Topic
7 posts
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
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.
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.
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
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
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
Schema-only synthetic ADaM generation plateaus at 0.45 overall quality; enriching schemas from protocol/SAP/CRF knowledge graphs plus templates reaches 0.70.
#synthetic-data#adam#knowledge-graphs#llm#statistical-programming