Agent based modeling in health care economics: examples in the field of thyroid cancer
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Romain Demeulemeester
,
Nicolas Savy
,
Pascale Grosclaude
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.
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Author contributions: All the authors have accepted responsibility for the entire content of this submitted manuscript and approved submission.
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Research funding: None declared.
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Conflict of interest statement: The authors declare no conflicts of interest regarding this article.
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© 2023 Walter de Gruyter GmbH, Berlin/Boston
Articles in the same Issue
- Frontmatter
- Part-1: SMAC 2021 Webconference
- Statistics, philosophy, and health: the SMAC 2021 webconference
- Part-2: Regular Articles
- “Show me the DAG!”
- Causal inference for oncology: past developments and current challenges
- The EBM+ movement
- Bayesianism from a philosophical perspective and its application to medicine
- Bayesian inference for optimal dynamic treatment regimes in practice
- Agent-based modeling in medical research, virtual baseline generator and change in patients’ profile issue
- Agent based modeling in health care economics: examples in the field of thyroid cancer
- A copula-based set-variant association test for bivariate continuous, binary or mixed phenotypes
- Detection of atypical response trajectories in biomedical longitudinal databases
- Potential application of elastic nets for shared polygenicity detection with adapted threshold selection
- Error analysis of the PacBio sequencing CCS reads
- A SIMEX approach for meta-analysis of diagnostic accuracy studies with attention to ROC curves
- Statistical modelling of COVID-19 and drug data via an INAR(1) process with a recent thinning operator and cosine Poisson innovations
- The balanced discrete triplet Lindley model and its INAR(1) extension: properties and COVID-19 applications
Articles in the same Issue
- Frontmatter
- Part-1: SMAC 2021 Webconference
- Statistics, philosophy, and health: the SMAC 2021 webconference
- Part-2: Regular Articles
- “Show me the DAG!”
- Causal inference for oncology: past developments and current challenges
- The EBM+ movement
- Bayesianism from a philosophical perspective and its application to medicine
- Bayesian inference for optimal dynamic treatment regimes in practice
- Agent-based modeling in medical research, virtual baseline generator and change in patients’ profile issue
- Agent based modeling in health care economics: examples in the field of thyroid cancer
- A copula-based set-variant association test for bivariate continuous, binary or mixed phenotypes
- Detection of atypical response trajectories in biomedical longitudinal databases
- Potential application of elastic nets for shared polygenicity detection with adapted threshold selection
- Error analysis of the PacBio sequencing CCS reads
- A SIMEX approach for meta-analysis of diagnostic accuracy studies with attention to ROC curves
- Statistical modelling of COVID-19 and drug data via an INAR(1) process with a recent thinning operator and cosine Poisson innovations
- The balanced discrete triplet Lindley model and its INAR(1) extension: properties and COVID-19 applications
