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

Author

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  • Fernando Alarid-Escudero

    (Division of Public Administration, Center for Research and Teaching in Economics (CIDE), Aguascalientes, Aguascalientes, Mexico)

  • Eline Krijkamp

    (Department of Epidemiology and Department of Radiology, Erasmus University Medical Center, Rotterdam, The Netherlands)

  • Eva A. Enns

    (Division of Health Policy and Management, University of Minnesota School of Public Health, Minneapolis, MN, USA)

  • Alan Yang

    (The Hospital for Sick Children, Toronto, Ontario, Canada)

  • M. G. Myriam Hunink

    (Department of Epidemiology and Department of Radiology, Erasmus University Medical Center, Rotterdam, The Netherlands
    Center for Health Decision Sciences, Harvard T.H. Chan School of Public Health, Boston, USA)

  • Petros Pechlivanoglou

    (The Hospital for Sick Children, Toronto and University of Toronto, Toronto, Ontario, Canada)

  • Hawre Jalal

    (School of Epidemiology and Public Health, Faculty of Medicine, University of Ottawa, Ottawa, ON, Canada)

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, 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.

Suggested Citation

  • Fernando Alarid-Escudero & Eline Krijkamp & Eva A. Enns & Alan Yang & M. G. Myriam Hunink & Petros Pechlivanoglou & Hawre Jalal, 2023. "An Introductory Tutorial on Cohort State-Transition Models in R Using a Cost-Effectiveness Analysis Example," Medical Decision Making, , vol. 43(1), pages 3-20, January.
    • Handle: RePEc:sae:medema:v:43:y:2023:i:1:p:3-20
    DOI: 10.1177/0272989X221103163
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    References listed on IDEAS

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    1. Joost van Rosmalen & Mehlika Toy & James F. O’Mahony, 2013. "A Mathematical Approach for Evaluating Markov Models in Continuous Time without Discrete-Event Simulation," Medical Decision Making, , vol. 33(6), pages 767-779, August.
    2. J.Robert Beck & Stephen G. Pauker, 1983. "The Markov Process in Medical Prognosis," Medical Decision Making, , vol. 3(4), pages 419-458, December.
    3. Eline M. Krijkamp & Fernando Alarid-Escudero & Eva A. Enns & Hawre J. Jalal & M. G. Myriam Hunink & Petros Pechlivanoglou, 2018. "Microsimulation Modeling for Health Decision Sciences Using R: A Tutorial," Medical Decision Making, , vol. 38(3), pages 400-422, April.
    4. Alexander Begun & Andrea Icks & Regina Waldeyer & Sandra Landwehr & Michael Koch & Guido Giani, 2013. "Identification of a Multistate Continuous-Time Nonhomogeneous Markov Chain Model for Patients with Decreased Renal Function," Medical Decision Making, , vol. 33(2), pages 298-306, February.
    5. Hawre Jalal & Petros Pechlivanoglou & Eline Krijkamp & Fernando Alarid-Escudero & Eva Enns & M. G. Myriam Hunink, 2017. "An Overview of R in Health Decision Sciences," Medical Decision Making, , vol. 37(7), pages 735-746, October.
    6. Andrew H. Briggs & A. E. Ades & Martin J. Price, 2003. "Probabilistic Sensitivity Analysis for Decision Trees with Multiple Branches: Use of the Dirichlet Distribution in a Bayesian Framework," Medical Decision Making, , vol. 23(4), pages 341-350, July.
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