  We describe in detail how to perform health economic cost-effectiveness
analyses (CEA) using the R package $\textbf{BCEA}$ (Bayesian Cost-Effectiveness
Analysis). CEA consist of analytic approaches for combining costs and health
consequences of intervention(s). These help to understand how much an
intervention may cost (per unit of health gained) compared to an alternative
intervention, such as a control or status quo. For resource allocation, a
decision maker may wish to know if an intervention is cost saving, and if not
then how much more would it cost to implement it compared to a less effective
intervention.
  Current guidance for cost-effectiveness analyses advocates the quantification
of uncertainties which can be represented by random samples obtained from a
probability sensitivity analysis or, more efficiently, a Bayesian model.
$\textbf{BCEA}$ can be used to post-process the sampled costs and health
impacts to perform advanced analyses producing standardised and highly
customisable outputs. We present the features of the package, including its
many functions and their practical application. $\textbf{BCEA}$ is valuable for
statisticians and practitioners working in the field of health economic
modelling wanting to simplify and standardise their workflow, for example in
the preparation of dossiers in support of marketing authorisation, or academic
and scientific publications.
