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Estimating Parameters of a Microsimulation Model for Breast Cancer Screening Using the Score Function Method

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  • Sita Tan
  • Gerrit van Oortmarssen
  • Nanda Piersma

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 (randomized) trials. 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 for breast cancer screening that is fitted to data from the HIP randomized trial for early detection of breast cancer. Comparison of the parameter values estimated using 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 sample of simulated life histories, and on the number of parameters estimated. Using analytical representations for parts of the microsimulation model can increase the precision of the estimated parameter values. 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 SF method in an (existing) simulation model may well be worth the effort. Copyright Kluwer Academic Publishers 2003

Suggested Citation

  • Sita Tan & Gerrit van Oortmarssen & Nanda Piersma, 2003. "Estimating Parameters of a Microsimulation Model for Breast Cancer Screening Using the Score Function Method," Annals of Operations Research, Springer, vol. 119(1), pages 43-61, March.
    • Handle: RePEc:spr:annopr:v:119:y:2003:i:1:p:43-61:10.1023/a:1022922204299
    DOI: 10.1023/A:1022922204299
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    Cited by:

    1. Tazio Vanni & Jonathan Karnon & Jason Madan & Richard White & W. Edmunds & Anna Foss & Rosa Legood, 2011. "Calibrating Models in Economic Evaluation," PharmacoEconomics, Springer, vol. 29(1), pages 35-49, January.
    2. Davis, Peter & Lay-Yee, Roy & Pearson, Janet, 2010. "Using micro-simulation to create a synthesised data set and test policy options: The case of health service effects under demographic ageing," Health Policy, Elsevier, vol. 97(2-3), pages 267-274, October.
    3. Jeroen J. van den Broek & Nicolien T. van Ravesteyn & Eveline A. Heijnsdijk & Harry J. de Koning, 2018. "Simulating the Impact of Risk-Based Screening and Treatment on Breast Cancer Outcomes with MISCAN-Fadia," Medical Decision Making, , vol. 38(1_suppl), pages 54-65, April.
    4. Joost van Rosmalen & Mehlika Toy & James F. O’Mahony, 2013. "A Mathematical Approach for Evaluating Markov Models in Continuous Time without Discrete-Event Simulation," Medical Decision Making, , vol. 33(6), pages 767-779, August.
    5. Carolyn M. Rutter & Alan M. Zaslavsky & Eric J. Feuer, 2011. "Dynamic Microsimulation Models for Health Outcomes," Medical Decision Making, , vol. 31(1), pages 10-18, January.

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