Open Access
December 2019 Microsimulation model calibration using incremental mixture approximate Bayesian computation
Carolyn M. Rutter, Jonathan Ozik, Maria DeYoreo, Nicholson Collier
Ann. Appl. Stat. 13(4): 2189-2212 (December 2019). DOI: 10.1214/19-AOAS1279


Microsimulation models (MSMs) are used to inform policy by predicting population-level outcomes under different scenarios. MSMs simulate individual-level event histories that mark the disease process (such as the development of cancer) and the effect of policy actions (such as screening) on these events. MSMs often have many unknown parameters; calibration is the process of searching the parameter space to select parameters that result in accurate MSM prediction of a wide range of targets. We develop Incremental Mixture Approximate Bayesian Computation (IMABC) for MSM calibration which results in a simulated sample from the posterior distribution of model parameters given calibration targets. IMABC begins with a rejection-based ABC step, drawing a sample of points from the prior distribution of model parameters and accepting points that result in simulated targets that are near observed targets. Next, the sample is iteratively updated by drawing additional points from a mixture of multivariate normal distributions and accepting points that result in accurate predictions. Posterior estimates are obtained by weighting the final set of accepted points to account for the adaptive sampling scheme. We demonstrate IMABC by calibrating CRC-SPIN 2.0, an updated version of a MSM for colorectal cancer (CRC) that has been used to inform national CRC screening guidelines.


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Carolyn M. Rutter. Jonathan Ozik. Maria DeYoreo. Nicholson Collier. "Microsimulation model calibration using incremental mixture approximate Bayesian computation." Ann. Appl. Stat. 13 (4) 2189 - 2212, December 2019.


Received: 1 August 2018; Revised: 1 June 2019; Published: December 2019
First available in Project Euclid: 28 November 2019

zbMATH: 07160936
MathSciNet: MR4037427
Digital Object Identifier: 10.1214/19-AOAS1279

Keywords: Adaptive ABC , agent-based models , colorectal cancer

Rights: Copyright © 2019 Institute of Mathematical Statistics

Vol.13 • No. 4 • December 2019
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