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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
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    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. 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.
    3. 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.
    4. 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.
    5. 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.

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