  Microsimulation models (MSMs) are used to predict population-level effects of
health care policies by simulating individual-level outcomes. Simulated
outcomes are governed by unknown parameters that are chosen so that the model
accurately predicts specific targets, a process referred to as model
calibration. Calibration targets can come from randomized controlled trials,
observational studies, and expert opinion, and are typically summary
statistics. A well calibrated model can reproduce a wide range of targets. MSM
calibration generally involves searching a high dimensional parameter space and
predicting many targets through model simulation. This requires efficient
methods for exploring the parameter space and sufficient computational
resources. We develop Incremental Mixture Approximate Bayesian Computation
(IMABC) as a method for MSM calibration and implement it via a high-performance
computing workflow, which provides the necessary computational scale. IMABC
begins with a rejection-based approximate Bayesian computation (ABC) step,
drawing a sample of parameters from the prior distribution and simulating
calibration targets. Next, the sample is iteratively updated by drawing
additional points from a mixture of multivariate normal distributions, centered
at the points that yield simulated targets that are near observed targets.
Posterior estimates are obtained by weighting sampled parameter vectors to
account for the adaptive sampling scheme. We demonstrate IMABC by calibrating a
MSM for the natural history of colorectal cancer to obtain simulated draws from
the joint posterior distribution of model parameters.
