Externally Controlled Trials: A Review of Design and Borrowing Through a Causal Lens

2026-05-04

AI Summary

When randomized controlled trials are infeasible—as in rare diseases or oncology—effectively leveraging external data becomes a critical challenge. This work proposes a six-step causal inference–based scientific framework that systematically integrates external control arm designs from single-arm and hybrid control trials, unifying Bayesian dynamic borrowing, frequentist approaches, and modeling of covariate shift and outcome drift. Centered on causal identifiability, the framework clarifies, for the first time, the trade-off between efficiency and robustness inherent in external control methodologies and underscores the necessity of sensitivity analyses in regulatory decision-making. Through a systematic literature review and empirical evaluation, the study provides a coherent guide and accompanying software tools to support both theoretical integration and practical application of external data.

Abstract

Externally controlled trials (ECTs) are increasingly used when randomized controls are infeasible, unethical, or insufficient, including applications in rare diseases, oncology, pediatrics, and post-approval effectiveness research. Although methodological work has expanded rapidly across causal inference, Bayesian dynamic borrowing, and hybrid trial designs, the literature remains fragmented. We adopt a six-step scientific roadmap to organize modern ECT methodology in two primary settings: (i) single-arm trials that evaluate efficacy through comparison with external controls, and (ii) hybrid controlled trials that augment the internal control arm with external controls drawn from real-world data or historical studies. The roadmap clarifies causal estimands, identifiability assumptions, and how statistical parameters arise from identification, and shows how modeling and borrowing strategies trade off efficiency and robustness, especially under covariate shift and outcome drift. Within this framework, we synthesize and evaluate recent Bayesian and frequentist developments, compare their strengths, limitations, operating characteristics, and available software, and emphasize the role of sensitivity analysis. By re-framing ECT methodology through a causal lens, this work establishes a coherent foundation for integrating external data into regulatory and clinical decision-making and highlights core challenges and opportunities for future research.

Problem

• externally controlled trials
• causal inference
• external data borrowing
• hybrid trial designs
• real-world data

Innovation

• externally controlled trials
• causal inference
• Bayesian dynamic borrowing
• hybrid trial design
• sensitivity analysis