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Frequentist and Bayesian approaches for a joint model for prostate cancer risk and longitudinal prostate-specific antigen data

Author

Listed:
  • Carles Serrat
  • Montserrat Ru�
  • Carmen Armero
  • Xavier Piulachs
  • H�ctor Perpi��n
  • Anabel Forte
  • �lvaro P�ez
  • Guadalupe G�mez

Abstract

The paper describes the use of frequentist and Bayesian shared-parameter joint models of longitudinal measurements of prostate-specific antigen (PSA) and the risk of prostate cancer (PCa). The motivating dataset corresponds to the screening arm of the Spanish branch of the European Randomized Screening for Prostate Cancer study. The results show that PSA is highly associated with the risk of being diagnosed with PCa and that there is an age-varying effect of PSA on PCa risk. Both the frequentist and Bayesian paradigms produced very close parameter estimates and subsequent 95% confidence and credibility intervals. Dynamic estimations of disease-free probabilities obtained using Bayesian inference highlight the potential of joint models to guide personalized risk-based screening strategies.

Suggested Citation

  • 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.
    • Handle: RePEc:taf:japsta:v:42:y:2015:i:6:p:1223-1239
    DOI: 10.1080/02664763.2014.999032
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    References listed on IDEAS

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    1. Jeremy M. G. Taylor & Yongseok Park & Donna P. Ankerst & Cecile Proust-Lima & Scott Williams & Larry Kestin & Kyoungwha Bae & Tom Pickles & Howard Sandler, 2013. "Real-Time Individual Predictions of Prostate Cancer Recurrence Using Joint Models," Biometrics, The International Biometric Society, vol. 69(1), pages 206-213, March.
    2. Little, Roderick J., 2006. "Calibrated Bayes: A Bayes/Frequentist Roadmap," The American Statistician, American Statistical Association, vol. 60, pages 213-223, August.
    3. 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.
    4. Rizopoulos, Dimitris, 2012. "Fast fitting of joint models for longitudinal and event time data using a pseudo-adaptive Gaussian quadrature rule," Computational Statistics & Data Analysis, Elsevier, vol. 56(3), pages 491-501.
    5. Dimitris Rizopoulos, 2011. "Dynamic Predictions and Prospective Accuracy in Joint Models for Longitudinal and Time-to-Event Data," Biometrics, The International Biometric Society, vol. 67(3), pages 819-829, September.
    6. Inoue, Lurdes Y.T. & Etzioni, Ruth & Morrell, Christopher & Muller, Peter, 2008. "Modeling Disease Progression With Longitudinal Markers," Journal of the American Statistical Association, American Statistical Association, vol. 103, pages 259-270, March.
    7. 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.
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    Cited by:

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    2. Ruben Amoros & Ruth King & Hidenori Toyoda & Takashi Kumada & Philip J. Johnson & Thomas G. Bird, 2019. "A continuous-time hidden Markov model for cancer surveillance using serum biomarkers with application to hepatocellular carcinoma," METRON, Springer;Sapienza Università di Roma, vol. 77(2), pages 67-86, August.

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