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Propensity score-integrated Bayesian prior approaches for augmented control designs: a simulation study.

Xi Wang1, Leah Suttner2, Thomas Jemielita2

  • 1Department of Public Health Sciences, College of Medicine, The Pennsylvania State University, Hershey, Pennsylvania, USA.

Journal of Biopharmaceutical Statistics
|December 23, 2021
PubMed
Summary

This study introduces a novel method combining propensity score adjustment with Bayesian commensurate priors to augment limited control arms in clinical trials. This approach enhances statistical properties for more reliable drug development, especially in rare diseases.

Keywords:
Bayesian augmented controlPS weightingcommensurate priorhistorical controlpropensity scorereal-world data

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Area of Science:

  • Clinical Trial Design
  • Biostatistics
  • Pharmaceutical Development

Background:

  • Drug development faces challenges with high costs and limited participant availability, particularly for rare or pediatric diseases.
  • Leveraging historical control data is a strategy to maximize resource utilization, but its appropriateness is difficult to assess without a current control arm.
  • Single-arm trials relying solely on historical controls lack robust comparative evaluation.

Purpose of the Study:

  • To evaluate a hybrid approach for clinical trials: a minimal control arm augmented with historical data.
  • To propose and assess methods integrating propensity score (PS) adjustment with Bayesian commensurate priors for adaptive information borrowing.
  • To inform future trial designs by evaluating the performance of different PS adjustment and Bayesian prior methods.

Main Methods:

  • Utilized propensity score (PS) adjustment methods, including propensity weighting and matching, to minimize bias in non-randomized data.
  • Integrated PS adjustment with Bayesian commensurate priors for adaptive borrowing of historical control data.
  • Conducted simulation studies to evaluate the performance of various PS adjustment techniques and Bayesian priors for augmented controls.

Main Results:

  • Propensity weighting or matching, when combined with the commensurate prior, demonstrated reasonable statistical properties across various simulated scenarios.
  • The proposed methods showed effectiveness in augmenting a minimal control arm with historical data.
  • The developed methods were successfully applied to a real-world clinical trial with a binary outcome.

Conclusions:

  • The integration of propensity score adjustment with Bayesian commensurate priors offers a viable strategy for augmenting control arms in clinical trials.
  • This hybrid approach improves the reliability of control arm comparisons, especially when historical data is leveraged.
  • The findings provide valuable guidance for designing more efficient and statistically sound clinical trials, particularly in resource-constrained settings.