Screening has resulted in a marked increase in the number of newly diagnosed prostate cancers, while it is unclear whether the early detection of these tumors reduces the prostate cancer mortality. However, while radical treatment for prostate cancer may or may not improve a man's longevity, it can certainly have a big impact on his lifestyle. Active surveillance aims to individualize the management of early prostate cancer by selecting only those men with significant cancers for curative treatment. Patients on active surveillance are closely monitored using serum PSA levels and repeat prostate biopsies. Current approaches of active surveillance often result in a large number of negative biopsies before identifying progression. In this work we utilize the framework of joint models for longitudinal and survival data to better tailor biopsies schedule to individual patients. In particular, we combine the observed PSA measurements and previous biopsies results to in dynamically predicting progression time of each patient and suitably adjusting the timing of the next biopsy. The motivation and data for this work come from the PRIAS study.