Robust Estimation and Inference with Selective Borrowing in Hybrid Controlled Trials: A Tutorial with SelectiveIntegrative and intFRT

2026-06-30

AI Summary

This study addresses the challenge of integrating external control data in hybrid controlled trials, where covariate shift and outcome drift can compromise validity. The authors propose a comprehensive analytical framework that explicitly defines the target parameter and identification assumptions, and combines subject alignment, matching strategies, and selective borrowing to enhance statistical efficiency while preserving inferential validity. The approach integrates asymptotic inference with randomization-based testing and is fully reproducible through the SelectivelyIntegrative and intFRT software packages. In simulations emulating the CALGB 9633 lung cancer trial, the method maintains Type I error control while substantially improving estimation precision, demonstrating its practical utility and robustness for incorporating real-world evidence into clinical trial analysis.

Abstract

Hybrid controlled trials (HCTs) augment randomized controlled trials (RCTs) with external controls (ECs) to improve statistical efficiency when RCTs face limited sample sizes, slow accrual, or ethical constraints. However, valid use of ECs requires careful adjustment for covariate shift and outcome drift, as inappropriate borrowing may introduce bias and compromise inference. This tutorial provides a practical workflow for estimation and inference in HCTs. We first present a statistical analysis roadmap covering estimands, identification assumptions, eligibility alignment, matching, full and selective borrowing strategies, and both asymptotic inference and randomization tests. We then demonstrate step-by-step implementation using the SelectiveIntegrative and intFRT packages. The workflow is illustrated using a synthetic lung cancer dataset included in the intFRT package that mimics the CALGB 9633 trial and ECs from the National Cancer Database. The tutorial aims to help applied statisticians conduct transparent, interpretable, and reproducible HCT analyses that improve efficiency while maintaining valid inference.

Problem

• hybrid controlled trials
• external controls
• covariate shift
• outcome drift
• valid inference

Innovation

• selective borrowing
• hybrid controlled trials
• external controls
• randomization tests
• covariate shift adjustment