Journal Article

ClinAgent: AI-Assisted Methodology for Clinical Trial Data Processing and Statistical Programming

Jaime Yan

Biology Methods and Protocols · 2026

Abstract

Clinical trial statistical programming remains labor-intensive: ADaM dataset derivation, TLF generation, and QC programming consume substantial effort per submission. ClinAgent is a skill-and-tool layer that augments any MCP-compatible AI coding agent with clinical-programming capabilities: nine skills (SK-001 Study Setup through SK-009 eSub Packaging) package prompts, deterministic rule engines, and decision trees, while thin MCP tools provide stateless I/O for SAS datasets, Excel specifications, and log files. All nine skills passed functional validation on artifacts from a single production Phase 2 study — deterministic components matched all 56 ADSL variables, and prompt-based specification generation reached 72.1% derivation accuracy (>96% on simple domains, <55% on complex domains, with wide confidence intervals making these point estimates upper bounds) — while end-to-end productivity gains were not measured and remain future work. Human review gates and deterministic audit trails preserve the traceability and reproducibility required for regulatory submissions.

Keywords

clinical trials · statistical programming · Model Context Protocol · AI coding agents · CDISC · ADaM · automation

Cite

Jaime Yan. "ClinAgent: AI-Assisted Methodology for Clinical Trial Data Processing and Statistical Programming." Biology Methods and Protocols, 2026. doi:10.1093/biomethods/bpag032.