With [Anna Heath](https://sites.google.com/site/annaheathstats/) and [Ioanna Manolopoulou](http://www.homepages.ucl.ac.uk/~ucakima/)

With [Andrea Gabrio](https://sites.google.com/site/agabriostats/), [Alexina Mason](https://www.lshtm.ac.uk/aboutus/people/mason.alexina), [Rachael Hunter](https://iris.ucl.ac.uk/iris/browse/profile?upi=RMHUN48) and Xiaoxiao Ling

With Katrin Haeussler, [Ardo van den Hout](https://www.ucl.ac.uk/statistics/people/ardovandenhout), [Giampiero Favato](http://www.kingston.ac.uk/staff/profile/professor-giampiero-favato-121/), [Francesco S Mennini](http://www.ceistorvergata.it/area.asp?a=542&oc=921&d=1292) and Alessandro Capone

[Antonio Remiro-Azocar](http://remiroazocar.com/), [Gianluca Baio](www.statistica.it/gianluca) and [Anna Heath](https://sites.google.com/site/annaheathstats/)

With Zhaojing Che

Check out my book (published with CRC - available also on Amazon, in ebook format too)
Table of contents
Preface
Get a promotional code - save 20% when ordering online from CRC website Read a sample for free BCEA - an R package to run Bayesian health economic evaluations (used throughout the book and specifically in the examples) Some discussion of the book in the blog can be found here, here, here and here The book has received excellent reviews, for example by Patrick Graham

BCEA is a R library specifically designed to post-process the result of a Bayesian health economic evaluation. Typically, this consists in the estimation of a set of relevant parameters that can be combined to produce an estimation of suitable measures of cost (\(c\)) and clinical benefits (\(e\)) associated with an intervention. Within the Bayesian framework, this amounts to estimating a posterior distribution for the pair \((e,c)\).
Health economic evaluations then proceed by computing some relevant summaries of the resulting decision process: is the innovative intervention \(t=1\) more “cost-effective” than the standard intervention \(t=0\)?

Introduction The intention of this vignette is to show how to plot different styles of cost-effectiveness acceptability curves using the BCEA package.
Two interventions only This is the simplest case, usually an alternative intervention (\(i=1\)) versus status-quo (\(i=0\)).
The plot show the probability that the alternative intervention is cost-effective for each willingness to pay, \(k\),
\[ p(NB_1 \geq NB_0 | k) \mbox{ where } NB_i = ke - c \]

There are several arguments passed to bcea() to specify the form of the analysis. These are
bcea(e, c, ref = 1, interventions = NULL, .comparison = NULL, Kmax = 50000, wtp = NULL, plot = FALSE) Those of interest here are:
ref is the reference intervention group to compare against the other groups. .comparisons are the groups to compare against ref. The default is all of the non-ref groups.

Check out our book on the BCEA R package published by Springer in the UseR! series. This book is co-authored by myself, Andrea Berardi and Anna Heath.
Table of contents Preface Get a promotional code - save 20% when ordering online from Springer website Read a sample for free BCEA - an R package to run Bayesian health economic evaluations (used throughout the book and specifically in the examples) Journal editors, journalists or bloggers can request a free Online Review Copy of the book.