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Estimting parameters of a microsimulation model for breast cancer screening using the score function method

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  • Tan, S.Y.G.L.
  • van Oortmarssen, G.J.
  • Piersma, N.

Abstract

In developing decision-making models for the evaluation of medical procedures, the model parameters can be estimated by fitting the model to data observed in trial studies. For complex models that are implemented by discrete event simulation (microsimulation) of individual life histories, the Score Function (SF) method can potentially be an appropriate approach for such estimation exercises. We test this approach for a microsimulation model of screening for cancer that is fitted to data from the HIP randomized trial for early detection of breast cancer. Comparison of the parameter values estimated by the SF method and the analytical solution shows that method performs well on this simple model. The precision of the estimated parameter values depends (as expected) on the size of the simulation number of life histories), and on the number of parameters estimated. Using analytical representations for parts of the microsimulation model can increase the precision in the estimation of the remaining parameters. Compared to the Nelder and Mead Simplex method which is often used in stochastic simulation because of its ease of implementation, the SF method is clearly more efficient (ratio computer time: precision of estimates). The additional analytical investment needed to implement the method in an (existing) simulation model may well be worth the effort.

Suggested Citation

  • Tan, S.Y.G.L. & van Oortmarssen, G.J. & Piersma, N., 2000. "Estimting parameters of a microsimulation model for breast cancer screening using the score function method," Econometric Institute Research Papers EI 2000-35/A, Erasmus University Rotterdam, Erasmus School of Economics (ESE), Econometric Institute.
  • Handle: RePEc:ems:eureir:1665
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    References listed on IDEAS

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    1. Antonio G. Chessa & Rommert Dekker & Bas Van Vliet & Ewout W. Steyerberg & J. Dik F. Habbema, 1999. "Correlations in Uncertainty Analysis for Medical Decision Making," Medical Decision Making, , vol. 19(3), pages 276-286, August.
    2. Kleijnen, J.P.C. & Rubinstein, R.Y., 1996. "Optimization and Sensitivity Analysis of Computer Simulation Models by the Score Function Method," Other publications TiSEM 958c9b9a-544f-48f3-a3d1-c, Tilburg University, School of Economics and Management.
    3. Kleijnen, Jack P. C. & Rubinstein, Reuven Y., 1996. "Optimization and sensitivity analysis of computer simulation models by the score function method," European Journal of Operational Research, Elsevier, vol. 88(3), pages 413-427, February.
    4. Neddermeijer, H.G. & van Oortmarssen, G.J. & Piersma, N. & Dekker, R. & Habbema, J.D.F., 2000. "Adaptive extensions of the Nelder and Mead Simplex Method for optimization of stochastic simulation models," Econometric Institute Research Papers EI 2000-22/A, Erasmus University Rotterdam, Erasmus School of Economics (ESE), Econometric Institute.
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