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Distributions of parameters for uncertainty analysis cannot be defined without using prior information.

Hendriek C Boshuizen1

  • 1Department of Statistics and Mathematical Modelling, National Institute of Public Health and the Environment, Bilthoven, The Netherlands. hendriek.boshuizen@rivm.nl

Value in Health : the Journal of the International Society for Pharmacoeconomics and Outcomes Research
|March 30, 2010
PubMed
Summary

Defining input distributions for relative risk in probabilistic sensitivity analysis (PSA) is inherently Bayesian. Barendregt's proposed non-Bayesian method is epistemologically impossible, requiring explicit justification of prior distributions.

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Area of Science:

  • Decision Analysis
  • Health Economics
  • Biostatistics

Background:

  • Probabilistic sensitivity analysis (PSA) often requires defining input distributions for model parameters, such as relative risk (RR).
  • Barendregt proposed a method for defining RR input distributions, claiming it to be "non-Bayesian" and thus not requiring prior knowledge.
  • This claim challenges conventional approaches in uncertainty quantification.

Purpose of the Study:

  • To epistemologically evaluate Barendregt's proposed non-Bayesian method for defining RR input distributions.
  • To examine the underlying assumptions and implications of the method within the framework of statistical inference.

Main Methods:

  • Epistemological examination of the assumptions underpinning Barendregt's method.
  • Analysis of the method's relationship to Bayesian principles and bootstrapping techniques.

Main Results:

  • The method, despite claims, is Bayesian in character, implying an unappealing prior distribution.
  • Bootstrapping can offer non-Bayesian results, but Barendregt's approach is not a form of bootstrapping.

Conclusions:

  • Defining probability distributions for model parameters like RR is epistemologically impossible without a Bayesian framework.
  • Transparency regarding prior distributions and their justification is essential for any proposed method of defining input distributions.