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Clinical Research Data Management

1. Foundations of Clinical Research Data Management

Foundations of Clinical Research Data Management: Orientation
Introduction to Clinical Research Data Management
The Evolution of Clinical Data Management
Why Data Management Matters
The Clinical Research Lifecycle
The Role of the Clinical Data Manager
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
Foundations of Clinical Research Data Management: Summary and Assessment

2. Protocol Translation and CRF Design

Protocol Translation and CRF Design: Orientation
Understanding the Study Protocol
Translating Research Objectives into Data Requirements
Primary and Secondary Outcomes
Developing a Data Collection Matrix
Principles of CRF Design
Visit Schedules and Longitudinal Data Collection
Variable Naming and Coding Standards
Data Dictionaries and Metadata
CRF Version Control
Common CRF Design Errors
Protocol Translation and CRF Design: Summary and Assessment

3. Database Design in REDCap

Database Design in REDCap: Orientation
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
Database Design in REDCap: Summary and Assessment

4. Data Entry, Validation, and Access Control

Data Entry, Validation, and Access Control: Orientation
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
Data Entry, Validation, and Access Control: Summary and Assessment

5. Data Quality Management and Query Resolution

Data Quality Management and Query Resolution: Orientation
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
Data Quality Management and Query Resolution: Summary and Assessment

6. Introduction to R for Clinical Data Management

Introduction to R for Clinical Data Management: Orientation
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
Introduction to R for Clinical Data Management: Summary and Assessment

7. Data Cleaning and Preparation in R

Data Cleaning and Preparation in R: Orientation
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
Data Cleaning and Preparation in R: Summary and Assessment

8. Data Analysis in R

Data Analysis in R: Orientation
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
Data Analysis in R: Summary and Assessment

9. Data Visualization and Dashboards

Data Visualization and Dashboards: Orientation
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
Data Visualization and Dashboards: Summary and Assessment

10. Reporting and Reproducibility

Reporting and Reproducibility: Orientation
Why Reproducible Reporting Matters
R Markdown and Quarto Basics
Designing a Data Management Report
Workflow Documentation
Version Control and Review
Reporting and Reproducibility: Summary and Assessment

11. Data Documentation and Metadata

Data Documentation and Metadata: Orientation
Why Documentation Matters
Codebooks and Data Dictionaries
FAIR Principles and Clinical Research Data
Archival and Governance
Preparing a Documentation Package
Data Documentation and Metadata: Summary and Assessment

12. Final Project Preparation, Presentation, and Course Integration

Final Project Preparation, Presentation, and Course Integration: Orientation
Purpose of the Final Project
Defining Project Scope
Suggested Project Themes
Preparing Final Deliverables
Presentation and Peer Review
Assessment Rubric
Course Integration and Professional Reflection
Final Project Preparation, Presentation, and Course Integration: Summary and Assessment

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Data Analysis in R

Summarizing Categorical Variables

Code Example 5

30-45 minutes Applied Step 11 of 13

Code

Code Example 5

11 / 13

Code

Code Example 5

r

prepared_data |>
  count(outcome_status, .drop = FALSE)

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