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A Mathematical Model Used to Analyze Breast Cancer Screening Strategies

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  • Michael Shwartz

    (Boston University, Boston, Massachusetts)

Abstract

A mathematical model of breast cancer is developed and used to evaluate the benefits of screening for breast cancer. This model consists of hypotheses concerning the age-specific incidence of the disease, the rate of disease progression, the tendency of the disease to be detected without benefit of scheduled screening examinations, and prognosis related to the extent of disease progression at treatment. We formulate these hypotheses quantitatively and estimate parameters by fitting the model statistically to published data on breast cancer. Model predictions are independently validated by comparison with data from breast cancer screening programs. On the basis of the model, we calculate the benefits of screening under alternative assumptions about the woman screened, the number of screens given, the ages at which the screens are given, the reliability of the screening technique, and the rate of disease progression. These calculations are then used to consider questions concerning the design of breast cancer screening strategies.

Suggested Citation

  • Michael Shwartz, 1978. "A Mathematical Model Used to Analyze Breast Cancer Screening Strategies," Operations Research, INFORMS, vol. 26(6), pages 937-955, December.
    • Handle: RePEc:inm:oropre:v:26:y:1978:i:6:p:937-955
    DOI: 10.1287/opre.26.6.937
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    Cited by:

    1. Alireza Sabouri & Woonghee Tim Huh & Steven M. Shechter, 2017. "Screening Strategies for Patients on the Kidney Transplant Waiting List," Operations Research, INFORMS, vol. 65(5), pages 1131-1146, October.
    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. Sandra J. Lee & Marvin Zelen, 2008. "Mortality Modeling of Early Detection Programs," Biometrics, The International Biometric Society, vol. 64(2), pages 386-395, June.
    4. James F. O’Mahony & Joost van Rosmalen & Nino A. Mushkudiani & Frans-Willem Goudsmit & Marinus J. C. Eijkemans & Eveline A. M. Heijnsdijk & Ewout W. Steyerberg & J. Dik F. Habbema, 2015. "The Influence of Disease Risk on the Optimal Time Interval between Screens for the Early Detection of Cancer," Medical Decision Making, , vol. 35(2), pages 183-195, February.
    5. Jonathan E. Helm & Mariel S. Lavieri & Mark P. Van Oyen & Joshua D. Stein & David C. Musch, 2015. "Dynamic Forecasting and Control Algorithms of Glaucoma Progression for Clinician Decision Support," Operations Research, INFORMS, vol. 63(5), pages 979-999, October.
    6. Sharareh Taghipour & Laurent N. Caudrelier & Anthony B. Miller & Bart Harvey, 2017. "Using Simulation to Model and Validate Invasive Breast Cancer Progression in Women in the Study and Control Groups of the Canadian National Breast Screening Studies I and II," Medical Decision Making, , vol. 37(2), pages 212-223, February.
    7. Süleyman Özekici & Talin Papazyan, 1988. "Inspection policies and processes for deteriorating systems subject to catastrophic failure," Naval Research Logistics (NRL), John Wiley & Sons, vol. 35(4), pages 481-492, August.
    8. Michael Shwartz, 1992. "Validation of a Model of Breast Cancer Screening," Medical Decision Making, , vol. 12(3), pages 222-228, August.
    9. Marion S. Rauner & Walter J. Gutjahr & Kurt Heidenberger & Joachim Wagner & Joseph Pasia, 2010. "Dynamic Policy Modeling for Chronic Diseases: Metaheuristic-Based Identification of Pareto-Optimal Screening Strategies," Operations Research, INFORMS, vol. 58(5), pages 1269-1286, October.

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