A Tutorial on Time-Dependent 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-dependent 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. A Tutorial on Time-Dependent Cohort State-Transition Models in R using a Cost-Effectiveness Analysis Example. Medical Decision Making. 2023;43(1):21-41. https://doi.org/10.1177/0272989X221121747

and its corresponding pre-print:

• Alarid-Escudero F, Krijkamp EM, Enns EA, Yang A, Hunink MGM, Pechlivanoglou P, Jalal H. A Tutorial on Time-Dependent Cohort State-Transition Models in R using a Cost-Effectiveness Analysis Example. arXiv:2108.13552v2. 2022:1-37.

The release that accompanies the published article has been archived in zenodo: https://zenodo.org/badge/latestdoi/6620902

The manuscript folder includes the RMarkdown files cSTM_Tutorial_TimeDep.Rmd and Appendix_table_Markov_Tutorial_Part2.Rmd with the text and code to generate the tutorial and Supplementary table pdf files, respectively. The manuscript RMarkdown file does not include all the code for all the analyses for both types of time dependence. The analysis folder includes the scripts with all the code, description and comments to reproduce the CEA, probabilistic sensitivity analysis (PSA) and generation of epidemiological measures of the manuscript:

• cSTM_time_dep_simulation.R: Code to replicate all simulation-time dependent cSTMs analyses of the manuscript.
• cSTM_time_dep_state_residence.R: Code to replicate all state-residence time dependent cSTMs analyses of the manuscript.

The R scripts require loading functions that synthesize cSTMs outputs and conduct several sensitivity analyses included in the R folder:

• Funtions.R: Functions to generate epidemiological measures from time-dependent cSTMs.
• Functions_cSTM_time_dep_simulation.R: These functions wrap the simulation-time dependent cSTMs, compute CEA and epidemiological measures, and generate probabilistic sensitivity analysis (PSA) input datasets.
• Functions_cSTM_time_dep_state_residence.R: These functions wrap the state-residence time dependent cSTMs, compute CEA and epidemiological measures, and generate probabilistic sensitivity analysis (PSA) input datasets.

How to cite this R code in your article

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. (2022)”. 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. A Tutorial on Time-Dependent Cohort State-Transition Models in R using a Cost-Effectiveness Analysis Example. Medical Decision Making. 2023;43(1):21-41.

Alarid-Escudero F, Krijkamp EM, Enns EA, Yang A, Hunink MGM, Pechlivanoglou P, Jalal H (2022). R Code for A Tutorial on Time-Dependent Cohort State-Transition Models in R using a Cost-Effectiveness Analysis Example (Version v0.2.0). Zenodo. 10.5281/zenodo.6620902. Last accessed 7 June 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.

Preliminaries

• Install RStudio
• Install dampack R package from CRAN

# Install release version from CRAN
install.packages("dampack")

# Or install development version from GitHub
# devtools::install_github("DARTH-git/dampack")

• Install devtools to install darthtools R 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 darthtools using devtools

# Install development version from GitHub
devtools::install_github("DARTH-git/darthtools")

We recommend reading our introductory tutorial on time-independent cSTMs in R:

• 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. Medical Decision Making, 2023;43(1):3-20. https://doi.org/10.1177/0272989X221103163

and understanding the use of multidimensional arrays to represent cSTM dynamics in R described in:

• Krijkamp EM, Alarid-Escudero F, Enns EA, Pechlivanoglou P, Hunink MGM, Yang A, Jalal HJ. A multidimensional array representation of state-transition model dynamics. Medical Decision Making, 2020;40(2):242-248. https://doi.org/10.1177/0272989X19893973,

and 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.

Use repository as a regular coding template

1. On the tutorial’s GitHub repository, navigate to the main page of the repository (https://github.com/DARTH-git/cohort-modeling-tutorial-timedep).
2. Above the file list, click Clone or download and select either
   1. Open in desktop, which requires the user to have a GitHub desktop installed, or
   2. Download zip that will ask the user to download the whole repository as a .zip file.
3. Open the RStudio project cohort-modeling-tutorial-timedep.Rproj.
4. Install all the required packages (as mentioned above)
   • dampack
   • darthtools
5. Run the scripts in the analysis folder.
6. Modify or adapt these scripts as needed for your project or analysis.

Full list of Contributors:

• Fernando Alarid-Escudero

• Eline Krijkamp

• Eva Enns

• Alan Yang

• Myriam Hunink

• Petros Pechlivanoglou

• Hawre Jalal