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

Value

Author(s)

Gianluca Baio, Andrea Berardi

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

# 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
)
#

mce <- multi.ce(m)          # uses the results of the economic analysis 

#

ceaf.plot(mce)              # plots the CEAF 

#

ceaf.plot(mce, graph="g")   # uses ggplot2 



# 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)

Results

R version 3.3.1 (2016-06-21) -- "Bug in Your Hair"
Copyright (C) 2016 The R Foundation for Statistical Computing
Platform: x86_64-pc-linux-gnu (64-bit)

R is free software and comes with ABSOLUTELY NO WARRANTY.
You are welcome to redistribute it under certain conditions.
Type 'license()' or 'licence()' for distribution details.

R is a collaborative project with many contributors.
Type 'contributors()' for more information and
'citation()' on how to cite R or R packages in publications.

Type 'demo()' for some demos, 'help()' for on-line help, or
'help.start()' for an HTML browser interface to help.
Type 'q()' to quit R.

> library(BCEA)
> png(filename="/home/ddbj/snapshot/RGM3/R_CC/result/BCEA/ceaf.plot.Rd_%03d_medium.png", width=480, height=480)
> ### Name: ceaf.plot
> ### Title: Cost-Effectiveness Acceptability Frontier (CEAF) plot
> ### Aliases: ceaf.plot
> ### Keywords: Health economic evaluation Multiple comparison
> 
> ### ** Examples
> 
> # 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
+ )
> #
> ## No test: 
> mce <- multi.ce(m)          # uses the results of the economic analysis 
> ## End(No test)
> #
> ## No test: 
> ceaf.plot(mce)              # plots the CEAF 
> ## End(No test)
> #
> ## No test: 
> ceaf.plot(mce, graph="g")   # uses ggplot2 
> ## End(No test)
> 
> ## No test: 
> # 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)
> ## End(No test)
> 
> 
> 
> 
> 
> dev.off()
null device 
          1 
>