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Bayesian treatment comparison using parametric mixture priors computed from elicited histograms.

Peter F Thall1, Moreno Ursino2, Véronique Baudouin3

  • 11 Department of Biostatistics, The University of Texas MD Anderson Cancer Center, USA.

Statistical Methods in Medical Research
|September 6, 2017
PubMed
Summary

This study introduces a Bayesian method for creating prior beliefs about treatment effects using expert physician input. The approach was applied to a trial for childhood idiopathic nephrotic syndrome, enhancing clinical trial design.

Keywords:
Bayesian inferenceclinical trialmixture modelpediatric medicineprior elicitationrare diseases

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Area of Science:

  • Biostatistics
  • Clinical Trial Design
  • Bayesian Inference

Background:

  • Expert opinion is crucial for informing clinical trial priors, especially in rare diseases.
  • Eliciting and quantifying expert knowledge in a structured manner presents a methodological challenge.
  • Parametric priors can improve the efficiency and interpretability of Bayesian analyses in clinical trials.

Purpose of the Study:

  • To propose a Bayesian methodology for constructing parametric priors on treatment effect parameters.
  • To utilize graphical information elicited from expert physicians for prior construction.
  • To apply and evaluate the methodology in a randomized trial for childhood idiopathic nephrotic syndrome.

Main Methods:

  • A Bayesian approach using histograms of treatment parameters elicited from physicians.
  • Fitting marginal priors to physician-generated histograms using location and precision hyperparameters.
  • Constructing a bivariate prior by averaging marginals over a latent physician effect distribution.
  • Developing an overall prior as a mixture of individual physician priors.
  • Evaluating the methodology via simulation and a sensitivity analysis framework.

Main Results:

  • The proposed methodology successfully constructs parametric priors from elicited physician data.
  • Simulation studies demonstrate the performance of different versions of the Bayesian approach.
  • A sensitivity analysis framework is established for assessing prior impact on posterior inferences.
  • The method is illustrated using data from a trial for idiopathic nephrotic syndrome.

Conclusions:

  • The developed Bayesian methodology provides a robust framework for incorporating expert knowledge into clinical trial priors.
  • This approach enhances the informativeness and transparency of Bayesian analyses in medical research.
  • The methodology is particularly valuable for trials with limited prior data, such as those for rare pediatric conditions.