Large Universe Models in clinical trials

What it ingests

Enrolment and screening logs, site performance and query rates, protocol deviations, adverse-event reports, monitoring visit findings, competing-trial registries, and supply and shipment data.

The belief that matters

Whether this trial will read out on time, at power — a belief that depends on enrolment rate, dropout, site quality and data completeness, all of which move continuously.

Operational, not statistical

The efficacy analysis is governed by a statistical plan and should stay that way; unblinding beliefs about outcomes is not what this is for. The application is operational, where continuous revision is uncontroversial and valuable.

A site whose query rate has doubled, whose enrolment has stalled, and which has had two protocol deviations is a belief that should have moved weeks before the monitoring visit.

Competitive enrolment

Trials compete for the same patients. A competing trial opening sites in your regions is direct evidence about your enrolment forecast, and it is publicly registered.

Limits

Anything touching blinded data requires careful separation. The correct boundary is operational metrics only, with efficacy analysis left to the pre-specified plan.