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

Abstract

Background: Barendregt proposes a method to define an input distribution for a relative risk, as used in the probabilistic sensitivity analysis (PSA), and suggests the method is "non-Bayesian" and thus does not require prior knowledge on the probability distribution of the relative risk. Aims: To discuss the method from an epistemologically viewpoint. Materials and Methods: Examination of the underlying assumptions. Results: The method, like other methods to define input distributions, is Bayesian in character and the implied prior distribution is not very appealing. Discussion: Bootstrapping offers possibilities to be non-Bayesian, but at the price of giving only non-Bayesian answers. The method presented by Barendregt, however, can not be seen as a bootstrapping approach. Conclusion: Defining the distribution of a RR or any other model parameter without being a Bayesian is epistemologically impossible. This means that being explicit on prior distributions used for deriving those distributions, and justifying them, is a necessary part of suggesting new ways to define distributions. © 2010, International Society for Pharmacoeconomics and Outcomes Research (ISPOR).