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.
Written by:
Taylor JM, Park Y, Ankerst DP, Proust-Lima C, Williams S, Kestin L, Bae K, Pickles T, Sandler H. Are you the author?
Department of Biostatistics, University of Michigan, Ann Arbor, Michigan 48109, U.S.A.; Zentrum Mathematik, Technische Universitaet Muenchen, Munich 85748, Germany; Department of Epidemiology/Biostatistics and Urology, University of Texas Health Science Center at San Antonio, San Antonio, Texas 78229, U.S.A.; INSERM, U897, Epidemiology and Biostatistics Research Center, 33076 Bordeaux, France; Division of Radiation Oncology, Peter MacCallum Cancer Centre, Melbourne, Victoria 3002, Australia; Michigan Healthcare Professionals/21st Century Oncology, Farmington Hills, Michigan 48334, U.S.A.; Novartis Pharmaceuticals Corporation, Florham Park, NJ 07932, U.S.A.; British Columbia Cancer Agency, Vancouver, British  Columbia V5Z 4E6, Canada; Department of Radiation Oncology, Cedars-Sinai Medical Center, Los Angeles, California 90048, U.S.A.
Reference: Biometrics. 2013 Feb 4. Epub ahead of print.
doi: 10.1111/j.1541-0420.2012.01823.x
PubMed Abstract
PMID: 23379600
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