Real-Time Individual Predictions of Prostate Cancer Recurrence Using Joint Models

Jeremy M.G. Taylor, Yongseok Park, Donna P. Ankerst, Cecile Proust-Lima, Scott Williams, Larry Kestin, Kyoungwha Bae, Tom Pickles, Howard Sandler

Research output: Contribution to journalArticlepeer-review

56 Scopus citations

Abstract

Patients who were previously treated for prostate cancer with radiation therapy are monitored at regular intervals using a laboratory test called Prostate Specific Antigen (PSA). If the value of the PSA test starts to rise, this is an indication that the prostate cancer is more likely to recur, and the patient may wish to initiate new treatments. Such patients could be helped in making medical decisions by an accurate estimate of the probability of recurrence of the cancer in the next few years. In this article, we describe the methodology for giving the probability of recurrence for a new patient, as implemented on a web-based calculator. The methods use a joint longitudinal survival model. The model is developed on a training dataset of 2386 patients and tested on a dataset of 846 patients. Bayesian estimation methods are used with one Markov chain Monte Carlo (MCMC) algorithm developed for estimation of the parameters from the training dataset and a second quick MCMC developed for prediction of the risk of recurrence that uses the longitudinal PSA measures from a new patient.

Original languageEnglish (US)
Pages (from-to)206-213
Number of pages8
JournalBiometrics
Volume69
Issue number1
DOIs
StatePublished - Mar 2013

Keywords

  • Joint longitudinal-survival model
  • Online calculator
  • PSA
  • Predicted probability
  • Prostate cancer

ASJC Scopus subject areas

  • Statistics and Probability
  • Biochemistry, Genetics and Molecular Biology(all)
  • Immunology and Microbiology(all)
  • Agricultural and Biological Sciences(all)
  • Applied Mathematics

Fingerprint Dive into the research topics of 'Real-Time Individual Predictions of Prostate Cancer Recurrence Using Joint Models'. Together they form a unique fingerprint.

Cite this