knitr::opts_chunk$set( collapse = TRUE, comment = "#>", fig.width = 6 )
library(BCEA) library(dplyr) library(reshape2) library(ggplot2) library(purrr)
The intention of this vignette is to show how to plot different styles of cost-effectiveness acceptability curves using the BCEA package.
This is the simplest case, usually an alternative intervention ($i=1$) versus status-quo ($i=0$).
The plot show the probability that the alternative intervention is cost-effective for each willingness to pay, $k$,
$$ p(NB_1 \geq NB_0 | k) \mbox{ where } NB_i = ke - c $$
Using the set of $N$ posterior samples, this is approximated by
$$ \frac{1}{N} \sum_j^N \mathbb{I} (k \Delta e^j - \Delta c^j) $$
To calculate these in BCEA we use the bcea()
function.
data("Vaccine") he <- bcea(eff, cost) # str(he) ceac.plot(he)
The plot defaults to base R plotting. Type of plot can be set explicitly using the graph
argument.
ceac.plot(he, graph = "base") ceac.plot(he, graph = "ggplot2") # ceac.plot(he, graph = "plotly")
Other plotting arguments can be specified such as title, line colours and theme.
ceac.plot(he, graph = "ggplot2", title = "my title", line = list(color = "green"), theme = theme_dark())
This situation is when there are more than two interventions to consider. Incremental values can be obtained either always against a fixed reference intervention, such as status-quo, or for all pair-wise comparisons.
Without loss of generality, if we assume that we are interested in intervention $i=1$, then we wish to calculate
$$ p(NB_1 \geq NB_s | k) \;\; \exists \; s \in S $$
Using the set of $N$ posterior samples, this is approximated by
$$ \frac{1}{N} \sum_j^N \mathbb{I} (k \Delta e_{1,s}^j - \Delta c_{1,s}^j) $$
This is the default plot for ceac.plot()
so we simply follow the same steps as above with the new data set.
data("Smoking") he <- bcea(eff, cost, ref = 4) # str(he)
ceac.plot(he) ceac.plot(he, graph = "base", title = "my title", line = list(color = "green"))
ceac.plot(he, graph = "ggplot2", title = "my title", line = list(color = "green"))
Reposition legend.
ceac.plot(he, pos = FALSE) # bottom right ceac.plot(he, pos = c(0, 0)) ceac.plot(he, pos = c(0, 1)) ceac.plot(he, pos = c(1, 0)) ceac.plot(he, pos = c(1, 1))
ceac.plot(he, graph = "ggplot2", pos = c(0, 0)) ceac.plot(he, graph = "ggplot2", pos = c(0, 1)) ceac.plot(he, graph = "ggplot2", pos = c(1, 0)) ceac.plot(he, graph = "ggplot2", pos = c(1, 1))
Define colour palette.
mypalette <- RColorBrewer::brewer.pal(3, "Accent") ceac.plot(he, graph = "base", title = "my title", line = list(color = mypalette), pos = FALSE) ceac.plot(he, graph = "ggplot2", title = "my title", line = list(color = mypalette), pos = FALSE)
Again, without loss of generality, if we assume that we are interested in intervention $i=1$, then we wish to calculate
$$ p(NB_1 = \max{NB_i : i \in S} | k) $$
This can be approximated by the following.
$$ \frac{1}{N} \sum_j^N \prod_{i \in S} \mathbb{I} (k \Delta e_{1,i}^j - \Delta c_{1,i}^j) $$
In BCEA we first we must determine all combinations of paired interventions using the multi.ce()
function.
he <- multi.ce(he)
We can use the same plotting calls as before i.e. ceac.plot()
and BCEA will deal with the pairwise situation appropriately. Note that in this case the probabilities at a given willingness to pay sum to 1.
ceac.plot(he, graph = "base") ceac.plot(he, graph = "base", title = "my title", line = list(color = "green"), pos = FALSE) mypalette <- RColorBrewer::brewer.pal(4, "Dark2") ceac.plot(he, graph = "base", title = "my title", line = list(color = mypalette), pos = c(0,1))
ceac.plot(he, graph = "ggplot2", title = "my title", line = list(color = mypalette), pos = c(0,1))
The line width can be changes with either a single value to change all lines to the same thickness or a value for each.
ceac.plot(he, graph = "ggplot2", title = "my title", line = list(size = 2))
ceac.plot(he, graph = "ggplot2", title = "my title", line = list(size = c(1,2,3)))
# create output docs # rmarkdown::render(input = "vignettes/ceac.Rmd", output_format = "pdf_document", output_dir = "vignettes") # rmarkdown::render(input = "vignettes/ceac.Rmd", output_format = "html_document", output_dir = "vignettes")
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