tidyCDISC is a shiny app to easily create custom tables and figures from ADaM-ish data sets.
tidyCDISC’s goals is to develop clinical tables that meet table standards leveraged for submission filings, called “standard analyses”. However, this is secondary to the app’s primary purpose: providing rich exploratory capabilities for clinical studies. High-level features of the app allow users to produce customized tables using a point-and-click interface, examine trends in patient populations with dynamic figures, and supply visualizations that narrow in on single patient profile.
The beauty of this application is that the user doesn’t have to write a lick of code to gather abundant insights from the study data, so it aims to serve a large population of clinical personnel with varying levels of programming experience. For example:
A clinical head, with presumably no programming skills but the most domain expertise, can explore results without asking a statistician or programmer to build tables & figures.
A statistician can use the application to make tables/figures instantly, cutting down on statistical programming requests for excess tables that aren’t required, but just “nice to see”.
tidyCDISCto perform preliminary QC programming prior to writing code in a validated process. Users who’ve leverage
tidyCDISCfor routine trial analysis tend to report significant time savings, about 95%, when performing programming duties.
For a high-level overview of the app with 10-minute demo, please review the following conference presentation on
tidyCDISCat R/Medicine 2020:
As previously mentioned,
tidyCDISC can only accept data sets that conform to CDISC ADaM standards with some minor flexibility (see upload requirements for more details). At this time, the app only accepts sas7bdat files.
If you’re looking to regularly generate R code for tables, the
tidyCDISC app has a built-in export feature that downloads an R script to reproduce any analysis performed in the app.
You can start using the demo version of the app here: tidyCDISC. Note the demo version disables the Data Upload feature and instead uses the CDISC pilot data. If you’d like to upload your own study data, we recommend installing
tidyCDISC from CRAN (instructions below) to run the app locally or deploy it in your preferred environment. Please review the “Get Started” guide to follow an example use case with the app. However, to optimize one’s use of
tidyCDISC, we highly recommend reading the following articles that take a deeper look into the topics presented in the “Get Started” tutorial:
We’re confident the
tidyCDISC application can save you time. If there is some use case that
tidyCDISC can’t solve, we want to know about it. Please send the developers a message with your question or request!
As a reminder, you can start using the demo version of the app here: tidyCDISC without any installation required. However, if you choose to upload your own study data OR export & run R code from the Table Generator, you will need the
tidyCDISC package installed on your machine locally. Execute the following code to install the package to your local machine:
# Install from CRAN install.packages("tidyCDISC") # Or install the latest dev version remotes::install_github("Biogen-Inc/tidyCDISC")
With a simple
library(tidyCDISC) you can access all the exported functions from
tidyCDISC that help users reproduce analysis performed in the app. Or, you can run the application locally (or deploy it in an
app.R file) using:
# Run the application tidyCDISC::run_app()