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

Fernando Alarid-Escudero, Eline Krijkamp, Eva A. Enns, Alan Yang, M. G. Myriam Hunink, Petros Pechlivanoglou and Hawre Jalal
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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
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
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

Medical Decision Making, 2023, vol. 43, issue 1, 21-41

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.

Keywords: cohort state-transition models; cost-effectiveness analysis; markov models; R software; time-dependent; tutorial (search for similar items in EconPapers)
Date: 2023
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Persistent link: https://EconPapers.repec.org/RePEc:sae:medema:v:43:y:2023:i:1:p:21-41

DOI: 10.1177/0272989X221121747

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