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Testing for treatment effect twice using internal and external controls in clinical trials.
Yanyao Yi1, Ying Zhang1, Yu Du1
1Global Statistical Sciences, Eli Lilly and Company, Indianapolis, IN 46285, United States.
Journal of Causal Inference
|June 29, 2026
Summary
Leveraging external controls in clinical trials can reduce costs and increase patient access to new treatments. A new sensitivity analysis method quantifies potential unmeasured bias when using external controls, ensuring reliable study conclusions.
Area of Science:
- Biostatistics
- Clinical Trial Design
- Health Economics
Background:
- External controls, including real-world data, offer potential cost savings and broader access to novel treatments in clinical trials.
- However, the lack of randomization in external controls can introduce unmeasured biases, potentially affecting study outcomes.
- Existing methods may struggle to account for these unmeasured differences between randomized controlled trial (RCT) patients and external controls.
Purpose of the Study:
- To propose a novel sensitivity analysis approach for quantifying unmeasured bias when incorporating external controls into clinical trials.
- To develop a combined testing procedure that balances the benefits of using external controls with the risks of unmeasured bias.
- To provide a robust method for data fusion problems, ensuring reliable treatment effect estimation.
Main Methods:
- Developed a sensitivity analysis to determine the magnitude of unmeasured bias required to change study conclusions.
- Proposed a combined testing procedure performing analyses with and without external controls, with a small correction for multiple testing.
- Utilized the joint distribution of test statistics for a robust statistical approach.
Main Results:
- The sensitivity analysis quantifies the impact of potential unmeasured biases, allowing researchers to assess the reliability of conclusions drawn from external controls.
- The combined testing procedure offers substantial power gains when external controls are beneficial, while minimizing power loss otherwise.
- The method demonstrated effectiveness in theoretical evaluations, power calculations, and application to a real clinical trial.
Conclusions:
- The proposed sensitivity analysis and combined testing procedure provide a reliable framework for leveraging external controls in clinical trials.
- This approach enhances the validity of studies using external data by addressing potential unmeasured biases.
- The method supports informed decision-making in data fusion, optimizing the use of external controls for robust clinical trial evidence.
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