Agent-based modeling in medical research, virtual baseline generator and change in patients’ profile issue
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Philippe Saint-Pierre
and
Nicolas Savy
Abstract
Simulation studies are promising in medical research in particular to improve drug development. For instance, one can aim to develop In Silico Clinical Trial in order to challenge trial’s design parameters in terms of feasibility and probability of success of the trial. Approaches based on agent-based models draw on a particularly useful framework to simulate patients evolution. In this paper, an approach based on agent-based modeling is described and discussed in the context of medical research. An R-vine copula model is used to represent the multivariate distribution of the data. A baseline data cohort can then be simulated and execution models can be developed to simulate the evolution of patients. R-vine copula models are very flexible tools which allow researchers to consider different marginal distributions than the ones observed in the data. It is then possible to perform data augmentation to explore a new population by simulating baseline data which are slightly different than those of the original population. A simulation study illustrates the efficiency of copula modeling to generate data according to specific marginal distributions but also highlights difficulties inherent to data augmentation.
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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
