SW-3F - Introduction to R Shiny for Pharmacoepidemiologists: Interactive Visualization in Real-World Evidence Studies

Background: Interactive visualizations using web applications are essential in pharmacoepidemiology to facilitate communication and decision-making among researchers, regulators, and healthcare stakeholders. A hands-on workshop format is the most effective way for participants to gain practical experience in building web applications with R Shiny for real-world evidence studies.

Objectives: • Understand the key functionalities of R Shiny and how it applies to pharmacoepidemiologic research.
• Gain practical experience in developing a basic interactive visualization tool using R Shiny.
• Learn how to create user-friendly interfaces and server logic to enable interactive data exploration.
• Explore best practices for using R Shiny to enhance stakeholder engagement and decision-making.
• Build confidence in implementing R Shiny for research projects through guided exercises.

Designed for researchers, data scientists, and professionals in pharmacoepidemiology who want to develop interactive tools for analyzing and visualizing healthcare data. Basic familiarity with R is recommended but not required.

Description: This interactive workshop provides a practical introduction to R Shiny, equipping participants with the skills to develop simple, effective applications for data visualization in pharmacoepidemiology. Led by experts in R Shiny and real-world data analysis, the workshop will feature live demonstrations, guided hands-on exercises, and interactive discussions.

Structure:
1. Introduction to R Shiny (15 min)
• Overview of R Shiny and its relevance in pharmacoepidemiologic research
• Examples of real-world applications to enhance communication and transparency
2. Hands-on: Your First App (40 min)
• Step-by-step guidance on developing a simple interactive app
• Working with user interface and server components
• Creating basic visualizations (e.g., treatment adherence)
• Interactive Q&A
3. Enhancing Interactivity (20 min)
• Adding interactive elements (e.g., filters, dropdowns, dynamic plots)
• Brief introduction to integrating large language models (LLMs) for enhanced usability
• Group discussion on potential applications in participants’ research
4. Wrap-Up (15 min)
• Key takeaways and resources for continued learning
• Discussion on challenges and future applications

Speakers:
Máté Szilcz, PhD (Karolinska Institutet, Viti Science) – R Shiny expert specializing in interactive tools for pharmacoepidemiology.
Yuchen Guo, PhD (DARWIN EU, University of Oxford) – Experienced in regulatory-focused R Shiny applications for real-world evidence studies.
Jyotirmoy Sarker, PhD candidate (University of Illinois Chicago) – Focused on interactive visualization in pharmacoepidemiology and economic modeling.