Designing Efficient Hybrid and Single-Arm Trials: External Control Borrowing and Sample Size Calculation
Yujing Gao, Xiang Zhang, Shu Yang

TL;DR
This paper develops new sample size calculation methods for hybrid and single-arm clinical trials that incorporate external control data, reducing required sample sizes while maintaining statistical validity.
Contribution
It introduces a novel framework for designing hybrid and single-arm trials using external controls, including variance estimation and power analysis techniques.
Findings
Proposed designs maintain valid type I error rates.
Achieve target power with fewer subjects than traditional RCTs.
Validated through simulations and real data application.
Abstract
External controls (ECs) from historical trials or real-world data have gained increasing attention as a way to augment hybrid and single-arm trials, especially when balanced randomization is infeasible. While most existing work has focused on post-trial inference using ECs, their role in prospective trial design remains less explored. We address this gap by focusing on the sample size determination and power analysis for an experimental design problem that encompasses standard randomized controlled trials (RCTs), hybrid trials, and single-arm trials. Building on estimators derived from the efficient influence function, we develop hybrid and single-arm design strategies that leverage comparable EC data to reduce the required sample size of the current study. We derive asymptotic variance expressions for these estimators in terms of interpretable, population-level quantities and introduce…
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Taxonomy
TopicsStatistical Methods in Clinical Trials · Advanced Causal Inference Techniques · Statistical Methods and Inference
