Microsimulation models are increasingly used in the evaluation of cancer screening. Latent parameters of such models can be estimated by optimization of the goodness-of-fit. We compared the efficiency and accuracy of the Response Surface Methodology and the Nelder and Mead Simplex Method for optimization of microsimulation models. To this end, we tested several automated versions of both methods on a small microsimulation model, as well as on a standard set of test functions. With respect to accuracy, Response Surface Methodology performed better in case of optimization of the microsimulation model, whereas the results for the test functions were rather variable. The Nelder and Mead Simplex Method performed more efficiently than Response Surface Methodology, both for the microsimulation model and the test functions.
Download Info
To download:
If you experience problems downloading a file, check if you have the
proper application to
view it first. Information about this may be contained
in the File-Format links below. In case of further problems read
the IDEAS help
page. Note that these files are not on the IDEAS
site. Please be patient as the files may be large.
Publisher Info
Paper provided by Erasmus University Rotterdam, Econometric Institute in its series Econometric Institute Report with number
EI 9924-/A Revision_Date: 2009-12-02.
References listed on IDEAS Please report citation or reference errors to , or , if you are the registered author of the cited work, log in to your RePEc Author Service profile, click on "citations" and make appropriate adjustments.: