tidyCDISC is a shiny app to easily create custom tables and figures
from ADaM-ish data sets.
One of 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 a single patient profile.
The beauty of the application: users don’t have to write a lick of code
to gather abundant insights from their study data. Thus, tidyCDISC
aims to serve a large population of clinical personnel with varying
levels of programming experience. For example:
-
A clinical head, with presumably no programming experience (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 excess statistical programming requests for tables that aren’t required, but are “nice to see”.
- A statistical programmer can use
tidyCDISCto perform preliminary QC programming prior to writing their own code in a validated process. Users who leveragetidyCDISCfor routine trial analysis report significant time savings, about 95% on average, when performing their programming duties.
For a high-level overview of the app with brief 10-minute demo, please
review the following presentation on tidyCDISCat Shiny Conf 2022:
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 is designed to accept
sas7bdat files only.
If you’re looking to regularly generate R code for tables, the
tidyCDISC app offers a handy feature to export an R script for full
reproducibility of analyses 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 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 right
now: launch 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. Execute the
following code to install the package:
# 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:
# Launch the application
tidyCDISC::run_app()Happy exploring!