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On efficient posterior inference in normalized power prior Bayesian analysis.

Zifei Han1, Qiang Zhang1, Min Wang2

  • 1School of Statistics, University of International Business and Economics, Beijing, China.

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|March 24, 2023
PubMed
Summary
This summary is machine-generated.

This study introduces an efficient Bayesian framework for the normalized power prior, simplifying clinical trial data analysis. The method enables adaptive borrowing of historical data, improving flexibility and practicality in complex models.

Keywords:
clinical trialsdiscountinghistorical borrowingimportance samplingpower priors

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Area of Science:

  • Biostatistics
  • Clinical Trial Design
  • Bayesian Inference

Background:

  • The power prior is a common method for incorporating historical data in clinical trials, using a parameter to quantify heterogeneity.
  • A fully Bayesian approach involves a hyperprior for the power parameter, requiring a complex normalizing factor for the normalized power prior.
  • Calculating this normalizing factor is computationally intensive, limiting the practical application of normalized power priors in complex models.

Purpose of the Study:

  • To develop an efficient computational framework for implementing the normalized power prior in clinical studies.
  • To enable the use of a random power parameter with adaptive data borrowing capabilities in general Bayesian models.
  • To demonstrate the numerical efficiency and practical utility of the proposed method.

Main Methods:

  • The study proposes a novel sampling procedure that bypasses the need to compute the normalizing factor.
  • The method involves sampling directly from the power prior distribution with respect to the power parameter and the parameter of interest.
  • The framework is implemented and evaluated using extensive simulation studies, a toxicological study, and an oncology study.

Main Results:

  • The proposed method significantly improves computational efficiency compared to traditional implementations of the normalized power prior.
  • The framework successfully facilitates the use of a random power parameter, allowing for adaptive borrowing of information from historical data.
  • Simulations and real-world study applications confirm the numerical efficiency and practical feasibility of the approach.

Conclusions:

  • The developed framework provides a computationally efficient and practical solution for implementing normalized power priors in clinical research.
  • This advancement allows for more flexible and robust Bayesian analyses of clinical trials by enabling adaptive borrowing of historical data.
  • The method has broad applicability in various complex modeling scenarios within biostatistics and clinical trial design.