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Real‐Time Individual Predictions of Prostate Cancer Recurrence Using Joint Models
Author(s) -
Taylor Jeremy M. G.,
Park Yongseok,
Ankerst Donna P.,
ProustLima Cecile,
Williams Scott,
Kestin Larry,
Bae Kyoungwha,
Pickles Tom,
Sandler Howard
Publication year - 2013
Publication title -
biometrics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 2.298
H-Index - 130
eISSN - 1541-0420
pISSN - 0006-341X
DOI - 10.1111/j.1541-0420.2012.01823.x
Subject(s) - prostate cancer , markov chain monte carlo , bayesian probability , computer science , medicine , prostate specific antigen , statistics , medical physics , cancer , artificial intelligence , mathematics
Summary 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.