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

Abstract

<jats:p> 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. </jats:p>

Guidance for the Harmonisation and Improvement of Economic Evaluations of Personalised Medicine

“…Nonetheless, it is acknowledged that the rise of PM has been argued to call for a more widespread use of patient-level modelling (as opposed to cohort-level modelling, which is currently most prevalent), owing to the former’s ability to simulate a greater variety of clinical pathways and easily include patient history into the analysis [ 75 , 77 ]. In patient-level modelling, in addition to considering the parameter and structural uncertainty that are discussed in the guidance, special attention should be paid to addressing patient heterogeneity and stochastic uncertainty [ 78 ].…”

Discrete Event Simulation Model for Cost-Effectiveness Evaluation of Screening for Asymptomatic Patients with Lower Extremity Arterial Disease

“…This assumption was determined based on the study by Simpson et al [ 20 ]. In order to maintain consistency in patient survival when events affecting patient survival occur, the procedure proposed by Corro Ramos et al [ 22 ] was used (e.g., that the survival of patients does not increase when IC occurs). All model structure setting values are listed in the Supplementary Material in Tables S2 and S3 .…”