ArticleMedical decision making : an international journal of the Society for Medical Decision Making2022
Microsimulation Model Calibration with Approximate Bayesian Computation in R: A Tutorial.
Article in Medical decision making : an international journal of the Society for Medical Decision Making, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers, 1 of them a synthesis that pooled it.
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Who cites it
10 citing papers in PubMed, 1 synthesis or guideline pooled it.
- A Systematic Review of Microsimulation Models in Cardiometabolic Disease: Model Calibration and Validation.Applied health economics and health policy · 2026Pooled it
- How Applied Researchers Engage with Methodological Tutorials: A Three-step Test Interview Study of an R Tutorial for Trial-Based Economic Evaluation.PharmacoEconomics - open · 2026Article
- Discrete-Event Simulation Modeling Framework for Cancer Interventions and Population Health in R (DESCIPHR): An Open-Source Pipeline.PharmacoEconomics · 2026Article
- Development of Disordered Weight Control Behaviors and Its Progression to Eating Disorders in Canada: A Nationally Representative Microsimulation.The International journal of eating disorders · 2026Article
- Developing and Calibrating a Colorectal Cancer Microsimulation Model for Northern Ireland.PharmacoEconomics · 2026Article
- Development of an asthma policy model for Canada: Lifetime Exposures and Asthma outcomes Projection.PloS one · 2026Article
- The cost of non-drug interventions that improve function and reduce dementia-related behaviors.BMC geriatrics · 2025Article
- Cost effectiveness of non-drug interventions that reduce nursing home admissions for people living with dementia.Alzheimer's & dementia : the journal of the Alzheimer's Association · 2023Article
- Calibration and Validation of the Colorectal Cancer and Adenoma Incidence and Mortality (CRC-AIM) Microsimulation Model Using Deep Neural Networks.Medical decision making : an international journal of the Society for Medical Decision Making · 2023Article
- Predicting and preventing COVID-19 outbreaks in indoor environments: an agent-based modeling study.Scientific reports · 2022Article
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Authors and funding
5 authors.
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Abstract
Mathematical health policy models, including microsimulation models (MSMs), are widely used to simulate complex processes and predict outcomes consistent with available data. Calibration is a method to estimate parameter values such that model predictions are similar to observed outcomes of interest. Bayesian calibration methods are popular among the available calibration techniques, given their strong theoretical basis and flexibility to incorporate prior beliefs and draw values from the posterior distribution of model parameters and hence the ability to characterize and evaluate parameter uncertainty in the model outcomes. Approximate Bayesian computation (ABC) is an approach to calibrate complex models in which the likelihood is intractable, focusing on measuring the difference between the simulated model predictions and outcomes of interest in observed data. Although ABC methods are increasingly being used, there is limited practical guidance in the medical decision-making literature on approaches to implement ABC to calibrate MSMs. In this tutorial, we describe the Bayesian calibration framework, introduce the ABC approach, and provide step-by-step guidance for implementing an ABC algorithm to calibrate MSMs, using 2 case examples based on a microsimulation model for dementia. We also provide the R code for applying these methods.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.