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    Calibrating Parameters for Microsimulation Disease Models

    Access Status
    Fulltext not available
    Authors
    Van Der Steen, A.
    Van Rosmalen, J.
    Kroep, S.
    Van Hees, F.
    Steyerberg, E.
    De Koning, H.
    Van Ballegooijen, M.
    Lansdorp_Vogelaar, Iris
    Date
    2015
    Type
    Journal Article
    
    Metadata
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    Citation
    Van Der Steen, A. and Van Rosmalen, J. and Kroep, S. and Van Hees, F. and Steyerberg, E. and De Koning, H. and Van Ballegooijen, M. et al. 2015. Calibrating Parameters for Microsimulation Disease Models. Medical Decision Making. 36 (5): pp. 652-665.
    Source Title
    Medical Decision Making
    DOI
    10.1177/0272989X16636851
    ISSN
    0272-989X
    URI
    http://hdl.handle.net/20.500.11937/50157
    Collection
    • Curtin Research Publications
    Abstract

    Background: Calibration (estimation of model parameters) compares model outcomes with observed outcomes and explores possible model parameter values to identify the set of values that provides the best fit to the data. The goodness-of-fit (GOF) criterion quantifies the difference between model and observed outcomes. There is no consensus on the most appropriate GOF criterion, because a direct performance comparison of GOF criteria in model calibration is lacking. Methods: We systematically compared the performance of commonly used GOF criteria (sum of squared errors [SSE], Pearson chi-square, and a likelihood-based approach [Poisson and/or binomial deviance functions]) in the calibration of selected parameters of the MISCAN-Colon microsimulation model for colorectal cancer. The performance of each GOF criterion was assessed by comparing the 1) root mean squared prediction error (RMSPE) of the selected parameters, 2) computation time of the calibration procedure of various calibration scenarios, and 3) impact on estimated cost-effectiveness ratios. Results: The likelihood-based deviance resulted in the lowest RMSPE in 4 of 6 calibration scenarios and was close to best in the other 2. The SSE had a 25 times higher RMSPE in a scenario with considerable differences in the values of observed outcomes, whereas the Pearson chi-square had a 60 times higher RMSPE in a scenario with multiple studies measuring the same outcome. In all scenarios, the SSE required the most computation time. The likelihood-based approach estimated the cost-effectiveness ratio most accurately (up to â'0.15% relative difference versus 0.44% [SSE] and 13% [Pearson chi-square]). Conclusions: The likelihood-based deviance criteria lead to accurate estimation of parameters under various circumstances. These criteria are recommended for calibration in microsimulation disease models in contrast with other commonly used criteria.

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