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. 2022 Jul;42(5):557-570.
doi: 10.1177/0272989X221085569. Epub 2022 Mar 21.

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

Peter Shewmaker  1 , Stavroula A Chrysanthopoulou  2 , Rowan Iskandar  3   4 , Derek Lake  1 , Earic Jutkowitz  1   5
Affiliations

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

Peter Shewmaker et al. Med Decis Making. 2022 Jul.

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.

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Figures

Figure 1.
Figure 1.
ABC Rejection Sampler Algorithm
Figure 2.
Figure 2.
ABC-MCMC Algorithm
Figure 3.
Figure 3.
ABC Rejection Sampler Results: Place of Death Calibration Target * * Boxplot whiskers represent the 90% credible interval for the distribution, instead of the common usage of 1.5 times the interquartile range.
Figure 4.
Figure 4.
ABC MCMC Results: Place of Death Calibration Target * * Boxplot whiskers represent the 90% credible interval for the distribution, instead of the common usage of 1.5 times the interquartile range.
See this image and copyright information in PMC

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