Clinical-data agent study separates plausible plans from correct execution

Discuss unreliable analysis handoffs with principal investigators, biostatisticians and research IT. Ask who verifies generated code, how review time is recorded, and which decisions require statistical sign-off. A bounded engagement could evaluate one low-risk analytical workflow against an independently reviewed reference. The value hypothesis is reducing drafting effort while retaining research quality, subject to a net-time comparison that includes correction and review. Do not promise autonomous biostatistics, generalized accuracy or savings from this small pilot. Confirm that reviewers have capacity before proposing broader adoption.

Build a sandbox with approved inputs, pinned analytical libraries and restricted execution privileges. Preserve exact model configuration and every generated file. Compare planned variables, boundary conditions, units and diagnostics with an independently prepared specification, then reconcile logs and narrative. Validate denied data access and controlled export. Include intentionally difficult edge cases and a held-out local dataset to address the study's narrow setting. A useful proof of value must establish logical correctness, not merely a successful process exit; do not treat agreement with an imperfect reference as sufficient.

The research-methods lead should own acceptance, supported by an R or Python engineer and data steward. Implement a documented review workflow, reviewer training and accessible reporting templates. Dependencies include usable reference analyses, approved data and staff time for adjudication. Proposed acceptance criteria are complete artifact traceability, correct prespecified edge cases and no unresolved material statistical error before release. Measure total analyst and reviewer time against the current workflow. Pause expansion when reviewers cannot explain discrepancies; risks include automation bias, incomplete diagnostics and unrecorded model changes.