IDEAS home Printed from https://ideas.repec.org/a/bla/jorssa/v166y2003i1p51-62.html
   My bibliography  Save this article

Screening for prostate cancer by using random‐effects models

Author

Listed:
  • Larry J. Brant
  • Shan L. Sheng
  • Christopher H. Morrell
  • Geert N. Verbeke
  • Emmanuel Lesaffre
  • H. Ballentine Carter

Abstract

Summary. Random‐effects models are used to screen male participants in a long‐term longitudinal study for prostate cancer. By using posterior probabilities, each male can be classified into one of four diagnostic states for prostate disease: normal, benign prostatic hyperplasia, local cancer and metastatic cancer. Repeated measurements of prostate‐specific antigen, collected when there was no clinical evidence of prostate disease, are used in the classification process. An individual's screening data are considered one repeated measurement at a time as his data are collected longitudinally over time. Posterior probabilities are calculated on the basis of data from other individuals with confirmed diagnoses of each of the four diagnostic states.

Suggested Citation

  • Larry J. Brant & Shan L. Sheng & Christopher H. Morrell & Geert N. Verbeke & Emmanuel Lesaffre & H. Ballentine Carter, 2003. "Screening for prostate cancer by using random‐effects models," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 166(1), pages 51-62, February.
    • Handle: RePEc:bla:jorssa:v:166:y:2003:i:1:p:51-62
    DOI: 10.1111/1467-985X.00258
    as

    Download full text from publisher

    File URL: https://doi.org/10.1111/1467-985X.00258
    Download Restriction: no
    File URL: https://libkey.io/10.1111/1467-985X.00258?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
    ---><---

    Citations

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


    Cited by:

    1. Guillermo Marshall & Rolando De la Cruz-Mesía & Fernando A. Quintana & Anna E. Barón, 2009. "Discriminant Analysis for Longitudinal Data with Multiple Continuous Responses and Possibly Missing Data," Biometrics, The International Biometric Society, vol. 65(1), pages 69-80, March.
    2. Christopher H. Morrell & Larry J. Brant & Shan Sheng & E. Jeffrey Metter, 2012. "Screening for prostate cancer using multivariate mixed-effects models," Journal of Applied Statistics, Taylor & Francis Journals, vol. 39(6), pages 1151-1175, November.
    3. Carles Serrat & Montserrat Ru� & Carmen Armero & Xavier Piulachs & H�ctor Perpi��n & Anabel Forte & �lvaro P�ez & Guadalupe G�mez, 2015. "Frequentist and Bayesian approaches for a joint model for prostate cancer risk and longitudinal prostate-specific antigen data," Journal of Applied Statistics, Taylor & Francis Journals, vol. 42(6), pages 1223-1239, June.
    4. De la Cruz, Rolando, 2008. "Bayesian non-linear regression models with skew-elliptical errors: Applications to the classification of longitudinal profiles," Computational Statistics & Data Analysis, Elsevier, vol. 53(2), pages 436-449, December.
    5. Ian J C MacCormick & Bryan M Williams & Yalin Zheng & Kun Li & Baidaa Al-Bander & Silvester Czanner & Rob Cheeseman & Colin E Willoughby & Emery N Brown & George L Spaeth & Gabriela Czanner, 2019. "Accurate, fast, data efficient and interpretable glaucoma diagnosis with automated spatial analysis of the whole cup to disc profile," PLOS ONE, Public Library of Science, vol. 14(1), pages 1-20, January.

    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:bla:jorssa:v:166:y:2003:i:1:p:51-62. 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: Wiley Content Delivery (email available below). General contact details of provider: https://edirc.repec.org/data/rssssea.html .

    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.