An Introductory Tutorial on Cohort State-Transition Models in R Using a Cost-Effectiveness Analysis Example
Abstract
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, where 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, 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.
Cite
@article{arxiv.2001.07824,
title = {An Introductory Tutorial on Cohort State-Transition Models in R Using a Cost-Effectiveness Analysis Example},
author = {Fernando Alarid-Escudero and Eline M. Krijkamp and Eva A. Enns and Alan Yang and M. G. Myriam Hunink and Petros Pechlivanoglou and Hawre Jalal},
journal= {arXiv preprint arXiv:2001.07824},
year = {2022}
}
Comments
Tutorial with 30 pages, 6 tables and 6 figures. For R code, see https://github.com/DARTH-git/cohort-modeling-tutorial-intro