This vignette will demonstrate a simple cost-effectiveness analysis
using BCEA using the smoking cessation data set
contained in the package.
Load the data.
This study has four interventions.
treats <- c("No intervention", "Self-help", "Individual counselling", "Group counselling")
Setting the reference group (
ref) to Group
counselling and the maximum willingness to pay (
bcea_smoke <- bcea(eff, cost, ref = 4, interventions = treats, Kmax = 500)
We can easily create a grid of the most common plots
Individual plots can be plotting using their own functions.
ceplane.plot(bcea_smoke, comparison = 2, wtp = 250)
#> NB: k (wtp) is defined in the interval [0 - 500]
More on this in the other vignettes but you can change the default
plotting style, such as follows.
graph = "ggplot2",
wtp = 250,
line = list(color = "red", size = 1),
point = list(color = c("plum", "tomato", "springgreen"), shape = 3:5, size = 2),
icer = list(color = c("red", "orange", "black"), size = 5))
#> Warning: Using linewidth for a discrete variable is not advised.