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Simulation optimization of PSA-threshold based prostate cancer screening policies

Author

Listed:
  • Daniel Underwood
  • Jingyu Zhang
  • Brian Denton
  • Nilay Shah
  • Brant Inman

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. Copyright Springer Science+Business Media, LLC 2012

Suggested Citation

  • Daniel Underwood & Jingyu Zhang & Brian Denton & Nilay Shah & Brant Inman, 2012. "Simulation optimization of PSA-threshold based prostate cancer screening policies," Health Care Management Science, Springer, vol. 15(4), pages 293-309, December.
    • Handle: RePEc:kap:hcarem:v:15:y:2012:i:4:p:293-309
    DOI: 10.1007/s10729-012-9195-x
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    References listed on IDEAS

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    1. Jagpreet Chhatwal & Oguzhan Alagoz & Elizabeth S. Burnside, 2010. "Optimal Breast Biopsy Decision-Making Based on Mammographic Features and Demographic Factors," Operations Research, INFORMS, vol. 58(6), pages 1577-1591, December.
    2. Lisa M. Maillart & Julie Simmons Ivy & Scott Ransom & Kathleen Diehl, 2008. "Assessing Dynamic Breast Cancer Screening Policies," Operations Research, INFORMS, vol. 56(6), pages 1411-1427, December.
    3. Ruth Etzioni & Roman Gulati & Seth Falcon & David F. Penson, 2008. "Impact of PSA Screening on the Incidence of Advanced Stage Prostate Cancer in the United States: A Surveillance Modeling Approach," Medical Decision Making, , vol. 28(3), pages 323-331, May.
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    Cited by:

    1. Dimitris Bertsimas & John Silberholz & Thomas Trikalinos, 2018. "Optimal healthcare decision making under multiple mathematical models: application in prostate cancer screening," Health Care Management Science, Springer, vol. 21(1), pages 105-118, March.
    2. Zheng Zhang & Brian T. Denton & Todd M. Morgan, 2022. "Optimization of active surveillance strategies for heterogeneous patients with prostate cancer," Production and Operations Management, Production and Operations Management Society, vol. 31(11), pages 4021-4037, November.
    3. Elliot Lee & Mariel S. Lavieri & Michael Volk, 2019. "Optimal Screening for Hepatocellular Carcinoma: A Restless Bandit Model," Service Science, INFORMS, vol. 21(1), pages 198-212, January.
    4. Arthur J. Swersey & John Colberg & Ronald Evans & Michael W. Kattan & Johannes Ledolter & Rodney Parker, 2020. "Decision models for distinguishing between clinically insignificant and significant tumors in prostate cancer biopsies: an application of Bayes’ Theorem to reduce costs and improve outcomes," Health Care Management Science, Springer, vol. 23(1), pages 102-116, March.

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