ceac.plot.bcea {BCEA} R Documentation

## Cost-Effectiveness Acceptability Curve (CEAC) Plot

### Description

Produces a plot of the Cost-Effectiveness 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 bcea object containing the results of the Bayesian modelling and the economic evaluation. 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). 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". ... If graph = "ggplot2" and a named theme object is supplied, it will be added to the ggplot object. Additional arguments: line_colors: specifies the line colour(s) - all graph types. line_types: specifies the line type(s) as lty numeric values - all graph types. area_include: logical, include area under the CEAC curves - plotly only. area_color: specifies the AUC colour - plotly only.

### Details

The CEAC estimates the probability of cost-effectiveness, with respect to a given willingness to pay threshold. The CEAC is used mainly to evaluate the uncertainty associated with the decision-making 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}(θ) > 0)

If the net benefit function is used as utility function, the definition can be re-written as

\textrm{CEAC} = P(k \cdot Δ_e - Δ_c > 0)

effectively depending on the willingness to pay value k.

### Value

 ceac 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 cost-effectiveness 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 cost-effectiveness of the reference intervention is very low. Similarly, values of the CEAC closer to 0 indicate that uncertainty in the cost-effectiveness of the comparator is very low.

### Author(s)

Gianluca Baio, Andrea Berardi

### References

Baio, G., Dawid, A. P. (2011). Probabilistic Sensitivity Analysis in Health Economics. Statistical Methods in Medical Research doi:10.1177/0962280211419832.

Baio G. (2012). Bayesian Methods in Health Economics. CRC/Chapman Hall, London.

bcea, plot.bcea

### Examples

data("Vaccine")
he <- BCEA::bcea(e, c)
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(colors = "green"),
theme = ggplot2::theme_dark())

## more interventions
he2 <- BCEA::bcea(cbind(e, e - 0.0002), cbind(c, c + 5))
mypalette <- RColorBrewer::brewer.pal(3, "Accent")
ceac.plot(he2, graph = "ggplot2",
title = "my title",
theme = ggplot2::theme_dark(),
pos = TRUE,
line = list(colors = mypalette))
ceac.plot(he, graph = "base", title = "my title", line = list(colors = "green"))
ceac.plot(he2, graph = "base")

ceac.plot(he2, graph = "plotly", pos = "bottom")



[Package BCEA version 2.4.1 Index]