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Getting Started

This vignette will demonstrate a simple cost-effectiveness analysis using BCEA using the smoking cessation data set contained in the package.

library(BCEA)

Load the data.

data(Smoking)

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 (Kmax) as 500.

bcea_smoke <- bcea(eff, cost, ref = 4, interventions = treats, Kmax = 500)

We can easily create a grid of the most common plots

library(ggplot2)
library(purrr)

plot(bcea_smoke)

Individual plots can be plotting using their own functions.

ceplane.plot(bcea_smoke, comparison = 2, wtp = 250)

eib.plot(bcea_smoke)

contour(bcea_smoke)

ceac.plot(bcea_smoke)

ib.plot(bcea_smoke)
#> 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.

plot(bcea_smoke,
     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))

These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.
Health stats visible at Monitor.