🤖 AI Summary
This study addresses the lack of a systematic framework for evaluating the validity, benefit, and robustness of borrowing historical data in external control arm trials. The authors propose a “three-pillar, eight-module” sensitivity analysis framework that integrates eight analytical techniques—heterogeneity diagnostics, source influence analysis, no-borrowing benchmarks, effective sample size quantification, prior sensitivity assessment, tipping point analysis, alternative borrowing methods, and model structure sensitivity—to comprehensively address these core concerns. This work presents the first structured, methodology-agnostic template compatible with both Bayesian and frequentist paradigms and adaptable to diverse data types. Through simulated evidence synthesis mimicking real-world regulatory review scenarios, the framework demonstrates practical feasibility and reproducibility, offering a standardized paradigm for sensitivity analysis in external control trials and helping to fill a critical gap in regulatory guidance.
📝 Abstract
Externally controlled trials (ECTs), including single-arm studies augmented with historical data and hybrid randomized designs with partial external augmentation, are increasingly used when concurrent randomized controls are infeasible or unethical. Regulatory guidance from the FDA, EMA, and NMPA calls for sensitivity analysis of borrowing assumptions, yet provides no structured template for which analyses to run or how to interpret them together.
We propose a three-pillar framework organized around three questions: was the borrowing appropriate, did it contribute meaningful value, and are the conclusions robust to perturbation? The framework comprises eight modular analyses covering heterogeneity diagnostics, source influence, no-borrowing references, effective sample size, prior sensitivity, tipping points, alternative borrowing methods, and structural model sensitivity. It is method-agnostic and applies to both Bayesian and frequentist borrowing in patient-level or hybrid settings.
We illustrate the framework using simulated data that mimic a hybrid evidence synthesis from a historical approval of ethnic-bridging submission under a real-world-evidence regulatory pathway. That original analysis combined individual patient data from a global pivotal study and a regional real-world study with aggregate data from two published cohorts, fitted via a Bayesian longitudinal model with ethnic-difference parameters. The worked example provides a reproducible template for sensitivity analysis in ECT submissions.