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Clinical R in Practice: The Open-Source Stack in Pharma
A 15-part series on R in pharma: pharmaverse ADaM, ARD tables, risk-based validation, targets pipelines, GxP Shiny, LLM agents — collected into a free bilingual book.
Topic
#clinical-trials
9 posts
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
Double Programming Without the Duplication: AI-Generated QC Code
Independent QC re-programming costs 30–50% of clinical programming effort. An AI framework matched 97.1–100% of variables while keeping independence intact.
Benchmarking RAG for Clinical TLF Templates: 1,999 Experiments
Five LLM generation methods benchmarked across 1,999 bootstrap experiments on ICH E3-conformant TLF templates: hybrid RAG with reranking beats direct prompting.
#rag#llm-benchmarking#tlf-templates#clinical-trials#r-code-generation
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.
Five Years of Automation Evidence: Real Gains, Mostly Low Grades
A GRADE-rated review of 2020–2025 automation evidence in statistical programming: real gains, mostly Low to Very Low quality evidence.
#statistical-programming#automation#evidence-quality#clinical-trials
Modernizing a 558-Macro SAS Library Without Touching Validated Code
A bridge map, typed IR, and orchestrator wrap a legacy SAS TFL library unchanged — AI-ready JSON on day one, 80%+ cell-level parity, optional 92% code cut.
#sas#legacy-modernization#clinical-trials#llm-integration#statistical-programming
Thin MCP, Thick Skills: Five Layers for Clinical Programming Agents
ClinAgent splits clinical programming capability into five layers, keeping MCP tools stateless and packaging domain expertise as testable skills.
#llm-agents#mcp#clinical-trials#statistical-programming#agent-architecture