H1ST-AI-STAT-004 · Task 3.4
The blank page shouldn't cost you a week
The hardest part is the blank page, and the scariest part is trusting a draft you can't check. This agent uses an LLM supplied with your study context, tables, and analysis datasets to draft results narratives and stakeholder summaries where every figure traces back to a source output. Biostatisticians and writers get past the blank page with the numbers grounded and every claim reviewable.
The grind you know
Turning a stack of tables into readable results text is slow, painstaking work, and every freehand paragraph carries the quiet risk of a number drifting from its source output. You have been burned by ungrounded AI writing too, which is why automation often costs you as much time fact-checking as it saves, or you avoid it altogether.
Part of the Statistical Programming family
SDTM/ADaM, Define.xml, TLFs & analytics.
The hardest part is the blank page, and the scariest part is trusting a draft you can't check. This agent uses an LLM supplied with your study context, tables, and analysis datasets to draft results narratives and stakeholder summaries where every figure traces back to a source output. Biostatisticians and writers get past the blank page with the numbers grounded and every claim reviewable.
Supply the study synopsis, TLFs, and datasets that the narrative should be written from.
The LLM generates narrative and summary text drawn strictly from the supplied outputs, tagging each figure to its source.
A biostatistician reviews the grounded draft, resolves flagged assumptions, and remains the author of record.
Capabilities
Generates narrative text only from supplied TLFs and datasets, so every reported number has a traceable source.
Drafts efficacy and safety results sections aligned to the ICH E3 results structure.
Condenses detailed results into stakeholder-ready summaries for internal and governance reporting.
Links each stated figure back to its originating table or listing for rapid statistician verification.
In practice
Where the agent shows up in the day-to-day of a live trial — the moments the grind usually lives in.
You're staring at a stack of efficacy tables and an empty results section. The agent drafts the narrative to the ICH E3 results structure using only your supplied TLFs, so you edit grounded prose instead of starting cold.
A reviewer wants to confirm a response rate in the draft. Because every figure is tagged to its source output, you trace the statement straight back to the originating table rather than re-checking it by hand.
A table is incomplete where the narrative needs a value. Instead of guessing, the agent flags the assumption for human resolution, keeping the biostatistician the author of record and in control of the claim.
Proof
Figures shown are pre-launch targets based on internal benchmarks, not guaranteed outcomes.
Gemini / Anthropic LLM
LLM
Produces submission-quality tables, listings, and figures from ADaM datasets using a library of 100+ CDISC- and FDA-standard output templates.
Learn moreAssembles ICH E3-structured Clinical Study Reports that pull results directly from your TLFs and datasets, with full track-changes review support.
Learn moreForecasts enrollment, retention, and outcome trends and surfaces early safety and efficacy signals through an explainable risk dashboard.
Learn morePeace of mind
Every output is generated inside a validated, audit-ready platform, kept under human-in-the-loop control, and mapped to the regulatory and CDISC standards this agent supports.
Walk through it on your own workflow with a clinical-trials expert — no pressure, no obligation, and honest answers, including on the limits.
