The One-Click Guide to Pharmaverse: Run Pilot-Ready Clinical Workflows Today

In the high-stakes world of pharmaceutical clinical reporting, the transition to open source often feels like a leap into the unknown. But what if you could try out the future of clinical programming without installing a single piece of software?

The Pharmaverse Examples Site is more than just documentation—it is a living, breathing “open book” of clinical workflows. It provides a direct path for teams to move from raw EDC data to submission-ready outputs using a curated, industry-standard open source toolkit.

Seeing Pharmaverse in Action through Living Examples 

Pharmaverse is a connected network of companies and individuals working to promote collaborative development of a curated ecosystem of R (and python) packages (like {admiral}, {sdtm.oak}, and {gtsummary}) designed to standardize clinical reporting. It is a working group under PHUSE. 

While each package has its own documentation, the Examples Site is where the magic happens: it’s where they are shown to work together to form a whole greater than the sum of the individual parts.

Zero to R in Seconds: The Pre-Loaded Playground

A barrier to trying R is often the environment setup. The Examples site removes this entirely via Posit Cloud.

• One Click: Just hit the “Launch Posit Cloud” button on the homepage.
• Zero Configuration: You enter a fully pre-configured environment where every package and dependency is already loaded.
• The Workflow: You aren’t just looking at static code snippets. You are opening a real RStudio instance, ready to run end-to-end scripts immediately.

Not only the environment, but it also offers a suite of test data and example specifications to support you. Here there is: {pharmaverseraw}, {pharmaversesdtm} and {pharmaverseadam} to allow you to explore end-to-end clinical reporting with a consistent set of realistic clinical data. Plus, there is example metadata and specifications provided here.

The Workflow: From Raw Data to Submission

The site is organized into dedicated folders that mirror the standard clinical programming lifecycle. You don’t have to guess where to start; you simply follow the data.

1. Starting with SDTM: Mapping via {sdtm.oak}

The flow begins with {pharmaverseraw}—synthetic, EDC-agnostic data. Here, using {sdtm.oak}, you can see how raw variables are mapped to SDTM standards using reusable, rule-based logic. Just open the SDTM folder, grab a script for a domain (like DM or AE), and watch how the package handles the heavy lifting of this data transformation.

2. ADaM Derivations: The {admiral} Engine

Once you have your SDTMs, you move to the ADaM folder. This is where the {admiral} family of packages shines, in conjunction with other pharmaverse packages. The modular scripts break down complex derivations—like subject-level analysis (ADSL) or time-to-event (ADTTE)—into readable, step-by-step R code. Because the scripts are ready-made, you can see exactly how a treatment start date is derived or how a population flag is set, ensuring total clarity for the programmer.

3. Reporting: Tables, Listings, and Graphs (TLGs)

The final clinical outputs are prepared in the Reporting section. The examples show you how to take your ADaM datasets and turn them into static displays, such as for demographics, adverse events or pharmacokinetics. There are a selection of different examples for different approaches including package preference and use of Analysis Results Datasets (ARDs). There’s even articles around document creation too, such as displaying these results in different formats like slide decks. 

4. Interactive Reporting with {teal}

Clinical reporting isn’t just static PDFs anymore. The site showcases {teal}, a Shiny-based framework. You can launch interactive applications directly to explore data dynamically, providing a glimpse into how modern “analysis data dynamic review” applications look in the real world.

Traceability and Submissions 

Modern clinical programming requires more than just data—it requires an audit trail. The Examples site highlights:

• Logging: Using packages like {logrx}, the examples demonstrate how to generate execution logs that prove exactly what happened during your R session.
• Submission Guidance: The site includes a dedicated eSubmission section, showing how to package your R-based outputs into the formats (like XPT) and structures required by regulatory agencies like the FDA or EMA.

The Future: Your Input Needed

Pharmaverse Examples is a living site, and pharmaverse itself is an ever-evolving community effort. The team needs your help to continue this growth for the good of all patients across the world.

How to get involved:

1. Explore: Launch the Posit Cloud playground and run an example script today.

2. Contribute: Want to help author new examples or to join the small across-industry team that maintains this resource? Submit an issue, pull request or reach out to us on GitHub!

3. Join the Community: Join the Pharmaverse Slack to ask questions and help collaboratively shape the future of clinical programming.

The era of “zero to R in seconds” is here. Stop wondering if open source works for pharma and start seeing it in action.