in Clinical Research
Visit Schedules and Longitudinal Data Collection
Variable Naming and Coding Standards
Data Dictionaries and Metadata
CRF Version Control
Common CRF Design Mistakes
Summary
Quiz

Database Design in REDCap
The REDCap database design phase transforms well-designed CRFs into a functional electronic data capture system.

Introduction to REDCap
REDCap Project Types
Project Setup and Online Designer
Instruments, Fields, and Variable Types
Validation Rules
Branching Logic
Calculated Fields
Longitudinal Projects and Events
Repeating Instruments
Data Access Groups
User Rights and Permissions
Moving from Development to Production
Testing and User Acceptance Testing
summary
Quiz

Data Entry, Validation, and Access Control
This chapter explores the operational aspects of data collection within REDCap, including data entry workflows, validation procedures, user rights management, audit trails, and quality assurance activities that help ensure research data remain reliable and compliant with regulatory standards.

Data Entry Workflows
Source Documents and Source Data Verification
Single Entry, Double Entry, and Direct Electronic Capture
Data Validation During Entry
Required Fields and Completion Guidelines
Role-Based Access Control
Data Access Groups in Multisite Studies
Audit Trails
User Training and Competency
Common Data Entry Errors
Chapter Summary
Quiz

Data Quality Management and Query Resolution
This topic is about ensuring that data is accurate, complete, and consistent. It covers identifying, investigating, and correcting errors or discrepancies in data. It also focuses on timely resolution of data queries to maintain data integrity and reliability.

Understanding Data Quality
Dimensions of Data Quality
Sources of Data Quality Problems
Data Quality Planning
REDCap Data Quality Module
Query Management Workflow
Writing Effective Queries
Query Metrics
Central Monitoring
Risk-Based Data Monitoring
Database Freeze and Database Lock
Chapter Summary
Quiz

Introduction to R for Clinical Data Management.
This topic introduces R, an open-source programming language used for statistical analysis, data management, and visualization. In clinical research, R is applied to clean, analyze, and report data in a reproducible and reliable manner.

Why R Matters for Clinical Data Management
Installing R and RStudio
Understanding the RStudio Interface
R Projects and Folder Structure
Scripts, Console, Environment, and Working Directory
Objects, Vectors, and Data Frames
Installing and Loading Packages
Introduction to the tidyverse
Importing REDCap Exports, CSV, and Excel Files
Inspecting Data with glimpse, summary, head, names, and dim
Basic Data Quality Checks in R
Chapter Summary
Quiz

Data Cleaning and Preparation in R
This topic introduces data cleaning and preparation in R, focusing on organizing and transforming raw data into a usable format. It covers handling missing values, correcting errors, and structuring data to ensure accurate and reliable analysis.

The Purpose of Data Cleaning and Preparation
Raw, Cleaned, and Analysis-Ready Datasets
Importing and Exporting Data Through the REDCap API
Handling Missing Data in Cleaning Workflows
Recoding Categorical Variables
Creating Derived Variables
Writing Readable Cleaning Scripts
Cleaning Logs and Query Outputs
Preparing Datasets for Analysis and Reporting
Common Cleaning Risks and How to Avoid Them
Chapter Summary
Quiz

Data Analysis in R
This topic introduces data analysis in R, focusing on applying statistical methods to explore and interpret data. It covers summarizing data, performing analyses, and generating insights to support clinical research decisions.

Why Descriptive Analysis Matters in Clinical Data Management
Preparing Data Before Analysis
Summarizing Categorical Variables
Summarizing Numeric Variables
Grouped Summaries for Monitoring and Reporting
Cross-Tabulations and Proportions
Creating Report-Ready Tables in R
Interpreting Descriptive Outputs
Exporting Summary Outputs
Common Errors in Descriptive Analysis
Chapter Summary
Quiz

Data Visualization and Dashboards
This topic is about presenting data in visual formats such as charts and graphs to make it easier to understand. It also covers creating dashboards that organize and display key information for effective monitoring and decision-making.

Why Visualization Matters in Clinical Data Management
Principles of Good Clinical Data Visualization
Creating Basic Charts with ggplot2
Visualizing Missingness and Data Quality
Enrollment and Follow-Up Monitoring Plots
Introduction to Shiny Dashboards
Dashboard Governance and Privacy
Chapter Summary
Quiz

Reporting and Reproducibility
This topic is about generating clear and accurate reports from data analysis. It also focuses on ensuring that analyses can be consistently reproduced using well-documented methods and scripts.

Data Documentation and Metadata
This topic is about describing and organizing data through clear documentation and metadata. It focuses on defining data elements, structures, and context to ensure data is understandable, consistent, and usable.

Final Project Preparation, Presentation, and Course Integration
Focuses on preparing and organizing a final project using knowledge and skills gained throughout the course.

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About Course

What Will You Learn?

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.

Welcome to this course

Introduction to clinical data management

The Evolution of Clinical Data Management

Why Data Management Matters.

The Clinical Research Lifecycle

The Role of the Clinical Data Manager

Key Characteristics of High-Quality Data

Data Flow in Clinical Research

Introduction to Good Clinical Practice

Introduction to FAIR Data Principles

Data Protection, Ethics, and Confidentiality

Chapter Summary

Quiz

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.

Understanding the Research Protocol

Translating Objectives into Data Requirements

Primary and Secondary Outcomes

Developing a Data Collection Matrix

Introduction to CRFs in Clinical Research

Visit Schedules and Longitudinal Data Collection

Variable Naming and Coding Standards

Data Dictionaries and Metadata

CRF Version Control

Common CRF Design Mistakes

Summary

Quiz

Database Design in REDCap The REDCap database design phase transforms well-designed CRFs into a functional electronic data capture system.

Introduction to REDCap

REDCap Project Types

Project Setup and Online Designer

Instruments, Fields, and Variable Types

Validation Rules

Branching Logic

Calculated Fields

Longitudinal Projects and Events

Repeating Instruments

Data Access Groups

User Rights and Permissions

Moving from Development to Production

Testing and User Acceptance Testing

summary

Quiz

Data Entry Workflows

Source Documents and Source Data Verification

Single Entry, Double Entry, and Direct Electronic Capture

Data Validation During Entry

Required Fields and Completion Guidelines

Role-Based Access Control

Data Access Groups in Multisite Studies

Audit Trails

User Training and Competency

Common Data Entry Errors

Chapter Summary

Quiz

Understanding Data Quality

Dimensions of Data Quality

Sources of Data Quality Problems

Data Quality Planning

REDCap Data Quality Module

Query Management Workflow

Writing Effective Queries

Query Metrics

Central Monitoring

Risk-Based Data Monitoring

Database Freeze and Database Lock

Chapter Summary

Quiz

Introduction to R for Clinical Data Management. This topic introduces R, an open-source programming language used for statistical analysis, data management, and visualization. In clinical research, R is applied to clean, analyze, and report data in a reproducible and reliable manner.

Why R Matters for Clinical Data Management

Installing R and RStudio

Understanding the RStudio Interface

R Projects and Folder Structure

Scripts, Console, Environment, and Working Directory

Objects, Vectors, and Data Frames

Installing and Loading Packages

Introduction to the tidyverse

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.

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.

Database Design in REDCap
The REDCap database design phase transforms well-designed CRFs into a functional electronic data capture system.

Introduction to R for Clinical Data Management.
This topic introduces R, an open-source programming language used for statistical analysis, data management, and visualization. In clinical research, R is applied to clean, analyze, and report data in a reproducible and reliable manner.

Data Cleaning and Preparation in R
This topic introduces data cleaning and preparation in R, focusing on organizing and transforming raw data into a usable format. It covers handling missing values, correcting errors, and structuring data to ensure accurate and reliable analysis.

Data Analysis in R
This topic introduces data analysis in R, focusing on applying statistical methods to explore and interpret data. It covers summarizing data, performing analyses, and generating insights to support clinical research decisions.

Data Visualization and Dashboards
This topic is about presenting data in visual formats such as charts and graphs to make it easier to understand. It also covers creating dashboards that organize and display key information for effective monitoring and decision-making.

Reporting and Reproducibility
This topic is about generating clear and accurate reports from data analysis. It also focuses on ensuring that analyses can be consistently reproduced using well-documented methods and scripts.

Data Documentation and Metadata
This topic is about describing and organizing data through clear documentation and metadata. It focuses on defining data elements, structures, and context to ensure data is understandable, consistent, and usable.

Final Project Preparation, Presentation, and Course Integration
Focuses on preparing and organizing a final project using knowledge and skills gained throughout the course.