BCEA (version 2.3-1.1)

ceaf.plot: Cost-Effectiveness Acceptability Frontier (CEAF) plot

Description

Produces a plot the Cost-Effectiveness Acceptability Frontier (CEAF) against the willingness to pay threshold

Usage

ceaf.plot(mce, graph=c("base","ggplot2"))

Arguments

mce

The output of the call to the function multi.ce

graph

A string used to select the graphical engine to use for plotting. Should (partial-)match the two options "base" or "ggplot2". Default value is "base".

Value

ceaf

A ggplot object containing the plot. Returned only if graph="ggplot2".

References

Baio, G., Dawid, A. P. (2011). Probabilistic Sensitivity Analysis in Health Economics. Statistical Methods in Medical Research doi:10.1177/0962280211419832.

Baio G. (2012). Bayesian Methods in Health Economics. CRC/Chapman Hall, London

See Also

bcea, multi.ce

Examples

# NOT RUN {
# See Baio G., Dawid A.P. (2011) for a detailed description of the 
# Bayesian model and economic problem
#
# Load the processed results of the MCMC simulation model
data(Vaccine)
# 
# Runs the health economic evaluation using BCEA
m <- bcea(e=e,c=c,          # defines the variables of 
                            #  effectiveness and cost
      ref=2,                # selects the 2nd row of (e,c) 
                            #  as containing the reference intervention
      interventions=treats, # defines the labels to be associated 
                            #  with each intervention
      Kmax=50000,           # maximum value possible for the willingness 
                            #  to pay threshold; implies that k is chosen 
                            #  in a grid from the interval (0,Kmax)
      plot=FALSE            # inhibits graphical output
)
#
# }
# NOT RUN {
mce <- multi.ce(m)          # uses the results of the economic analysis 
# }
# NOT RUN {
#
# }
# NOT RUN {
ceaf.plot(mce)              # plots the CEAF 
# }
# NOT RUN {
#
# }
# NOT RUN {
ceaf.plot(mce, graph="g")   # uses ggplot2 
# }
# NOT RUN {
# }
# NOT RUN {
# Use the smoking cessation dataset
data(Smoking)
m <- bcea(e,c,ref=4,intervention=treats,Kmax=500,plot=FALSE)
mce <- multi.ce(m)
ceaf.plot(mce)
# }