This is the repository for the rcea package, which accompanies a short course on model-based cost-effectiveness analysis (CEA) with R. A range of models are covered including time-homogeneous and time-inhomogeneous Markov cohort models, partitioned survival models, and semi-Markov individual patient simulations. In addition, the course shows how simulated costs and QALYs from a probabilistic sensitivity analysis can be used for decision-analysis within a cost-effectiveness framework. Analyses are conducted using both base R and the R package hesim.

The course materials are available at https://hesim-dev.github.io/rcea.

## Installation and setup

All required R packages and course materials can be installed with the following steps.

1. Open an R session. We recommend using RStudio.

2. Install the rcea package from GitHub, which will also install all other required packages.

# install.packages("devtools") # You must install the "devtools" R package first.
devtools::install_github("hesim-dev/rcea")
3. Create a new project in your desired directory.

# Create a project named "rcea-exercises" within a directory named "Projects"
usethis::create_project("~/Projects/rcea-exercises")
4. Add the course materials (R scripts for the tutorials) to your new project.

rcea::use_rcea("~/Projects/rcea-exercises")

## Tutorials

The course contains six tutorials:

1. Markov Cohort Model: A simple time-homogeneous Markov cohort model with fixed parameter values.

2. Incorporating Probabilistic Sensitivity Analysis (PSA): The Markov cohort model is re-analyzed using suitable probability distributions for the parameters.

3. Markov Cohort Model with hesim: The second tutorial—programmed primarily using base R— is repeated using the R package hesim.

4. Semi-Markov Multi-state state Model: A semi-Markov multi-state model is fit to patient-level data and outcomes are simulated using an individual patient simulation.

5. Partitioned Survival Model: The data from the fourth tutorial is refit using partitioned survival analysis and state probabilities are computed using the “area under the curve” technique.

6. Cost-effectiveness Analysis (CEA): CEA is performed using the cost and QALY output of the PSA from the fourth tutorial. A number of methods are used to represent decision uncertainty (e.g. cost-effectiveness planes, cost-effectiveness acceptability curves, and cost-effectiveness acceptability frontiers), and value of information analysis is conducted.