contour.bcea {BCEA}R Documentation

Contour Plots for the Cost-Effectiveness Plane


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).


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

contour(he, ...)



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


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


Number of levels to be plotted in the contour.


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


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.


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


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


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



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)


Gianluca Baio, Andrea Berardi


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



# 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, 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]