Agent based modeling in health care economics: examples in the field of thyroid cancer

Demeulemeester Romain (), Savy Nicolas (), Grosclaude Pascale (), Costa Nadège () and Saint-Pierre Philippe ()
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Demeulemeester Romain: Health Economic Evaluation Unit, Medical Information Department, University of Toulouse, University Hospital of Toulouse, UMR 1295 Centre d’Epidémiologie et de Recherche en santé des POPulations, National Institute for Health and Medical Research, Toulouse, France
Savy Nicolas: Toulouse Institute of Mathematics, University of Toulouse III and IFERISS FED 4142, University of Toulouse, Toulouse, France
Grosclaude Pascale: University of Toulouse, UMR 1295 Centre d’Epidémiologie et de Recherche en santé des POPulations, National Institute for Health and Medical Research, Toulouse, France
Costa Nadège: Health Economic Evaluation Unit, Medical Information Department, University of Toulouse, University Hospital of Toulouse, UMR 1295 Centre d’Epidémiologie et de Recherche en santé des POPulations, National Institute for Health and Medical Research, Toulouse, France
Saint-Pierre Philippe: Toulouse Institute of Mathematics, University of Toulouse III and IFERISS FED 4142, University of Toulouse, Toulouse, France

The International Journal of Biostatistics, 2023, vol. 19, issue 2, 351-368

Abstract: Although they remain little used in the field of Health Care Economics, Agent Based Models (ABM) are potentially powerful decision-making tools that open up great prospects. The reasons for this lack of popularity are essentially to be found in a methodology that should be further clarified. This article hence aims to illustrate the methodology by means of two applications to medical examples. The first example of ABM illustrates the construction of a Baseline Data Cohort by means of a Virtual Baseline Generator. The aim is to describe the prevalence of thyroid cancer in the French population over the long term according to different scenarios of evolution of this population. The second study considers a setting where the Baseline Data Cohort is an established cohort of (real) patients: the EVATHYR cohort. The aim of the ABM is to describe the long-term costs associated with different scenarios of thyroid cancer management. The results are evaluated using several simulation runs in order to observe the variability of simulations and to derive prediction intervals. The ABM approach is very flexible since several sources of data can be involved and a large variety of simulation models can be calibrated to generate observations according to different evolution scenarios.

Keywords: agent-based modeling; in silico clinical trials; thyroid cancer; virtual baseline generator (search for similar items in EconPapers)
Date: 2023
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DOI: 10.1515/ijb-2022-0107

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