TrialAgentBench: Evaluating AI Agents for Clinical-Trial Analysis and Long-Horizon Drug-Development Decisions
Abstract
Clinical drug development is high stakes. Small statistical errors propagate into trial design, participant exposure, and stop/advance decisions. As agents are increasingly used to support analysis and decision-making, more robust evaluations of their capabilities in clinical domains are needed. We introduce TrialAgentBench, a public benchmark in which agents must analyse participant-level evidence, choose defensible estimands and methods, quantify uncertainty, diagnose assumption failures, and carry those analysis decisions into downstream drug-development decisions. TrialEvalBench contains 400 patient-level analysis tasks across design, assumption, and context tiers. TrialDevBench contains 20 sequential development programmes across five scenario worlds, each starting from a confounded 10,000-patient observational extract and proceeding through asset nomination, endpoint and sample-size design, deterministic phase trials, phase analysis, and stop/advance decisions. The release spans 420 evaluated tasks or programmes, 528,660 simulated subjects, 77.6 million public tabular records, and 1.46 billion row-variable cells. Agents see only participant-facing data; grader-only files contain cell-appropriate targets, deterministic phase state, and scoring code. On TrialEvalBench, GPT-5.4 completes all 400 items but matches the required estimand/method class (e.g., log-HR versus RMST or risk-difference targets) for only 215/400 and produces an acceptable next-trial sample-size plan for 161/400. On TrialDevBench, GPT-5.4 and Claude Opus 4.7 complete all 60 seeded development programme runs per model, yet frequently nominate the wrong initial asset under confounding and choose evidence-supported stop/advance timing in fewer than one-third of programmes. Together, the two suites make clinical-trial decision quality measurable. Current agents can effectively execute familiar analyses, but fail to make the decisions those analyses exist to support.