Changepoint Modeling of Longitudinal PSA as a Biomarker for Prostate Cancer

Prostate-specific antigen (PSA) is an important indicator of the presence of prostate disease. When the volume of the prostate increases, as when cancer is present, the levels of PSA in the blood also increase. Our work focuses on using PSA levels as a biomarker for the recurrence of prostate cancer in patients that have been previously diagnosed and treated by radiotherapy. We fit a fully Bayesian hierarchical changepoint model to longitudinal PSA readings. Our objective is twofold; to better understand the natural history of PSA levels in patients who have completed treatment, and to use the model to identify individual changepoints that are indicative of recurrence. With the goal of accurate early detection of recurrence, we perform a prospective sequential analysis to compare several diagnostic rules, including a rule based on the posterior distribution of individual changepoints.