Zero-Shot External-Control Construction for Single-Arm Clinical Trials

Oncology single-arm clinical trials (SCTs) evaluate treatments without a control group. External controls constructed from real-world data (RWD) enable comparative evaluation, but require trial-specific elements governing patient inclusion, follow-up initiation, and historical borrowing. Existing methods largely leave these elements to manual specification. Additionally, the absence of a dedicated benchmark has restricted prior evaluations to individual trials or single cancer settings, leaving unclear whether the methods can be transferred to unseen trials. To address these gaps, we introduce SCT-Bench, the first benchmark for this setting, comprising 9,163 clinical trials organized into seven complementary tasks that evaluate protocol extraction, feature ascertainability, control-arm similarity, outcome recovery, survival-curve recovery, treatment-effect recovery, and drug-efficacy recovery. We formulate the SCT evaluation task as zero-shot external-control construction: selecting construction elements for an unseen SCT using only observable evidence. Withheld randomized control arms provide reference outcomes and populations for evaluating both trial-specific element determination and outcome recovery. Such zero-shot construction requires transferring prior methodological knowledge into executable trial-specific actions. Building on this idea, we propose Task-Adaptive External-Control Construction (TAEC), an LLM-based knowledge-to-action framework. Skill-guided planning converts such prior knowledge, together with the target protocol and RWD context, into ten structured construction elements. Data-grounded construction identifies candidate donors and assigns weights through partial optimal transport. Outcome-free refinement verifies the construction without target-trial outcomes, revises unsupported elements and their supporting skills, and updates the skill library within the current target trial. Across SCT-Bench evaluations with two RWD sources, TAEC consistently improves external-control construction and downstream outcome recovery over existing baselines. Further experiments show that transferring TAEC's selected time-zero choice into otherwise unchanged baseline pipelines yields large performance gains. These results identify upstream task instantiation as a major source of generalization beyond estimator choice.