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Nonidentifiability in Model Calibration and Implications for Medical Decision Making

Author

Listed:
  • Fernando Alarid-Escudero

    (Division of Health Policy and Management, University of Minnesota School of Public Health, Minneapolis, MN)

  • Richard F. MacLehose

    (Division of Epidemiology and Community Health, University of Minnesota School of Public Health, Minneapolis, MN)

  • Yadira Peralta

    (Department of Educational Psychology, University of Minnesota, Minneapolis, MN)

  • Karen M. Kuntz

    (Division of Health Policy and Management, University of Minnesota School of Public Health, Minneapolis, MN)

  • Eva A. Enns

    (Division of Health Policy and Management, University of Minnesota School of Public Health, Minneapolis, MN)

Abstract

Background. Calibration is the process of estimating parameters of a mathematical model by matching model outputs to calibration targets. In the presence of nonidentifiability, multiple parameter sets solve the calibration problem, which may have important implications for decision making. We evaluate the implications of nonidentifiability on the optimal strategy and provide methods to check for nonidentifiability. Methods. We illustrate nonidentifiability by calibrating a 3-state Markov model of cancer relative survival (RS). We performed 2 different calibration exercises: 1) only including RS as a calibration target and 2) adding the ratio between the 2 nondeath states over time as an additional target. We used the Nelder-Mead (NM) algorithm to identify parameter sets that best matched the calibration targets. We used collinearity and likelihood profile analyses to check for nonidentifiability. We then estimated the benefit of a hypothetical treatment in terms of life expectancy gains using different, but equally good-fitting, parameter sets. We also applied collinearity analysis to a realistic model of the natural history of colorectal cancer. Results. When only RS is used as the calibration target, 2 different parameter sets yield similar maximum likelihood values. The high collinearity index and the bimodal likelihood profile on both parameters demonstrated the presence of nonidentifiability. These different, equally good-fitting parameter sets produce different estimates of the treatment effectiveness (0.67 v. 0.31 years), which could influence the optimal decision. By incorporating the additional target, the model becomes identifiable with a collinearity index of 3.5 and a unimodal likelihood profile. Conclusions. In the presence of nonidentifiability, equally likely parameter estimates might yield different conclusions. Checking for the existence of nonidentifiability and its implications should be incorporated into standard model calibration procedures.

Suggested Citation

  • Fernando Alarid-Escudero & Richard F. MacLehose & Yadira Peralta & Karen M. Kuntz & Eva A. Enns, 2018. "Nonidentifiability in Model Calibration and Implications for Medical Decision Making," Medical Decision Making, , vol. 38(7), pages 810-821, October.
    • Handle: RePEc:sae:medema:v:38:y:2018:i:7:p:810-821
    DOI: 10.1177/0272989X18792283
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    References listed on IDEAS

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    1. Jack P.C. Kleijnen, 2015. "Design and Analysis of Simulation Experiments," International Series in Operations Research and Management Science, Springer, edition 2, number 978-3-319-18087-8, December.
    2. Alan Brennan & Stephen E. Chick & Ruth Davies, 2006. "A taxonomy of model structures for economic evaluation of health technologies," Health Economics, John Wiley & Sons, Ltd., vol. 15(12), pages 1295-1310, December.
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    Cited by:

    1. 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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