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

TL;DR

This paper reviews the design and methodology of externally controlled trials (ECTs), emphasizing causal inference, data borrowing strategies, and their applications in various clinical settings.

Contribution

It provides a structured causal framework for ECTs, synthesizing recent Bayesian and frequentist methods, and discusses their trade-offs, software, and future research directions.

Findings

Clarifies causal estimands and assumptions in ECTs.

Compares Bayesian and frequentist approaches and their software.

Highlights challenges like covariate shift and outcome drift.

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.