BCEA (version 2.3-1.1)

plot.bcea: Summary plot of the health economic analysis

Description

Plots in a single graph the Cost-Effectiveness plane, the Expected Incremental Benefit, the CEAC and the EVPI

Usage

# S3 method for bcea
plot(x, comparison=NULL, wtp=25000, pos=FALSE, 
graph=c("base","ggplot2"), ...)

Arguments

x

A bcea object containing the results of the Bayesian modelling and the economic evaluation.

comparison

Selects the comparator, in case of more than two interventions being analysed. The value is passed to ceplane.plot, eib.plot and ceac.plot.

wtp

The value of the willingness to pay parameter. It is passed to ceplane.plot.

pos

Parameter to set the position of the legend. Can be given in form of a string, a single logical value, or a two-element vector with the respective relative positions on the x and y axis. Default as FALSE sets the legend position to the default one for each plot (see the details section), while TRUE puts it on the bottom of each plot. Changes will affect all the individual plots.

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".

...

Arguments to be passed to the methods ceplane.plot and eib.plot. Please see the manual pages for the individual functions. Arguments like size, ICER.size and plot.cri can be supplied to the functions in this way. In addition if graph="ggplot2" and the arguments are named theme objects they will be added to each plot.

Value

The function produces a plot with four graphical summaries of the health economic evaluation.

Details

The default position of the legend for the cost-effectiveness plane (produced by ceplane.plot) is set to c(1,1.025) overriding its default for pos=FALSE, since multiple ggplot2 plots are rendered in a slightly different way than single plots.

For more information see the documentation of each individual plot function.

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, ceplane.plot, eib.plot, ceac.plot, evi.plot

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            # does not produce graphical outputs
)
#
# Plots the summary plots for the "bcea" object m using base graphics
plot(m,graph="base")

# Plots the same summary plots using ggplot2
if(require(ggplot2)){
plot(m,graph="ggplot2")

##### Example of a customized plot.bcea with ggplot2
plot(m,
  graph="ggplot2",                                      # use ggplot2
  theme=theme(plot.title=element_text(size=rel(1.25))), # theme elements must have a name
  ICER.size=1.5,                                        # hidden option in ceplane.plot
  size=rel(2.5)                                         # modifies the size of k= labels
)                                                       #  in ceplane.plot and eib.plot
}
# }