Plots the Expected Value of Information (EVI) against the willingness to pay.

## Usage

```
# S3 method for bcea
evi.plot(he, graph = c("base", "ggplot2", "plotly"), ...)
evi.plot(he, ...)
```

## Arguments

- he
A

`bcea`

object containing the results of the Bayesian modelling and the economic evaluation.- 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"`

.- ...
Additional graphical arguments:

`line_colors`

to specify the EVPI line colour - all graph types.`line_types`

to specify the line type (lty) - all graph types.`area_include`

to specify whether to include the area under the EVPI curve - plotly only.`area_color`

to specify the area under the colour curve - plotly only.

## Value

- eib
If

`graph="ggplot2"`

a ggplot object, or if`graph="plotly"`

a plotly object containing the requested plot. Nothing is returned when`graph="base"`

, the default.

The function produces a plot of the Expected Value of Information as a function of the discrete grid approximation of the willingness to pay parameter. The break even point(s) (i.e. the point in which the EIB=0, ie when the optimal decision changes from one intervention to another) is(are) also showed.

## 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)
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
)
evi.plot(m)
data(Smoking)
treats <- c("No intervention", "Self-help",
"Individual counselling", "Group counselling")
m <- bcea(eff, cost, ref = 4, interventions = treats, Kmax = 500)
evi.plot(m)
```