eib.plot.bcea {BCEA}  R Documentation 
Expected Incremental Benefit (EIB) Plot
Description
Produces a plot of the Expected Incremental Benefit (EIB) as a function of the willingness to pay.
Usage
## S3 method for class 'bcea'
eib.plot(
he,
comparison = NULL,
pos = c(1, 0),
size = NULL,
plot.cri = NULL,
graph = c("base", "ggplot2", "plotly"),
...
)
eib.plot(he, ...)
Arguments
he 
A 
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.,

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 
size 
Value (in millimetres) of the size of the willingness to pay
label. Used only if 
plot.cri 
Logical value. Should the credible intervals be plotted
along with the expected incremental benefit? Default as 
graph 
A string used to select the graphical engine to use for
plotting. Should (partial)match the three options 
... 
If

Value
eib 
If 
The function produces a plot of the
Expected Incremental Benefit as a function of the discrete grid
approximation of the willingness to pay parameter. The break even point
(i.e. the point in which the EIB = 0, i.e. when the optimal decision changes
from one intervention to another) is also showed by default. The value k*
is
the discrete grid approximation of the ICER.
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 14770334, doi:10.1177/0962280211419832, https://pubmed.ncbi.nlm.nih.gov/21930515/.
Baio G (2013). Bayesian Methods in Health Economics. CRC.
See Also
bcea()
,
ib.plot()
,
ceplane.plot()
Examples
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 # plots the results
)
eib.plot(m)
eib.plot(m, graph = "ggplot2") + ggplot2::theme_linedraw()
data(Smoking)
treats < c("No intervention", "Selfhelp",
"Individual counselling", "Group counselling")
m < bcea(eff, cost, ref = 4, interventions = treats, Kmax = 500)
eib.plot(m)