MENTOR: An Evidence-Informed Large Language Model-Based Multi-Agent System for Efficient Selection of Institution-Specific Negative Control Outcomes

Objective Negative control outcomes (NCOs) help detect and correct systematic error in observational studies, yet their selection remains largely manual. The Common Evidence Model (CEM) can generate candidate lists, but researchers must still screen them for plausibility as true nulls and for sufficient observation in each institution's data. We developed and evaluated MENTOR (Multi-agent Evidence-informed Negative conTrol Outcome selection with Reasoning), a multi-agent system of large language models (LLMs) that combines CEM evidence with LLM reasoning to select institution-specific NCOs. Me

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