A practical reference to CDISC standards for clinical programming teams. Covers Define-XML structure, ADaM submission requirements, Dataset-JSON, ARS, and how to build define.html from define.xml.

Cloud-native is often discussed as a technical concept, but for pharma SCE leaders it has very practical implications. This article explains what cloud-native means in the context of a Statistical Computing Environment, how technologies like containers, Kubernetes, infrastructure as code, and CI/CD reduce manual work, and why they can make change management faster without weakening compliance. It also outlines key questions to ask when evaluating whether an SCE is built for reproducibility, auditability, and future modernization.

A practical guide for pharma leaders deciding whether to build their statistical computing environment (SCE) on-premise or in the cloud. The piece argues that while on-prem still suits stable, predictable workloads and large legacy datasets, modern SCEs are no longer stable workloads — they need elastic compute, multi-language support, and AI/ML-ready hardware that cloud delivers natively. It addresses the security objection head-on (citing Gartner's finding that 99% of cloud security failures are customer misconfigurations, not platform weaknesses), highlights how cloud democratizes access to the same infrastructure powering Novo Nordisk and Eli Lilly's NVIDIA partnerships, and lands on a pragmatic recommendation: build on cloud-native patterns (containers, IaC, automated deployments) regardless of where you deploy today, so a future migration is incremental rather than a rewrite. Hybrid is positioned as the enterprise default, with 90% of organizations expected to adopt it by 2027.

Where R, Python, and SAS each fit in a modern pharma SCE, and why the AI era turns this from a language choice into an architecture decision.

If you work in clinical development, you probably hear “SCE” in every other meeting about infrastructure, compliance, or moving off SAS. The acronym gets used loosely. Sometimes it means one specific tool. Sometimes it means an entire ecosystem. Sometimes it’s shorthand for “the thing our statisticians open every morning.”

A step-by-step guide to stronger R Shiny testing with stable selectors and reusable actions. Most shinytest2 tests break too easily. They expose fragile details of your app that break your tests as your implementation changes. It’s either by doing snapshots or by using raw input IDs. But you can take back control: make tests describe behavior, not wiring.

In this article, we'll cover why R package validation is harder than most teams expect, the two main ways pharma organizations go about it today, and what we built at Appsilon to solve it.

Most statistical computing environments in pharma were designed when SAS was the default language, studies were simpler, timelines were longer, and "open source in regulated settings" was still a controversial idea.

A practical guide to building focused AI assistants inside Shiny applications using shinychat, ellmer, and vitals — covering UI setup, streaming responses, prompt design, and benchmarking.

Posit has been shipping AI packages recently. If you've tried to keep up but ended up more confused than when you started, you’re not alone.

New drugs do not reach patients quickly. The drug development process usually takes 10 to 15 years, and most programs fail before approval. That long path is not only a science challenge. It is also a data, reporting, and compliance challenge.

Follow each step as I develop a form with Shiny and Behavior-Driven Development. Learn practical techniques for better software design through automated tests. Testing your Shiny applications shouldn’t be an afterthought. Many developers write tests after building features, missing the core benefit of test-first development: tests as design tools. I’ll walk you through how I built a small content submission form using Behavior-Driven Development (BDD), starting from absolutely nothing. Whether you’re building a new app or adding a feature, the steps are the same. Follow along and see how tests drive design decisions, keep business value visible, and make refactoring safe.

If your statistical computing environment was designed before R became a viable language for submissions, before cloud infrastructure had the prominent position it has today, and before "data engineering bottleneck" entered everyone's vocabulary you're probably already feeling the pressure. It's obvious that to overcome some of these challenges, a serious modernization effort needs to ensue. The questions that remain are how, how fast, and whether to build, buy, or partner. To answer those questions, Appsilon interviewed statistical computing leaders at top pharmaceutical companies. We coupled this with internal expertise and released a comprehensive report last year: The Anatomy of Modern Statistical Computing Environments in Pharma.

If you're a Statistical Programming Manager, you're familiar with Monday mornings that start with three urgent requests from different Biostatistics leads. All of them need analytical applications for studies with regulatory deadlines this quarter—and your team's sprint backlog is already jammed. Executives push for delivering more while cutting costs and reducing teams. Talk about being between a rock and a hard place.

Learn how to write reliable R tests by wrapping external dependencies and using `testthat::local_mocked_bindings`. Make your tests fast, clean, and predictable.

Your team pitched building a clinical dashboard in-house with a $300K budget and 6-month timeline. Read this article if you don't want to be patching bugs and spending $600K more than anticipated two years from today.

You’ve requested development support for a survival analysis application aligned with your SAP, but the team’s capacity means it’ll be at least three weeks before they can start.

The conversation around AI readiness typically centers on data quality, talent acquisition, and executive buy-in. While these factors matter, they overlook a more fundamental question: does the organization have the technical and cultural infrastructure to operationalize AI at scale? 

Imagine your team needs evidence-based answers to clinical questions like "Which medications can be combined with ibuprofen to help treat back pain?" The traditional approach means weeks of work. We built a working conversational AI assistant that queries this data in hours.

Global pharma leaders have proven open source is FDA-ready, which allows mid-sized companies to cut costs, avoid vendor lock-in, and compete on innovation with the same validated tools.

AI-powered rapid prototyping turns vague dashboard ideas into interactive prototypes in hours, which cuts requirements iteration by up to 90% and prevents costly misalignment.

What if you could go from concept to working clinical dashboard prototype in 48 hours instead of 4-6 weeks?

How to speed up clinical trials since you don't have to wait months for clinical trial data to become actionable insights.

How R & Shiny are transforming clinical trial analytics and why your company can't afford to wait four months for clinical trial data to become actionable insights

Learn why traditional analytics approaches can't handle today's complex data ecosystem and what opportunities exist to dramatically accelerate your decision timeline.

Discover how aNCA simplifies non-compartmental analysis by replacing complex, fragmented workflows with a single R Shiny application.

Discover 14 trusted news, analysis, and thought leadership resources that top pharma executives use to stay informed without information overload.

Welcome to the first issue of Pharma Brief Extended, covering AI agents, open-source tools, industry trends, and events to watch this June.

What we’ve learned from five R Consortium pilots testing R and Shiny in FDA submissions.

Boost Rhino development with prompts that help Copilot understand box and module patterns.

See how a Delivery Manager helped Elkem adopt Agile and fix technical debt in their growing dev team.

Wondering how Delivery Managers add value to software projects? Learn about their impact on project success in this blog post.

Generate reliable Shiny code with GitHub Copilot by using the new @shiny tag in VS Code.

Missed ShinyConf 2025? Catch up on top talks, replays, and standout Shiny app showcases from this year’s event.

Try Rhino 1.11.0 with auto test reruns and devmode for a smoother R development setup.

Heading to ShinyConf 2025? Here's how to plan your schedule and make the most of the event.