  In early clinical test evaluations the potential benefits of the introduction
of a new technology into the healthcare system are assessed in the challenging
situation of limited available empirical data. The aim of these evaluations is
to provide additional evidence for the decision maker, who is typically a
funder or the company developing the test, to evaluate which technologies
should progress to the next stage of evaluation. In this paper we consider the
evaluation of a diagnostic test for patients suffering from Chronic Obstructive
Pulmonary Disease (COPD). We describe the use of graphical models, prior
elicitation and uncertainty analysis to provide the required evidence to allow
the test to progress to the next stage of evaluation. We specifically discuss
inferring an influence diagram from a care pathway and conducting an
elicitation exercise to allow specification of prior distributions over all
model parameters. We describe the uncertainty analysis, via Monte Carlo
simulation, which allowed us to demonstrate that the potential value of the
test was robust to uncertainties. This paper provides a case study illustrating
how a careful Bayesian analysis can be used to enhance early clinical test
evaluations.
