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How to Address Uncertainty in Health Economic Discrete-Event Simulation Models: An Illustration for Chronic Obstructive Pulmonary Disease

Author

Listed:
  • Isaac Corro Ramos

    (Institute for Medical Technology Assessment (iMTA), Erasmus University Rotterdam, Rotterdam, Zuid-Holland, The Netherlands)

  • Martine Hoogendoorn

    (Institute for Medical Technology Assessment (iMTA), Erasmus University Rotterdam, Rotterdam, Zuid-Holland, The Netherlands)

  • Maureen P. M. H. Rutten-van Mölken

    (Institute for Medical Technology Assessment (iMTA), Erasmus University Rotterdam, Rotterdam, Zuid-Holland, The Netherlands
    Erasmus School of Health Policy & Management (ESHPM), Erasmus University Rotterdam, Rotterdam, The Netherlands)

Abstract

Background . Evaluation of personalized treatment options requires health economic models that include multiple patient characteristics. Patient-level discrete-event simulation (DES) models are deemed appropriate because of their ability to simulate a variety of characteristics and treatment pathways. However, DES models are scarce in the literature, and details about their methods are often missing. Methods . We describe 4 challenges associated with modeling heterogeneity and structural, stochastic, and parameter uncertainty that can be encountered during the development of DES models. We explain why these are important and how to correctly implement them. To illustrate the impact of the modeling choices discussed, we use (results of) a model for chronic obstructive pulmonary disease (COPD) as a case study. Results . The results from the case study showed that, under a correct implementation of the uncertainty in the model, a hypothetical intervention can be deemed as cost-effective. The consequences of incorrect modeling uncertainty included an increase in the incremental cost-effectiveness ratio ranging from 50% to almost a factor of 14, an extended life expectancy of approximately 1.4 years, and an enormously increased uncertainty around the model outcomes. Thus, modeling uncertainty incorrectly can have substantial implications for decision making. Conclusions . This article provides guidance on the implementation of uncertainty in DES models and improves the transparency of reporting uncertainty methods. The COPD case study illustrates the issues described in the article and helps understanding them better. The model R code shows how the uncertainty was implemented. For readers not familiar with R, the model’s pseudo-code can be used to understand how the model works. By doing this, we can help other developers, who are likely to face similar challenges to those described here.

Suggested Citation

  • Isaac Corro Ramos & Martine Hoogendoorn & Maureen P. M. H. Rutten-van Mölken, 2020. "How to Address Uncertainty in Health Economic Discrete-Event Simulation Models: An Illustration for Chronic Obstructive Pulmonary Disease," Medical Decision Making, , vol. 40(5), pages 619-632, July.
    • Handle: RePEc:sae:medema:v:40:y:2020:i:5:p:619-632
    DOI: 10.1177/0272989X20932145
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    References listed on IDEAS

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    1. Irmgard C. Schiller-Frühwirth & Beate Jahn & Marjan Arvandi & Uwe Siebert, 2017. "Cost-Effectiveness Models in Breast Cancer Screening in the General Population: A Systematic Review," Applied Health Economics and Health Policy, Springer, vol. 15(3), pages 333-351, June.
    2. Alan Brennan & Stephen E. Chick & Ruth Davies, 2006. "A taxonomy of model structures for economic evaluation of health technologies," Health Economics, John Wiley & Sons, Ltd., vol. 15(12), pages 1295-1310, December.
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    1. Ortiz-Barrios, Miguel & Arias-Fonseca, Sebastián & Ishizaka, Alessio & Barbati, Maria & Avendaño-Collante, Betty & Navarro-Jiménez, Eduardo, 2023. "Artificial intelligence and discrete-event simulation for capacity management of intensive care units during the Covid-19 pandemic: A case study," Journal of Business Research, Elsevier, vol. 160(C).

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