IDEAS home Printed from https://ideas.repec.org/a/taf/uiiexx/v52y2020i2p166-180.html
   My bibliography  Save this article

Optimal data-driven policies for disease screening under noisy biomarker measurement

Author

Listed:
  • Saloumeh Sadeghzadeh
  • Ebru K. Bish
  • Douglas R. Bish

Abstract

Biomarker testing, where a biochemical marker is used to predict the presence or absence of a disease in a subject, is an essential tool in public health screening. For many diseases, related biomarkers may have a wide range of concentration among subjects, particularly among the disease positive subjects. Furthermore, biomarker levels may fluctuate based on external or subject-specific factors. These sources of variability can increase the likelihood of subject misclassification based on a biomarker test. We study the minimization of the subject misclassification cost for public health screening of non-infectious diseases, considering regret and expectation-based objectives, and derive various key structural properties of optimal screening policies. Our case study of newborn screening for cystic fibrosis, based on real data from North Carolina, indicates that substantial reductions in classification errors can be achieved through the use of the proposed optimization-based models over current practices.

Suggested Citation

  • Saloumeh Sadeghzadeh & Ebru K. Bish & Douglas R. Bish, 2020. "Optimal data-driven policies for disease screening under noisy biomarker measurement," IISE Transactions, Taylor & Francis Journals, vol. 52(2), pages 166-180, February.
    • Handle: RePEc:taf:uiiexx:v:52:y:2020:i:2:p:166-180
    DOI: 10.1080/24725854.2019.1630867
    as

    Download full text from publisher

    File URL: http://hdl.handle.net/10.1080/24725854.2019.1630867
    Download Restriction: Access to full text is restricted to subscribers.
    File URL: https://libkey.io/10.1080/24725854.2019.1630867?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
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Hussein El Hajj & Douglas R. Bish & Ebru K. Bish & Denise M. Kay, 2022. "Novel Pooling Strategies for Genetic Testing, with Application to Newborn Screening," Management Science, INFORMS, vol. 68(11), pages 7994-8014, November.
    2. Steffen Rebennack, 2022. "Data-driven stochastic optimization for distributional ambiguity with integrated confidence region," Journal of Global Optimization, Springer, vol. 84(2), pages 255-293, October.

    More about this item

    Statistics

    Access and download statistics

    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:taf:uiiexx:v:52:y:2020:i:2:p:166-180. 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.

    We have no bibliographic references for this item. You can help adding them by using 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: Chris Longhurst (email available below). General contact details of provider: http://www.tandfonline.com/uiie .

    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.