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Simulation optimization of PSA-threshold based prostate cancer screening policies

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Abstract

We describe a simulation optimization method to design PSA screening policies based on expected quality adjusted life years (QALYs). Our method integrates a simulation model in a genetic algorithm which uses a probabilistic method for selection of the best policy. We present computational results about the efficiency of our algorithm. The best policy generated by our algorithm is compared to previously recommended screening policies. Using the policies determined by our model, we present evidence that patients should be screened more aggressively but for a shorter length of time than previously published guidelines recommend.

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Acknowledgements

This material is based in part upon work supported by the National Science Foundation under Grant Number CMMI 0844511. Any opinions, findings, and conclusions or recommendations expressed in this material are those of the authors and do not necessarily reflect the views of the National Science Foundation. This study was also made possible by the Rochester Epidemiology Project (Grant #R01 - AG034676-47 from the National Institute of Aging). We wish to thank two anonymous reviewers for their constructive comments, which were instrumental in improving this manuscript.

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Authors and Affiliations

  1. Edward P. Fitts Department of Industrial & Systems Engineering, North Carolina State University, Raleigh, NC, 27695, USA

    Daniel J. Underwood & Brian T. Denton

  2. Philips Research North America, Briarcliff Manor, NY, 10510, USA

    Jingyu Zhang

  3. Division of Health Care Policy and Research, Mayo Clinic, Rochester, MN, 55905, USA

    Nilay D. Shah

  4. Division of Urology, Duke University Medical Center, Durham, NC, 27710, USA

    Brant A. Inman

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  1. Daniel J. Underwood
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Correspondence to Daniel J. Underwood.

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Underwood, D.J., Zhang, J., Denton, B.T. et al. Simulation optimization of PSA-threshold based prostate cancer screening policies. Health Care Manag Sci 15, 293–309 (2012). https://doi.org/10.1007/s10729-012-9195-x

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