Plots the probability that each intervention is the most cost-effective

This function is deprecated. Use ceac.plot() instead. Plots the probability that each of the n_int interventions being analysed is the most cost-effective.

Usage

mce.plot(mce, pos = c(1, 0.5), graph = c("base", "ggplot2"), ...)

Arguments

mce: The output of the call to the function multi.ce().
pos: Parameter to set the position of the legend. Can be given in form of a string (bottom|top)(right|left) for base graphics and bottom|top|left|right for ggplot2. It can be a two-elements vector, which specifies the relative position on the x and y axis respectively, or alternatively it can be in form of a logical variable, with TRUE indicating to use the first standard and FALSE to use the second one. Default value is c(1,0.5), that is on the right inside the plot area.
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".
...: Optional arguments. For example, it is possible to specify the colours to be used in the plot. This is done in a vector color=c(...). The length of the vector colors needs to be the same as the number of comparators included in the analysis, otherwise BCEA will fall back to the default values (all black, or shades of grey)

Value

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

References

Baio G, Dawid aP (2011). “Probabilistic sensitivity analysis in health economics.” Stat. Methods Med. Res., 1--20. ISSN 1477-0334, doi:10.1177/0962280211419832 , https://pubmed.ncbi.nlm.nih.gov/21930515/.

Baio G (2013). Bayesian Methods in Health Economics. CRC.

Author

Gianluca Baio, Andrea Berardi

Examples


# See Baio G., Dawid A.P. (2011) for a detailed description of the 
# Bayesian model and economic problem

if (FALSE) {
# Load the processed results of the MCMC simulation model
data(Vaccine)
# 
# Runs the health economic evaluation using BCEA
m <- bcea(e=eff, c=cost,    # 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 
#
mce.plot(mce,               # plots the probability of being most cost-effective
      graph="base")         #  using base graphics
#
if(require(ggplot2)){
mce.plot(mce,               # the same plot
      graph="ggplot2")      #  using ggplot2 instead
}
}