IDEAS home Printed from https://ideas.repec.org/a/sae/medema/v12y1992i3p222-228.html
   My bibliography  Save this article

Validation of a Model of Breast Cancer Screening

Author

Listed:
  • Michael Shwartz

Abstract

As part of a validation of a mathematical model of breast cancer, the Health Insurance Plan of Greater New York (HIP) randomized controlled trial of breast cancer screening and the uncontrolled Breast Cancer Detection Demonstration Projects (BCDDP) trial were simulated. Model predictions were in accord with the nine-year survival experience of women in the HIP trial, and, with the exception of women 40-44 years old, with HIP data on 18-year survival. Five-year survival rates of screen-detected cases in the BCDDP were very close to the model's predictions. However, the model did not predict the high survival rate of women who had interval cancers in the BCDDP. By the end of the BCDDP, almost 85% of the participants performed breast self-examination (BSE) regularly. Consistent with this obser vation, an appealing hypothesis to explain the high survival rate of women who had interval cancers is that BSE is of value in detecting breast cancers earlier. Consideration of model outliers can be of value in increasing understanding of the phenomena being modeled. Key words: breast self-examinations; breast cancer screening; mathematical models; model val idation. (Med Decis Making 1992;12:222-228)

Suggested Citation

  • Michael Shwartz, 1992. "Validation of a Model of Breast Cancer Screening," Medical Decision Making, , vol. 12(3), pages 222-228, August.
  • Handle: RePEc:sae:medema:v:12:y:1992:i:3:p:222-228
    DOI: 10.1177/0272989X9201200308
    as

    Download full text from publisher

    File URL: https://journals.sagepub.com/doi/10.1177/0272989X9201200308
    Download Restriction: no

    File URL: https://libkey.io/10.1177/0272989X9201200308?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    References listed on IDEAS

    as
    1. Michael Shwartz, 1978. "A Mathematical Model Used to Analyze Breast Cancer Screening Strategies," Operations Research, INFORMS, vol. 26(6), pages 937-955, December.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. 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.
    2. 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.
    3. 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.
    4. 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.
    5. 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.
    6. 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.
    7. 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.
    8. Sandra J. Lee & Marvin Zelen, 2008. "Mortality Modeling of Early Detection Programs," Biometrics, The International Biometric Society, vol. 64(2), pages 386-395, June.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:sae:medema:v:12:y:1992:i:3:p:222-228. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: SAGE Publications (email available below). General contact details of provider: .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.