contour.bcea {BCEA}

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,
  pos = c(0, 1),
  graph = c("base", "ggplot2"),
  comparison = NULL,
  ...
)

contour(he, ...)

Arguments

he

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

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.

graph

A string used to select the graphical engine to use for plotting. Should (partial-)match the three options "base", "ggplot2" or "plotly". Default value is "base". Not all plotting functions have a "plotly" implementation yet.

comparison

Selects the comparator, in case of more than two interventions being analysed. Default as NULL plots all the comparisons together. Any subset of the possible comparisons can be selected (e.g., comparison=c(1,3) or comparison=2).

...

Additional graphical arguments. The usual ggplot2 syntax is used regardless of graph type.

• 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

• scale: Scales the plot as a function of the observed standard deviation.

• levels: Numeric vector of levels at which to draw contour lines. Quantiles 0<p<1.

• nlevels: Number of levels to be plotted in the contour.

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 (\Delta_e, \Delta_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 \Delta_e and \Delta_c)

Author(s)

Gianluca Baio, Andrea Berardi

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.

See Also

bcea(), ceplane.plot(), contour2()

Examples

data(Vaccine)

# run 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=TRUE             # plots the results
)

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

contour(m,          # uses the results of the economic evaluation 
                    #  (a "bcea" object)
      comparison=1, # if more than 2 interventions, selects the 
                    #  pairwise comparison 
      nlevels=10,   # 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=1,      # scales the bandwidths for both x- and 
                    #  y-axis (default=0.5)
      graph="base"  # uses base graphics to produce the plot
)

# use the smoking cessation dataset
data(Smoking)
m <- bcea(eff, cost, ref = 4, intervention = treats, Kmax = 500, plot = FALSE)
contour(m)
contour(m, graph = "ggplot2")