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A Tutorial on Time-Dependent Cohort State-Transition Models in R Using a Cost-Effectiveness Analysis Example

Author

Listed:
  • Fernando Alarid-Escudero

    (Department of Health Policy, School of Medicine, and Stanford Health Policy, Freeman-Spogli Institute for International Studies, Stanford University, Stanford, California, USA
    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
    Erasmus School of Health Policy and Management, Erasmus University Rotterdam)

  • 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, Ontario, Canada)

  • Hawre Jalal

    (University of Toronto, Toronto, Ontario, Canada (PP); University of Ottawa, Ottawa, Ontario, Canada)

Abstract

In an introductory tutorial, we illustrated building cohort state-transition models (cSTMs) in R, where the state transition probabilities were constant over time. However, in practice, many cSTMs require transitions, rewards, or both to vary over time (time dependent). This tutorial illustrates adding 2 types of time dependence using a previously published cost-effectiveness analysis of multiple strategies as an example. The first is simulation-time dependence, which allows for the transition probabilities to vary as a function of time as measured since the start of the simulation (e.g., varying probability of death as the cohort ages). The second is state-residence time dependence, allowing for history by tracking the time spent in any particular health state using tunnel states. We use these time-dependent cSTMs to conduct cost-effectiveness and probabilistic sensitivity analyses. We also obtain various epidemiological outcomes of interest from the outputs generated from the cSTM, such as survival probability and disease prevalence, often used for model calibration and validation. We present the mathematical notation first, followed by the R code to execute the calculations. The full R code is provided in a public code repository for broader implementation.

Suggested Citation

  • Fernando Alarid-Escudero & Eline Krijkamp & Eva A. Enns & Alan Yang & M. G. Myriam Hunink & Petros Pechlivanoglou & Hawre Jalal, 2023. "A Tutorial on Time-Dependent Cohort State-Transition Models in R Using a Cost-Effectiveness Analysis Example," Medical Decision Making, , vol. 43(1), pages 21-41, January.
    • Handle: RePEc:sae:medema:v:43:y:2023:i:1:p:21-41
    DOI: 10.1177/0272989X221121747
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    References listed on IDEAS

    as
    1. 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.
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    3. 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.
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