Increase Programming Throughput Across Clinical Studies
Biostatistics teams manage increasingprogramming volume, but execution across structured workflows remains constrained.
Agentic AI for biostatistics enablessupervised execution under governance, helping improve consistency, reducebacklog, and accelerate submission readiness.
A Day in
the Life of a Head of Biometrics
You are managing multiple studies withlimited programming capacity - balancing SDTM/ADaM deliverables, interimanalysis timelines, and increasing data complexity across trials. Programming cycles extend over weeks, while database lock and submission milestones remain fixed.
Your senior statisticians are absorbedin repetitive derivation work, limiting their ability to focus on advancedmodeling and study design. Backlogs begin to build, and each delay directlyimpacts downstream timelines.
The challenge is not statisticalexpertise. It is scaling execution capacity fast enough to meet growingworkload demands.
The Pressures Biostatistics Leaders Feel Every Day
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Industry Reality
Clinical trials generate high-volume, multi-source datasets requiring structured processing
Experienced statistical programmers remain limited and difficult to scale
Increasing endpoints and adaptive designs expand statistical workload
Why Maxis AI Is Built for Biometrics
Maxis AI delivers a supervised execution layer - an AI Workforce embedded within biometrics workflows.
It executes structured programming outputs such as SDTM/ADaM mapping, TLF generation, and validation under expert oversight.
Unlike traditional automation, Maxis AI ensures outputs are generated within governed, audit-ready workflows, improving both speed and quality
From Pain to Outcome: How Maxis AI Works for You
| Pain Point | AI Capability | Outcome |
|---|---|---|
| Repetitive SDTM/ADaM mapping and TLF generation consuming 60–70% of programmer time | Supervised AI agents execute structured programming outputs with defined validation checkpoints and expert review thresholds | 70% automation of programming effort; database lock accelerated by up to 50%; programmers refocused on high-value work |
| Interim analysis turn arounds taking 2–3 weeks, delaying adaptive decisions | Automated supervised pipelines generate survival curves, statistical computations, and interim outputs with audit traceability | 85–90% faster interim analysis (weeks → days); 2–3× faster decision cycles during trial reviews |
| Data quality issues detected late in review, increasing error risk under regulatory scrutiny | AI Workforce flags anomalies and inconsistencies earlier in the data flow under human-in-the-loop validation | 30–40% reduction in data queries; improved submission readiness and quality consistency |
All You Need to Know
It automates SDTM/ADaM mapping, TLF generation, and validation under expert oversight freeing your team for high-value statistical design.
Yes. Supervised automated pipelines produce computations and statistical outputs in days rather than weeks, with full audit traceability.
AI agents flag anomalies earlier in the data flow, reducing queries and improving submission readiness before issues reach reporting stages.
No. It removes repetitive workload so experienced programmers can focus on the complex, high-value work they can do.
Teams typically see 70% automation of repetitive programming effort and database lock accelerated by up to 50%.
Looking for Agentic AI for clinical trials?
Explore our Agentic AI Platform to see how AI agents are transforming study startup, data management, oversight, and regulatory submissions.