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.
R packages and course materials can be installed with the following steps.
Open an R session. We recommend using RStudio.
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")
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")
Add the course materials (
R scripts for the tutorials) to your new project.
The course contains six tutorials:
Markov Cohort Model: A simple time-homogeneous Markov cohort model with fixed parameter values.
Incorporating Probabilistic Sensitivity Analysis (PSA): The Markov cohort model is re-analyzed using suitable probability distributions for the parameters.
Markov Cohort Model with hesim: The second tutorial—programmed primarily using base
R— is repeated using the
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.
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.
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.