  Bayesian modelling for cost-effectiveness data has received much attention in
both the health economics and the statistical literature in recent years.
Cost-effectiveness data are characterised by a relatively complex structure of
relationships linking the suitable measure of clinical benefit (\eg QALYs) and
the associated costs. Simplifying assumptions, such as (bivariate) normality of
the underlying distributions are usually not granted, particularly for the cost
variable, which is characterised by markedly skewed distributions. In addition,
individual-level datasets are often characterised by the presence of structural
zeros in the cost variable.
  Hurdle models can be used to account for the presence of excess zeros in a
distribution and have been applied in the context of cost data. We extend their
application to cost-effectiveness data, defining a full Bayesian model which
consists of a selection model for the subjects with null costs, a marginal
model for the costs and a conditional model for the measure of effectiveness
(conditionally on the observed costs). The model is presented using a working
example to describe its main features.
