Using PSA to detect prostate cancer onset: An application of Bayesian retrospective and prospective changepoint identification
Serum prostate-specific antigen (PSA) concentrations are now widely used to aid in the detection of prostate cancer. When prostate cancer is present, PSA levels typically increase. But a number of benign conditions will also cause elevated PSA levels and, conversely, prostate cancer has been diagnosed in the absence of raised PSA.
We analyze one of the most extensive data sets currently available for longitudinal PSA readings, obtained by an historical prospective study of frozen serum samples from the Nutritional Prevention of Cancer Trial (Clark et al.1996). These data consist of serial readings for over 1200 men taken at approximate six-month intervals over an 11 year period.
We fit a fully Bayesian hierarchical changepoint model to the longitudinal PSA readings. Our ojectives include better understanding the natural history of PSA levels in patients who have and have not been diagnosed with prostate cancer and identifying subject-specific changepoints that are indicative of cancer onset. With the goal of accurate early detection, we perform a prospective sequential analysis to compare several diagnostic rules, including a rule based on the posterior distribution of individual changepoints.
KeywordsProstate Cancer Receiver Operating Characteristic Curve Posterior Distribution Benign Prostatic Hyperplasia Linear Mixed Effect Model
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