H1ST-AI-STAT-004 · Task 3.4
You shouldn't learn the problem too late to fix it
You make better calls when you can see the curve bending before it breaks, not after. This agent applies predictive models to your operational and clinical data to project enrollment curves, dropout risk, and emerging safety or efficacy signals, with SHAP and LIME explanations behind every prediction. Instead of the rear-view reporting of a static status deck, sponsors and operations leads get a forward-looking view they can question and trust.
The grind you know
By the time an enrollment shortfall, a wave of dropouts, or a slow-building safety signal is undeniable, it has usually already cost you timeline or raised your risk profile. Without an explainable, continuously updated forecast, you are always reacting, mitigating a problem after it landed rather than steering around it before it did.
Part of the Statistical Programming family
SDTM/ADaM, Define.xml, TLFs & analytics.
You make better calls when you can see the curve bending before it breaks, not after. This agent applies predictive models to your operational and clinical data to project enrollment curves, dropout risk, and emerging safety or efficacy signals, with SHAP and LIME explanations behind every prediction. Instead of the rear-view reporting of a static status deck, sponsors and operations leads get a forward-looking view they can question and trust.
The agent ingests enrollment, operational, and clinical measures from your EDC and trial-management systems.
Predictive models generate enrollment, retention, and outcome projections with confidence ranges.
Results populate a risk dashboard with early-warning indicators, each backed by SHAP/LIME explanations for review.
Capabilities
Projects site-level and study-level accrual curves and flags projected shortfalls against the recruitment plan.
Estimates dropout risk by cohort and site so retention resources can be directed where they matter most.
Monitors safety and efficacy measures over time to surface emerging signals ahead of scheduled reviews.
Attaches SHAP and LIME attributions to every forecast so reviewers can see which factors drive each result.
In practice
Where the agent shows up in the day-to-day of a live trial — the moments the grind usually lives in.
Site accrual is quietly tracking below plan. The agent projects the study-level curve and flags the shortfall against your recruitment plan weeks early, so you add sites or reallocate resources before the timeline slips.
A sponsor questions why dropout risk is rising in one cohort. Because each prediction carries SHAP and LIME attributions, you show exactly which factors drive the estimate instead of asking them to trust a black box.
A safety measure begins trending before the next DSMB meeting. The agent surfaces the emerging signal early to prompt closer review, without replacing the medical monitor's or DSMB's formal assessment.
Proof
Figures shown are pre-launch targets based on internal benchmarks, not guaranteed outcomes.
Medidata Rave
EDC
IWRS / RTSM
IWRS/RTSM
Who it's for
Drafts study and results narratives and executive summaries grounded in your actual TLFs and datasets, ready for statistician review.
Learn moreProduces submission-quality tables, listings, and figures from ADaM datasets using a library of 100+ CDISC- and FDA-standard output templates.
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.
