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A frequentist approach to dynamic borrowing.

Ruilin Li1, Ray Lin2, Jiangeng Huang2

  • 1Institute for Computational and Mathematical Engineering, Stanford University, Stanford, California, USA.

Biometrical Journal. Biometrische Zeitschrift
|May 15, 2023
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Summary
This summary is machine-generated.

This study introduces a novel adaptive lasso method for clinical trials, improving how external control data is used with randomized data. The new approach offers competitive performance and addresses challenges in Bayesian dynamic borrowing methods.

Keywords:
adaptive lassodynamic borrowingexternal controlfrequentisthybrid controlreal-world data

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Area of Science:

  • Biostatistics
  • Clinical Trial Design
  • Real-World Evidence Integration

Background:

  • Growing interest in using external control data to augment randomized controlled trials (RCTs).
  • Real-world data (RWD) quality has improved, making it a viable option for external controls.
  • Direct pooling of external and randomized controls can introduce bias in treatment effect estimation.

Purpose of the Study:

  • To address computational and parameter tuning challenges in Bayesian dynamic borrowing methods.
  • To propose a novel frequentist dynamic borrowing approach using adaptive lasso.
  • To provide a method with a known asymptotic distribution for confidence intervals and hypothesis testing.

Main Methods:

  • Developed a frequentist interpretation of Bayesian commensurate prior borrowing.
  • Proposed a new dynamic borrowing approach utilizing adaptive lasso.
  • Conducted extensive Monte Carlo simulations to evaluate finite sample performance.

Main Results:

  • The adaptive lasso method demonstrated highly competitive performance compared to Bayesian approaches.
  • The proposed method provides a treatment effect estimate with a known asymptotic distribution.
  • Tuning parameter selection methods were thoroughly investigated.

Conclusions:

  • Adaptive lasso offers a viable frequentist alternative for dynamic borrowing in clinical trials.
  • The method effectively controls for bias and facilitates statistical inference.
  • Addresses practical challenges associated with Bayesian dynamic borrowing techniques.