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Statistical Guardrails for Hybrid-Controlled Trials: Robust to Confounding and Between-Study Heterogeneity
Di Ran1, Fanni Zhang2, Kristine Broglio2
1Oncology Biometrics Statistical Innovation, 1 MedImmune Way, AstraZeneca, Gaithersburg, MD, 20878, USA. di.ran@astrazeneca.com.
Therapeutic Innovation & Regulatory Science
|March 15, 2026
Summary
Hybrid-controlled trials leverage external data to boost drug development efficiency. A two-step strategy combining propensity score balancing and Bayesian dynamic borrowing offers the best balance of precision and bias control for valid inference.
Area of Science:
- Biostatistics
- Clinical Trial Design
- Real-World Evidence
Background:
- External data sources (trials, registries, real-world data) can enhance drug development efficiency.
- Hybrid-controlled trial designs integrate external data with randomized controlled arms, promising reduced concurrent enrollment and maintained internal validity.
- Regulatory acceptance of hybrid designs is hindered by concerns regarding potential bias from data source discrepancies.
Purpose of the Study:
- To evaluate statistical methods for mitigating bias in hybrid-controlled trials using external data.
- To identify robust methodologies for valid inference in hybrid trial designs.
- To assess the performance of various statistical approaches under different confounding and heterogeneity scenarios.
Main Methods:
- Assessed eight statistical methods designed to address discrepancies between external and trial data.
- Applied methods to a large clinical trial case study.
- Conducted a comprehensive simulation study with continuous outcomes, varying confounding, data heterogeneity, and the number of external data sources.
Main Results:
- The two-step strategy, involving propensity score-based balancing followed by Bayesian dynamic borrowing, demonstrated a superior trade-off between precision and bias control.
- This approach proved effective across various simulation scenarios, including different levels of measured/unmeasured confounding and data heterogeneity.
- Consistent performance was observed regardless of the number of external data sources utilized.
Conclusions:
- The combination of propensity score balancing and Bayesian dynamic borrowing provides a robust method for hybrid-controlled trial implementation.
- This approach enables valid inference and bias mitigation when using fit-for-purpose external data.
- The findings support broader adoption of hybrid trial designs beyond current limited applications.
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