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

Isaac Corro Ramos, Martine Hoogendoorn and Maureen P. M. H. Rutten- van Mölken
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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

Medical Decision Making, 2020, vol. 40, issue 5, 619-632

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.

Keywords: patient-level model; discrete event simulation model; uncertainty; heterogeneity; COPD; personalized medicine (search for similar items in EconPapers)
Date: 2020
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Persistent link: https://EconPapers.repec.org/RePEc:sae:medema:v:40:y:2020:i:5:p:619-632

DOI: 10.1177/0272989X20932145

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