Cost-Effectiveness Efficiency Frontier"
In BCEA: Bayesian Cost Effectiveness Analysis

knitr::opts_chunk$set(
collapse = TRUE,
comment = "#>",
fig.width = 6
)

library(BCEA)
library(dplyr)
library(reshape2)
library(ggplot2)
library(purrr)

Introduction

The line connecting successive points on a cost-effectiveness plane which each represent the effect and cost associated with different treatment alternatives. The gradient of a line segment represents the ICER of the treatment comparison between the two alternatives represented by that segment. The cost-effectiveness frontier consists of the set of points corresponding to treatment alternatives that are considered to be cost-effective at different values of the cost-effectiveness threshold. The steeper the gradient between successive points on the frontier, the higher is the ICER between these treatment alternatives and the more expensive alternative would be considered cost-effective only when a high value of the cost-effectiveness threshold is assumed. Points not lying on the cost-effectiveness frontier represent treatment alternatives that are not considered cost-effective at any value of the cost-effectiveness threshold.

R code

To create the plots in BCEA we first call the bcea() function.

data(Smoking)

treats <- c("No intervention", "Self-help", "Individual counselling", "Group counselling")
bcea_smoke <- bcea(eff, cost, ref = 4, interventions = treats, Kmax = 500)

base R

# all interventions
ceef.plot(bcea_smoke)

# subset
setComparisons(bcea_smoke) <- c(1,3)
ceef.plot(bcea_smoke)

# check numbering and legend
setComparisons(bcea_smoke) <- c(3,1)
ceef.plot(bcea_smoke)

setComparisons(bcea_smoke) <- c(3,2)
ceef.plot(bcea_smoke)

setComparisons(bcea_smoke) <- 1
ceef.plot(bcea_smoke)

# add interventions back in
setComparisons(bcea_smoke) <- c(1,3)
ceef.plot(bcea_smoke)

ggplot

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

# all interventions
ceef.plot(bcea_smoke, graph = "ggplot")

# subset
setComparisons(bcea_smoke) <- c(1,3)
ceef.plot(bcea_smoke, graph = "ggplot")

# check numbering and legend
setComparisons(bcea_smoke) <- c(3,1)
ceef.plot(bcea_smoke, graph = "ggplot")

setComparisons(bcea_smoke) <- c(3,2)
ceef.plot(bcea_smoke, graph = "ggplot")

setComparisons(bcea_smoke) <- 1
ceef.plot(bcea_smoke, graph = "ggplot")

# add interventions back in
setComparisons(bcea_smoke) <- c(1,3)
ceef.plot(bcea_smoke, graph = "ggplot")

Check legend position argument:

# base R
ceef.plot(bcea_smoke, pos = c(1,0))
ceef.plot(bcea_smoke, pos = c(1,1))

ceef.plot(bcea_smoke, pos = TRUE)
ceef.plot(bcea_smoke, pos = FALSE)

ceef.plot(bcea_smoke, pos = "topleft")
ceef.plot(bcea_smoke, pos = "topright")
ceef.plot(bcea_smoke, pos = "bottomleft")
ceef.plot(bcea_smoke, pos = "bottomright")

# ggplot2
ceef.plot(bcea_smoke, graph = "ggplot", pos = c(1,0))
ceef.plot(bcea_smoke, graph = "ggplot", pos = c(1,1))

ceef.plot(bcea_smoke, graph = "ggplot", pos = TRUE)
ceef.plot(bcea_smoke, graph = "ggplot", pos = FALSE)

ceef.plot(bcea_smoke, graph = "ggplot", pos = "top")
ceef.plot(bcea_smoke, graph = "ggplot", pos = "bottom")
ceef.plot(bcea_smoke, graph = "ggplot", pos = "left")
ceef.plot(bcea_smoke, graph = "ggplot", pos = "right")

Flipping plot

ceef.plot(bcea_smoke,
          flip = TRUE,
          dominance = FALSE,
          start.from.origins = FALSE,
          print.summary = FALSE,
          graph = "base")

ceef.plot(bcea_smoke,
          dominance = TRUE,
          start.from.origins = FALSE,
          pos = TRUE,
          print.summary = FALSE,
          graph = "ggplot2")

Start from origin or smallest (e,c).

ceef.plot(bcea_smoke,
          flip = TRUE,
          dominance = TRUE,
          start.from.origins = TRUE,
          print.summary = FALSE,
          graph = "base")

ceef.plot(bcea_smoke,
          dominance = TRUE,
          start.from.origins = TRUE,
          pos = TRUE,
          print.summary = FALSE,
          graph = "ggplot2")

Negative cost or effectiveness

data("Smoking")

cost[, 4] <- -cost[, 4]
bcea_smoke <- bcea(eff, cost, ref = 3, interventions = treats, Kmax = 500)

# all interventions
ceef.plot(bcea_smoke, graph = "ggplot")
ceef.plot(bcea_smoke, graph = "base")

ceef.plot(bcea_smoke, start.from.origins = TRUE, graph = "ggplot")
ceef.plot(bcea_smoke, start.from.origins = TRUE, graph = "base")

setComparisons(bcea_smoke) <- c(1,2)
ceef.plot(bcea_smoke, graph = "ggplot")
ceef.plot(bcea_smoke, graph = "base")

eff[, 3] <- -eff[, 3]
bcea_smoke <- bcea(eff, cost, ref = 3, interventions = treats, Kmax = 500)
ceef.plot(bcea_smoke, graph = "ggplot")
ceef.plot(bcea_smoke, graph = "base")


data("Smoking")
eff[, 3] <- -eff[, 3]
bcea_smoke <- bcea(eff, cost, ref = 3, interventions = treats, Kmax = 500)
ceef.plot(bcea_smoke, graph = "ggplot")
ceef.plot(bcea_smoke, graph = "base")

# create output docs
rmarkdown::render(input = "vignettes/ceef.Rmd", output_format = "pdf_document", output_dir = "vignettes")
rmarkdown::render(input = "vignettes/ceef.Rmd", output_format = "html_document", output_dir = "vignettes")