ceac.plot.bcea {BCEA}  R Documentation 
CostEffectiveness Acceptability Curve (CEAC) Plot
Description
Produces a plot of the CostEffectiveness Acceptability Curve (CEAC) against the willingness to pay threshold.
Usage
## S3 method for class 'bcea'
ceac.plot(
he,
comparison = NULL,
pos = c(1, 0),
graph = c("base", "ggplot2", "plotly"),
...
)
ceac.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 
graph 
A string used to select the graphical engine to use for
plotting. Should (partial)match the three options 
... 
If

Details
The CEAC estimates the probability of costeffectiveness, with respect to a given willingness to pay threshold. The CEAC is used mainly to evaluate the uncertainty associated with the decisionmaking process, since it enables the quantification of the preference of the compared interventions, defined in terms of difference in utilities. Formally, the CEAC is defined as:
\textrm{CEAC} = P(\textrm{IB}(\theta) > 0)
If the net benefit function is used as utility function, the definition can be rewritten as
\textrm{CEAC} = P(k \cdot \Delta_e  \Delta_c > 0)
effectively depending on the willingness to pay value k
.
Value
ceac 
If 
The function produces a plot of the costeffectiveness acceptability curve against the discrete grid of possible values for the willingness to pay parameter. Values of the CEAC closer to 1 indicate that uncertainty in the costeffectiveness of the reference intervention is very low. Similarly, values of the CEAC closer to 0 indicate that uncertainty in the costeffectiveness of the comparator is very low.
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
Examples
data("Vaccine")
he < BCEA::bcea(eff, cost)
ceac.plot(he)
ceac.plot(he, graph = "base")
ceac.plot(he, graph = "ggplot2")
ceac.plot(he, graph = "plotly")
ceac.plot(he, graph = "ggplot2",
title = "my title",
line = list(color = "green"),
theme = ggplot2::theme_dark())
## more interventions
he2 < BCEA::bcea(cbind(eff, eff  0.0002), cbind(cost, cost + 5))
mypalette < RColorBrewer::brewer.pal(3, "Accent")
ceac.plot(he2, graph = "ggplot2",
title = "my title",
theme = ggplot2::theme_dark(),
pos = TRUE,
line = list(color = mypalette))
ceac.plot(he, graph = "base", title = "my title", line = list(color = "green"))
ceac.plot(he2, graph = "base")
ceac.plot(he2, graph = "plotly", pos = "bottom")