Git for SAS Programmers: Version Control in a GxP World
Why filename-versioned SAS programs are an audit-trail liability, the minimum Git vocabulary a clinical programmer needs, and how branch-per-output maps to QC.
Topic
#gxp
11 posts
▶ video
Shiny in GxP: From rhino to a Validation File
The complete path for a clinical Shiny app: rhino structure, shinytest2 suites, secure backends, async patterns, and the validation evidence file that lets QA sign.
▶ video
Risk-Based R Validation: Open Source Under Inspection
The R Validation Hub method, hands-on: risk dimensions, the public risk-assessment app, package qualification records, and what an inspector actually asks for when your stack is open source.
▶ video
Validation as a Release Gate: Building a GxP Governance Layer for Shiny at Kardigan
How I designed the governance and validation layer for ClinVista, an internal Shiny data-review platform — and why it survived the decision to replace the platform itself.
▶ video ✎ interactive lesson
Clinical SAS Interview Questions: What Actually Gets Asked
How clinical SAS interviews actually work: four rounds from SAS mechanics to GxP habits, real-format questions, and the signals strong answers contain.
▶ video ✎ interactive lesson
Statistical Computing Environments: From SAS VM to Cloud SCE
What a statistical computing environment is for in clinical trials, why desktop SAS and shared drives failed audits, and how cloud SCE workflow changes day one.
AI Coding Assistants for Clinical Programmers: What Works in GxP
Where ChatGPT, Claude, and Copilot genuinely save time in SAS and R clinical programming, where they fail, and how to use them inside auditable GxP workflows.
#ai-coding-assistants#sas-programming#gxp#clinical-programming#llm-tools
LLM Agents in Clinical Trials: What's Real Beyond the Demos
How to tell demo-ware from deployable agentic AI in clinical trials: determinism, audit trails, 21 CFR Part 11, constrained architectures, and vendor questions.
#llm-agents#clinical-trials#gxp#audit-trail#agent-architecture
LLMs in Clinical Statistical Programming: Proven vs Hype (2026)
A tiered survey of LLM evidence in clinical trial statistical programming: benchmarked results, promising single-team studies, vendor hype, and open gaps for 2026–2027.
#llm#clinical-trials#statistical-programming#survey#benchmarks#gxp
Temperature 0 Doesn't Buy You Reproducibility
Setting temperature to zero feels like determinism. It isn't — and in GxP work the difference will find you during an audit, not during development.
Why LLM Agents Fail at Regulated Programming — the Process-DAG Fix
Free-form agent loops break down in GxP clinical programming. Structuring the work as a typed process DAG makes LLM agents reliable, replayable, and auditable.