R Shiny Clinical Review Tools on the Horizon

Jimmy Wong, Statistician
FDA/Center for Drug Evaluation and Research/
Office of Translational Sciences/Office of Biostatistics

PharmaSUG Annual Conference
Baltimore, MD | May 16, 2017


This presentation reflects the views of the author and should not be construed to represent FDA's views or policies.

About Myself

  • A statistician/statistical analyst in CDER's Office of Biostatistics for over a year
  • Previously in the Office of Pharmaceutical Quality as an ORISE Fellow working on post-marketing stat analysis
  • Developing R Shiny Apps for clinical reviews, such as
    • Patient-reported outcomes (PRO) visualizations
    • Forest plots for subgroups
    • Multiplicity procedures
  • Providing statistical support to reviews and data standards
  • Organizing an R Shiny Users Group within CDER


  1. What is Shiny?
  2. How does Shiny work?
  3. How is Shiny used at the FDA?
  4. How can I start using Shiny?
  5. Take Home Points

What is Shiny?

First things first.

Shiny Intro

  • Only requires programming experience in R to develop an app
  • Used in various disciplines ranging from biostatistics, statistics education, finance, etc.
  • Free of charge to develop but cost of a server is expected for vast distribution
  • New features are continuously being developed by the RStudio team

Shiny’s Advantages

  • Data analyses and visualizations can be programmed into a user-interface format starting from base R code
  • Combined with RMarkdown, routine reports can be generated for higher productivity
  • In the regulatory setting, standardized tools can be created for efficiency in conducting reviews
  • No HTML, CSS, or JavaScript experience required but could provide enhancements
  • Access to Shiny apps can be easily done through a Shiny server

How does Shiny work?

Components of a Shiny App