Does this skill require specific data formats?

It is designed to work with CDISC-standard ADaM datasets in various formats including .xpt, .sas7bdat, .csv, and .json.

Can I customize the generated Shiny app?

Yes, the skill produces a standard R script (app.R) that can be further modified, extended with custom modules, or integrated into larger Shiny environments.

Is the code generated by the teal app reproducible?

Yes, one of the primary benefits of teal is that it automatically tracks and displays the R code required to reproduce any specific output or filter state within the app.

ADaM-to-Teal Clinical App Generator

Name: ADaM-to-Teal Clinical App Generator
Author: Appsilon

byAppsilon

•

Data Science & ML

Generates interactive Shiny-based clinical trial exploration apps using the R teal framework from ADaM datasets and mock TLG shells.

This skill automates the creation of robust teal Shiny applications for clinical data exploration, review, and quality control. By processing ADaM datasets and optional mock Table, Listing, and Graph (TLG) shells, it builds customized dashboards featuring global filtering, reproducible R code generation, and integrated report exporting. It serves as a powerful interactive alternative to static outputs, allowing biostatisticians and clinical programmers to explore trial data through a live, filterable interface using the industry-standard teal ecosystem.

Key Features

01Automated teal module configuration based on ADaM dataset types

02Dynamic R script generation for self-contained Shiny applications

03Integrated reporting functionality via teal.reporter and teal.slice

04Support for multi-format clinical data (CSV, JSON, XPT, SAS7BDAT)

05Built-in reproducibility features showing the R code behind every output

0622 GitHub stars

Use Cases

01Rapidly prototyping Shiny apps from static mock TLG specifications

02Creating interactive dashboards for ad-hoc clinical trial data review

03Performing visual quality control and validation of ADaM datasets

Key Features

01Automated teal module configuration based on ADaM dataset types

02Dynamic R script generation for self-contained Shiny applications

03Integrated reporting functionality via teal.reporter and teal.slice

04Support for multi-format clinical data (CSV, JSON, XPT, SAS7BDAT)

05Built-in reproducibility features showing the R code behind every output

0622 GitHub stars

Use Cases

01Rapidly prototyping Shiny apps from static mock TLG specifications

02Creating interactive dashboards for ad-hoc clinical trial data review

03Performing visual quality control and validation of ADaM datasets