An Introductory Tutorial on Cohort State-Transition Models in R Using a Cost-Effectiveness Analysis Example

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Alarid-Escudero, F., Krijkamp, E., Enns, E. A., Yang, A., Hunink, M. M., Pechlivanoglou, P., & Jalal, H. (2023). An introductory tutorial on cohort state-transition models in r using a cost-effectiveness analysis example. Medical Decision Making, 43(1), 3-20.

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BibTex

@article{alarid2023introductory,
  title={An introductory tutorial on cohort state-transition models in r using a cost-effectiveness analysis example},
  author={Alarid-Escudero, Fernando and Krijkamp, Eline and Enns, Eva A and Yang, Alan and Hunink, MG Myriam and Pechlivanoglou, Petros and Jalal, Hawre},
  journal={Medical Decision Making},
  volume={43},
  number={1},
  pages={3--20},
  year={2023},
  publisher={SAGE Publications Sage CA: Los Angeles, CA}
}

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EndNote

%0 Journal Article
%T An introductory tutorial on cohort state-transition models in r using a cost-effectiveness analysis example
%A Alarid-Escudero, Fernando
%A Krijkamp, Eline
%A Enns, Eva A
%A Yang, Alan
%A Hunink, MG Myriam
%A Pechlivanoglou, Petros
%A Jalal, Hawre
%J Medical Decision Making
%V 43
%N 1
%P 3-20
%@ 0272-989X
%D 2023
%I SAGE Publications Sage CA: Los Angeles, CA

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RefMan

TY  - JOUR
T1  - An introductory tutorial on cohort state-transition models in r using a cost-effectiveness analysis example
A1  - Alarid-Escudero, Fernando
A1  - Krijkamp, Eline
A1  - Enns, Eva A
A1  - Yang, Alan
A1  - Hunink, MG Myriam
A1  - Pechlivanoglou, Petros
A1  - Jalal, Hawre
JO  - Medical Decision Making
VL  - 43
IS  - 1
SP  - 3
EP  - 20
SN  - 0272-989X
Y1  - 2023
PB  - SAGE Publications Sage CA: Los Angeles, CA
ER  - 

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Abstract

 

Background

Decision models can combine information from different sources to simulate the long-term consequences of alternative strategies in the presence of uncertainty. A cohort state-transition model (cSTM) is a decision model commonly used in medical decision making to simulate the transitions of a hypothetical cohort among various health states over time. This tutorial focuses on time-independent cSTM, in which transition probabilities among health states remain constant over time. We implement time-independent cSTM in R, an open-source mathematical and statistical programming language. We illustrate time-independent cSTMs using a previously published decision model, calculate costs and effectiveness outcomes, and conduct a cost-effectiveness analysis of multiple strategies, including a probabilistic sensitivity analysis. We provide open-source code in R to facilitate wider adoption. In a second, more advanced tutorial, we illustrate time-dependent cSTMs.

 

Methods

This introductory tutorial aims to 1) conceptualize time-independent cSTMs for implementation in a programming language and 2) provide a template for implementing these cSTMs in base R. We focus on using R base packages, ensuring modelers understand the concept and structure of cSTMs and avoid the limitation of constructing cSTMs in a package-specific structure. We used previously developed R packages for visualizing CEA results and checking that cSTMs are correctly specified.  

Results

In this introductory tutorial, we provided a step-by-step mathematical conceptualization of time-independent cSTMs and a walk through of their implementation in R using a hypothetical disease example with accompanying code throughout the tutorial. Although some of the presented implementation details are specific to the R programming language, much of the code structure shown in this tutorial would be similar in other programming languages. Thus, readers may use this tutorial as a template for coding cSTMs more generally in different programming languages.

 

Conclusions

In summary, this tutorial provides a conceptualization of time-independent cSTMs and a step-by-step guide to implement them in R. We aim to add to the current body of literature and material on building this type of decision model so that health decision scientists and health economists can develop cSTMs in a more flexible, efficient, open-source manner and to encourage increased transparency and reproducibility. In the advanced cSTM tutorial, we explore generalizing this framework to time-dependent cSTM, generating epidemiological outcomes, and incorporating transition rewards.