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This summary is machine-generated.

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.

Keywords:
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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.