BCEA: An R package to perform Bayesian Cost-Effectiveness Analysis
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 \(t=0\)?
BCEA provides a set of functions that can be used to produce a standardised analysis. The package has been created to complement my book on Bayesian methods in health economics where it features heavily (some discussion of the package in the blog can be found here, here, here and here). A discussion of the changes in release 2.0 is here. This is a presentation given at European Conference on Health Economics (Zurich, 2012).
BCEA produces a synthesis of the decision process given the current evidence and uncertainty, as well as several indicators that can be used to perform Probabilistic Sensitivity Analysis to parameter and model uncertainty. These include the Cost-Effectiveness Acceptability Curve and the analysis of the Expected Value of Information, that can be used to prioritize research. Examples of the output of the function are given below.
There are two ways of installing
BCEA. A “stable” version (currently 2.3.1) is packaged and available from CRAN. So you can simply type on your R terminal
The second way involves using the “development” version of
BCEA - this will usually be updated more frequently and may be continuously tested. On Windows machines, you need to install a few dependencies, including Rtools first, e.g. by running
pkgs <- c("MASS","Rtools","devtools") repos <- c("https://cran.rstudio.com", "https://www.math.ntnu.no/inla/R/stable") install.packages(pkgs,repos=repos,dependencies = "Depends")
before installing the package using
Under Linux or MacOS, it is sufficient to install the package via