Lectures and seminars PhD student seminar: Máté Szilcz

23-10-2025 3:00 pm - 4:30 pm Add to iCal
Wargentin lecture hall

Title: Introduction to R Shiny for Pharmacoepidemiologists: Interactive Visualization in Real-World Evidence Studies

Description:

Interactive visualizations using web applications are essential in pharmacoepidemiology to facilitate communication and decision-making among researchers, regulators, and healthcare stakeholders. 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. 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.

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.

Structure:

1. Introduction to R Shiny

  • 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

  • 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

  • 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

  • Key takeaways and resources for continued learning