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Bayesian Methods for Calibrating Health Policy Models: A Tutorial

Author

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  • Nicolas A. Menzies

    (Harvard T.H. Chan School of Public Health
    Harvard T.H. Chan School of Public Health)

  • Djøra I. Soeteman

    (Harvard T.H. Chan School of Public Health)

  • Ankur Pandya

    (Harvard T.H. Chan School of Public Health
    Harvard T.H. Chan School of Public Health)

  • Jane J. Kim

    (Harvard T.H. Chan School of Public Health
    Harvard T.H. Chan School of Public Health)

Abstract

Mathematical simulation models are commonly used to inform health policy decisions. These health policy models represent the social and biological mechanisms that determine health and economic outcomes, combine multiple sources of evidence about how policy alternatives will impact those outcomes, and synthesize outcomes into summary measures salient for the policy decision. Calibrating these health policy models to fit empirical data can provide face validity and improve the quality of model predictions. Bayesian methods provide powerful tools for model calibration. These methods summarize information relevant to a particular policy decision into (1) prior distributions for model parameters, (2) structural assumptions of the model, and (3) a likelihood function created from the calibration data, combining these different sources of evidence via Bayes’ theorem. This article provides a tutorial on Bayesian approaches for model calibration, describing the theoretical basis for Bayesian calibration approaches as well as pragmatic considerations that arise in the tasks of creating calibration targets, estimating the posterior distribution, and obtaining results to inform the policy decision. These considerations, as well as the specific steps for implementing the calibration, are described in the context of an extended worked example about the policy choice to provide (or not provide) treatment for a hypothetical infectious disease. Given the many simplifications and subjective decisions required to create prior distributions, model structure, and likelihood, calibration should be considered an exercise in creating a reasonable model that produces valid evidence for policy, rather than as a technique for identifying a unique theoretically optimal summary of the evidence.

Suggested Citation

  • Nicolas A. Menzies & Djøra I. Soeteman & Ankur Pandya & Jane J. Kim, 2017. "Bayesian Methods for Calibrating Health Policy Models: A Tutorial," PharmacoEconomics, Springer, vol. 35(6), pages 613-624, June.
  • Handle: RePEc:spr:pharme:v:35:y:2017:i:6:d:10.1007_s40273-017-0494-4
    DOI: 10.1007/s40273-017-0494-4
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    References listed on IDEAS

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    1. Elisabeth Fenwick & Bernie J. O'Brien & Andrew Briggs, 2004. "Cost‐effectiveness acceptability curves – facts, fallacies and frequently asked questions," Health Economics, John Wiley & Sons, Ltd., vol. 13(5), pages 405-415, May.
    2. Andrew H. Briggs, 1999. "A Bayesian approach to stochastic cost‐effectiveness analysis," Health Economics, John Wiley & Sons, Ltd., vol. 8(3), pages 257-261, May.
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    1. C Marijn Hazelbag & Jonathan Dushoff & Emanuel M Dominic & Zinhle E Mthombothi & Wim Delva, 2020. "Calibration of individual-based models to epidemiological data: A systematic review," PLOS Computational Biology, Public Library of Science, vol. 16(5), pages 1-17, May.
    2. Penny R. Breeze & Hazel Squires & Kate Ennis & Petra Meier & Kate Hayes & Nik Lomax & Alan Shiell & Frank Kee & Frank de Vocht & Martin O’Flaherty & Nigel Gilbert & Robin Purshouse & Stewart Robinson , 2023. "Guidance on the use of complex systems models for economic evaluations of public health interventions," Health Economics, John Wiley & Sons, Ltd., vol. 32(7), pages 1603-1625, July.
    3. Vahab Vahdat & Oguzhan Alagoz & Jing Voon Chen & Leila Saoud & Bijan J. Borah & Paul J. Limburg, 2023. "Calibration and Validation of the Colorectal Cancer and Adenoma Incidence and Mortality (CRC-AIM) Microsimulation Model Using Deep Neural Networks," Medical Decision Making, , vol. 43(6), pages 719-736, August.

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