  Active surveillance (AS) using repeated biopsies to monitor disease
progression has been a popular alternative to immediate surgical intervention
in cancer care. However, a biopsy procedure is invasive and sometimes leads to
severe side effects of infection and bleeding. To reduce the burden of repeated
surveillance biopsies, biomarker-assistant decision rules are sought to replace
the fix-for-all regimen with tailored biopsy intensity for individual patients.
Constructing or evaluating such decision rules is challenging. The key AS
outcome is often ascertained subject to interval censoring. Furthermore,
patients will discontinue their participation in the AS study once they receive
a positive surveillance biopsy. Thus, patient dropout is affected by the
outcomes of these biopsies. In this work, we propose a nonparametric
kernel-based method to estimate the true positive rates (TPRs) and true
negative rates (TNRs) of a tailored AS strategy, accounting for interval
censoring and immediate dropouts. Based on these estimates, we develop a
weighted classification framework to estimate the optimal tailored AS strategy
and further incorporate the cost-benefit ratio for cost-effectiveness in
medical decision-making. Theoretically, we provide a uniform generalization
error bound of the derived AS strategy accommodating all possible trade-offs
between TPRs and TNRs. Simulation and application to a prostate cancer
surveillance study show the superiority of the proposed method.
