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Title: Microsimulation model calibration using incremental mixture approximate Bayesian computation

Abstract

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 nationalmore » CRC screening guidelines.« less

Authors:
 [1];  [2];  [1];  [2]
  1. RAND Corporation, Santa Monica, CA (United States)
  2. Argonne National Lab. (ANL), Argonne, IL (United States)
Publication Date:
Research Org.:
Argonne National Lab. (ANL), Argonne, IL (United States)
Sponsoring Org.:
National Institutes of Health (NIH) - National Institute of General Medical Sciences; National Institutes of Health (NIH) - National Cancer Institute; USDOE
OSTI Identifier:
1607644
Grant/Contract Number:  
AC02-06CH11357
Resource Type:
Accepted Manuscript
Journal Name:
The Annals of Applied Statistics
Additional Journal Information:
Journal Volume: 13; Journal Issue: 4; Journal ID: ISSN 1932-6157
Publisher:
Institute of Mathematical Statistics
Country of Publication:
United States
Language:
English
Subject:
97 MATHEMATICS AND COMPUTING; Adaptive ABC; agent-based models; colorectal cancer

Citation Formats

Rutter, Carolyn, Ozik, Jonathan, DeYoreo, Maria, and Collier, Nicholson. Microsimulation model calibration using incremental mixture approximate Bayesian computation. United States: N. p., 2019. Web. https://doi.org/10.1214/19-AOAS1279.
Rutter, Carolyn, Ozik, Jonathan, DeYoreo, Maria, & Collier, Nicholson. Microsimulation model calibration using incremental mixture approximate Bayesian computation. United States. https://doi.org/10.1214/19-AOAS1279
Rutter, Carolyn, Ozik, Jonathan, DeYoreo, Maria, and Collier, Nicholson. Sun . "Microsimulation model calibration using incremental mixture approximate Bayesian computation". United States. https://doi.org/10.1214/19-AOAS1279. https://www.osti.gov/servlets/purl/1607644.
@article{osti_1607644,
title = {Microsimulation model calibration using incremental mixture approximate Bayesian computation},
author = {Rutter, Carolyn and Ozik, Jonathan and DeYoreo, Maria and Collier, Nicholson},
abstractNote = {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.},
doi = {10.1214/19-AOAS1279},
journal = {The Annals of Applied Statistics},
number = 4,
volume = 13,
place = {United States},
year = {2019},
month = {12}
}

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