Microsimulation Model Calibration with Approximate Bayesian Computation in R: A Tutorial

Peter Shewmaker, Stavroula A. Chrysanthopoulou, Rowan Iskandar, Derek Lake and Earic Jutkowitz
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Peter Shewmaker: Center for Gerontology and Healthcare Research, Brown University, Providence, RI, USA
Stavroula A. Chrysanthopoulou: Center for Statistical Sciences, Brown University, Providence, RI, USA
Rowan Iskandar: Center for Evidence Synthesis in Health, Brown University, Providence, RI, USA
Derek Lake: Center for Gerontology and Healthcare Research, Brown University, Providence, RI, USA
Earic Jutkowitz: Center for Gerontology and Healthcare Research, Brown University, Providence, RI, USA

Medical Decision Making, 2022, vol. 42, issue 5, 557-570

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.

Keywords: approximate Bayesian computation; calibration; dementia; microsimulation (search for similar items in EconPapers)
Date: 2022
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Citations: View citations in EconPapers (1)

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Persistent link: https://EconPapers.repec.org/RePEc:sae:medema:v:42:y:2022:i:5:p:557-570

DOI: 10.1177/0272989X221085569

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