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International Biometric Society Dynamic individualized predictions based on joint models with applications in prostate cancer research Jeremy M. G. Taylor Department of Biostatistics, University of Michigan Following radiation therapy treatment for prostate cancer patients are monitored by regular measurements of prostate specific antigen (PSA), a simple blood test. Increasing trends in PSA are suggestive that the cancer may be regrowing and that clinical recurrence of a detectable tumor may be imminent. Thus for each patient it would be useful to be able to calculate the risk of clinical recurrence in the next short period of time and be able to update this calculation as more PSA data is collected for that patient. Using a large training datasets we build a joint longitudinal model for the PSA values and survival model for the clinical recurrences. The longitudinal model involves random effects and the survival model involves a proportional hazards model with PSA a time-dependent covariate. Markov chain Monte Carlo methods are used for estimation. To provide individualized predictions for a new patient, the posterior distribution from the training dataset is used as a prior for the data from the new patient. Calculation of the probability of recurrence in the next three years for the new patient involves a second MCMC algorithm. The calculator is implemented online at psacalc.sph.umich.edu International Biometric Conference, Floripa, Brazil, 5 – 10 December 2010
