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This is a guide to the code and software resources for the book Value of Information for Health Economic Evaluations (eds. Heath, Kunst, Jackson), (Chapman and Hall/CRC, 2024).

Before running any of this code, you should install the voi package, which is available from CRAN.

Then you can set the working directory as follows


then your working directory should contain subdirectories that include 01_data_raw, 02_data, etc. You can check this by running the command list.files() in your R console.

Alternatively, you can copy the contents of the Chemotherapy_Book directory to your preferred working directory, and work there instead. If you work in RStudio then it is recommended to use a “project”. You could create a project for this work by going to File,New Project,Version Control,Git, then selecting as the Repository URL, and changing the project name and save location according to your preference.

Chapter 3: Chemotherapy case study

The code to run the baseline cost-effectiveness analysis (Section 2.4) is in the 04_analysis/02_baseline_model_output.R subdirectory (link to GitHub source). This can be run all at once using


Or alternatively, open this file in R, and examine and run each line of code by hand.

Chapter 4: Value of Perfect Information

EVPI analysis for the Chemotherapy model (Section 3.2)

04_analysis/03_Expected_Value_of_Perfect_Information.R (link to GitHub source)

EVPPI analysis for the Chemotherapy model (Section 3.4)

04_analysis/04_Expected_Value_of_Partial_Perfect_Information.R (link to GitHub source)

Nonparametric regression examples

Code to draw illustrations of GAM regression (Figure 3.3)

misc_dir <- setwd(system.file("book_misc",package="voi"))

Code to draw illustrations of Gaussian process regression (Figure 3.4)

file.path("misc_dir", "gp_graphs.R")

Code to calculate EVPPI using nonparametric regression (Section 3.3.5)

file.path("misc_dir", "evppi_reg.Rmd")

Chapter 5: Value of Sample Information

Moment matching methods

04_analysis/05_Expected_Value_of_Sample_Information_MM.R (link to GitHub source)

Regression-based methods

04_analysis/05_Expected_Value_of_Sample_Information_RB.R (link to GitHub source)

Presenting EVSI and ENBS Analyses

For base R functions to reproduce the plots in the book, see the source on GitHub

For functions to calculate and optimise ENBS, and ggplot2 recipes to reproduce similar plots, see the voi package vignette.