Individualized Prediction in Prostate Cancer Using Joint Longitudinal and Survival Models

Abstract:

Patients with localized prostate cancer are frequently treated with radiation therapy. Following treatment PSA measurements are typically obtained at regular intervals for the purpose of monitoring and obatining an early indication of disease recurrence. In this talk I will present a statistical model that describes serial PSA measurements and clinical recurrence. This joint longitudinal survival model will be fit to data on over 2000 patients using Markov chain Monte Carlo methods. The model can be used to provide predictions of the future probability of disease recurrence based on a patient's series of PSA values. For a patient who shows a pattern of increasing PSA values but no clinical symptoms a possible intervention is hormone therapy. This statistical model provide useful quantitative information that can aid in the clinical decision making about whether to initiate hormone therapy. A website calculator psacalc.sph.umich.edu has been developed so that patients and their physicians can make individualized predictions.