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).

## 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 \(\Delta_c\))

## 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.

## 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")
```