ALT {teal} is an innovative open-source R-Shiny framework that has transformed how clinical trial data is analyzed and visualized in recent years. By streamlining the creation of interactive web applications, it enables R programmers and data scientists to deliver insights faster while promoting efficiency, transparency, and reproducibility in data exploration. This hands-on workshop, built on the latest {teal} 1.0 release, will start from the basics and progressively cover practical topics for building {teal} applications. Together, we will explore key features of {teal} and work through step-by-step exercises designed to build confidence and proficiency in {teal} programming. No prior experience with {teal} or attendance at previous workshops is required - all R users are welcome. Designed to deepen participants’ understanding of the {teal} framework, this session will equip attendees with the skills to leverage the {teal} ecosystem to create scalable, reproducible applications that acce

ALT GSK’s journey to Clinical Study Reporting Using Open Source GSK’s Journey to Clinical Study Reporting Using Open Source chronicles the transformation of clinical reporting at GSK, the presentation marks pivotal moments in the pharmaceutical industry’s evolution—from the proprietary, SAS-dominated era of the 1980s and 90s, through the pain of rising R&D costs and global health crises, to the emergence of collaborative models and open-source innovation. The story highlights how industry challenges catalyzed a shift toward openness and collaboration. Key milestones include the FDA’s clarification on software neutrality, the rise of R and open-source platforms, and GSK’s commitment to writing at least 50% of code in open-source languages. The COVID-19 pandemic accelerated this transformation, driving rapid adoption of data science platforms and collaborative tools. GSK’s multi-wave approach to change is shared and candidly addresses ongoing challenges: technical validation, regulatory un

ALT The number of workflows and tools leveraging large-language-models (LLMs) is growing exponentially across the world, and the life sciences industry is no exception. In this session, Eric Nantz takes the role of the curious and savvy data scientist as he is joined by Posit CTO Joe Cheng in a special live demonstration on using Databot to power an exploratory data science analysis. Throughout the demonstration, Joe will share the fascinating origin story of how Databot came to be, along with a deeper conversation on the recent trends and guiding principles of harnessing AI tools for data analyses and software engineering. This interactive, spontaneous format will allow the audience to submit questions and steer the direction of the session, making it a truly engaging, shared experience.

ALT Simon (he/him), an R developer and data scientist. I build tools for data scientists at Posit PBC (formerly RStudio).🐛 focused on helping R users get the most out of LLMs: tidyverse/vitals: LLM evaluation posit-dev/mcptools: Model Context Protocol (MCP) servers and clients in R posit-dev/btw: easily provide context on R stuff to LLMs simonpcouch/gander: high-performance, low-friction chat for data science simonpcouch/chores: an extensible collection of LLM assistants simonpcouch/predictive: an agentic frontend for predictive modeling with tidymodels simonpcouch/kapa: RAG-based search via the kapa.ai API

ALT With the pointblank package it’s really easy to methodically validate your data whether in the form of data frames or as database tables. On top of the validation toolset, the package gives you the means to provide and keep up-to-date with the information that defines your tables. https://rstudio.github.io/pointblank/

ALT R/Pharma conference registration is now open. R/Pharma is a free conference covering the use of R and other open source technologies in Pharma and Biotech industries. This year the conference will run from 3-7 November with both a Europe/US and an APAC track.

ALT Mon, Nov 3, 2025 7:45 AM - Fri, Nov 7, 11:45 PM EST R/Pharma 2025 https://events.zoom.us/ev/Ai-geyS63AOzr-Tskeu2jwsPAZkX9yQixTAJuQzNT-h_rw24wX-p~AnyGxi92zGyWPS_7xPj-Oax9RjoaU3pZS0DjeX45ML084eyqq4qo6O7BMg

ALT Bayesian inference and Stan offer many advantages for analyzing time-to-event data, including incorporating prior knowledge into the model, propagating all sources of uncertainty to produce well-calibrated predictions, and integrating arbitrary utility functions for optimal decision-making, such as patients' preferences for different types of risks. The latter point is particularly relevant in multistate models where people may differ in their preferences towards multiple competing events.In this workshop, we will briefly introduce Bayesian workflow – the typical steps in Bayesian analysis and basic (single-event) survival models in the Bayesian context. We will then proceed to introduce multistate models where we are tracking multiple event types, such as bleeding and stroke in cardiovascular trials, or stable disease, progressive disease, and death in oncology trials. Time permitting, we will demonstrate how to incorporate the patient’s utility function into a decision analysis.