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

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.