  Active surveillance (AS), where biopsies are conducted to detect cancer
progression, has been acknowledged as an efficient way to reduce the
overtreatment of prostate cancer. Most AS cohorts use fixed biopsy schedules
for all patients. However, the ideal test frequency remains unknown, and the
routine use of such invasive tests burdens the patients. An emerging idea is to
generate personalized biopsy schedules based on each patient's
progression-specific risk. To achieve that, we propose the interval-censored
cause-specific joint model (ICJM), which models the impact of longitudinal
biomarkers on cancer progression while considering the competing event of early
treatment initiation. The underlying likelihood function incorporates the
interval-censoring of cancer progression, the competing risk of treatment, and
the uncertainty about whether cancer progression occurred since the last biopsy
in patients that are right-censored or experience the competing event. The
model can produce patient-specific risk profiles until a horizon time. If the
risk exceeds a certain threshold, a biopsy is conducted. The optimal threshold
can be chosen by balancing two indicators of the biopsy schedules: the expected
number of biopsies and expected delay in detection of cancer progression. A
simulation study showed that our personalized schedules could considerably
reduce the number of biopsies per patient by 34%-54% compared to the fixed
schedules, though at the cost of a slightly longer detection delay.
