An Introductory Tutorial on Cohort State-Transition Models in R Using a Cost-Effectiveness Analysis Example
This GitHub repository provides the code of the tutorial on how to implement time-independent cohort state-transition models (cSTMs) in R using a cost-effectiveness analysis (CEA) example, explained in the following manuscript:
- Alarid-Escudero F, Krijkamp EM, Enns EA, Yang A, Hunink MGM, Pechlivanoglou P, Jalal H. An Introductory Tutorial on Cohort State-Transition Models in R Using a Cost-Effectiveness Analysis Example. arXiv:200107824v4. 2022:1-30.
The release that accompanies the published article has been archived in zenodo: https://zenodo.org/badge/latestdoi/357362984
The
R
folder includes two different scripts corresponding to functions used to
synthesize cSTMs outputs and conduct several sensitivity analyses:
Funtions.R: Functions that generate epidemiological measures from time-independent cSTMs and compute within-cycle correction, parameter transformation, matrix checks, and CEA and PSA visualization.Functions_cSTM_time_indep.R: These functions wrap the time-independent cSTM, compute CEA measures, and generate probabilistic sensitivity analysis (PSA) input datasets.
You can cite the R code in this repository like this “we based our analysis using the R code from Alarid-Escudero F et al. (2021)”. Here is the full bibliographic reference to include in your reference list for the manuscript and the R code (don’t forget to update the ‘last accessed’ date):
Alarid-Escudero F, Krijkamp EM, Enns EA, Yang A, Hunink MGM, Pechlivanoglou P, Jalal H. An Introductory Tutorial on Cohort State-Transition Models in R Using a Cost-Effectiveness Analysis Example (http://arxiv.org/abs/2001.07824). arXiv:200107824v4. 2022:1-30.
Alarid-Escudero F, Krijkamp EM, Enns EA, Yang A, Hunink MGM, Pechlivanoglou P, Jalal H (2022). R Code for An Introductory Tutorial on Cohort State-Transition Models in R Using a Cost-Effectiveness Analysis Example (Version v0.2.1). Zenodo. 10.5281/zenodo.5223093. Last accessed 30 March 2022.
If you adapted the code, you should indicate “Adapted from:” or “Based on” so it is understood that you modified the code. For more information on how to cite computer code, we refer the user to review Writing Code (from MIT Research Guide), which provides examples of how and when to cite computer code.
# Install release version from CRAN
install.packages("dampack")
# Or install development version from GitHub
# devtools::install_github("DARTH-git/dampack")- Install
devtoolsto installdarthtoolsR package from DARTH’s GitHub
# Install release version from CRAN
install.packages("devtools")
# Or install development version from GitHub
# devtools::install_github("r-lib/devtools")- Install
darthtoolsusingdevtools
# Install development version from GitHub
devtools::install_github("DARTH-git/darthtools")We recommend familiarizing with the DARTH coding framework described in
- Alarid-Escudero F, Krijkamp EM, Pechlivanoglou P, Jalal HJ, Kao SYZ, Yang A, Enns EA. A Need for Change! A Coding Framework for Improving Transparency in Decision Modeling. PharmacoEconomics, 2190;37(11):1329–1339. https://doi.org/10.1007/s40273-019-00837-x
To run the CEA, you require dampack: Decision-Analytic Modeling
Package, an
R package for analyzing and visualizing the health economic outputs of
decision models.
- On the tutorial’s GitHub repository, navigate to the main page of the repository (https://github.com/DARTH-git/cohort-modeling-tutorial-intro).
- Above the file list, click Clone or download and select either
- Open in desktop, which requires the user to have a GitHub desktop installed, or
- Download zip that will ask the user to download the whole repository as a .zip file.
- Open the RStudio project
cohort-modeling-tutorial-intro.Rproj. - Install all the required packages (as mentioned above)
- Run the scripts in the analysis folder.
- Modify or adapt these scripts as needed for your project or analysis.