contour.bcea {BCEA}R Documentation

Contour Plots for the Cost-Effectiveness Plane

Description

Contour method for objects in the class bcea. Produces a scatterplot of the cost-effectiveness plane, with a contour-plot of the bivariate density of the differentials of cost (y-axis) and effectiveness (x-axis).

Usage

## S3 method for class 'bcea'
contour(
  he,
  scale = 0.5,
  nlevels = 4,
  levels = NULL,
  pos = c(1, 0),
  xlim = NULL,
  ylim = NULL,
  graph = c("base", "ggplot2"),
  ...
)

contour(he, ...)

Arguments

he

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

scale

Scales the plot as a function of the observed standard deviation.

nlevels

Number of levels to be plotted in the contour.

levels

Numeric vector of levels at which to draw contour lines. Will be ignored using graph="ggplot2".

pos

Parameter to set the position of the legend (only relevant for multiple interventions, ie more than 2 interventions being compared). 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 FALSE indicating to use the default position and TRUE to place it on the bottom of the plot.

xlim

The range of the plot along the x-axis. If NULL (default) it is determined by the range of the simulated values for delta_e

ylim

The range of the plot along the y-axis. If NULL (default) it is determined by the range of the simulated values for delta_c

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".

...

Additional arguments

Value

ceplane

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

Plots the cost-effectiveness plane with a scatterplot of all the simulated values from the (posterior) bivariate distribution of (Δ_e, Δ_c), the differentials of effectiveness and costs; superimposes a contour of the distribution and prints the estimated value of the probability of each quadrant (combination of positive/negative values for both Δ_e and Δ_c)

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, ceplane.plot, contour2

Examples

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=TRUE             # plots the results
)

contour(m)
contour(m, graph = "ggplot2")

# Plots the contour and scatterplot of the bivariate 
# distribution of (Delta_e, Delta_c)
contour(m,          # uses the results of the economic evaluation 
                    #  (a "bcea" object)
      comparison=1, # if more than 2 interventions, selects the 
                    #  pairwise comparison 
      nlevels=4,    # selects the number of levels to be 
                    #  plotted (default=4)
      levels=NULL,  # specifies the actual levels to be plotted 
                    #  (default=NULL, so that R will decide)
      scale=0.5,    # scales the bandwidths for both x- and 
                    #  y-axis (default=0.5)
      graph="base"  # uses base graphics to produce the plot
)


[Package BCEA version 2.4.1 Index]