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Adaptively leverage multiple real-world data sources for treatment effect estimation based on similarity.

Meihua Long1, Jiali Song1, Zhiwei Rong1

  • 1Department of Biostatistics, Peking University, Beijing, China.

Journal of Biopharmaceutical Statistics
|April 1, 2024
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Summary

This study introduces Tree-based Monte Carlo (TMC), a novel method for integrating real-world data (RWD) in clinical trials. TMC dynamically weights RWD sources by similarity to clinical trial data, improving treatment effect estimation accuracy.

Keywords:
Gaussian processhierarchical clusteringinformation borrowingpropensity scorereal-world data

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Area of Science:

  • Biostatistics
  • Clinical Trial Methodology
  • Real-World Evidence (RWE)

Background:

  • Growing use of real-world data (RWD) in medical product development and evaluation.
  • Lack of standardized methods for quantifying information from external RWD sources.
  • Need for robust approaches to integrate diverse RWD into clinical trial analysis.

Purpose of the Study:

  • To propose and evaluate a novel study design methodology, Tree-based Monte Carlo (TMC).
  • To dynamically integrate patients from various RWD sources based on similarity to clinical trial data.
  • To improve the accuracy of treatment effect calculations by appropriately weighting RWD.

Main Methods:

  • Development of a propensity score to measure similarity between clinical trial data and RWD.
  • Construction of a hierarchical clustering tree based on similarity metrics to group RWD sources.
  • Application of Gaussian process methodology within the clustering framework to synthesize treatment effects.

Main Results:

  • The proposed clustering tree effectively identifies and quantifies similarity between RWD sources and clinical trial data.
  • Data sources with higher similarity receive greater weight in treatment effect estimation.
  • The TMC method demonstrated reduced bias and closer estimates to the true value compared to the meta-analytic predictive prior (MAP) method.

Conclusions:

  • Tree-based Monte Carlo (TMC) offers a robust and adaptable framework for integrating RWD in clinical studies.
  • The weighting mechanism based on data similarity enhances the reliability of treatment effect estimation.
  • TMC provides a statistically sound alternative to existing methods for leveraging RWD in medical product evaluation.