B1008
Title: Estimating optimal tailored active surveillance strategy under interval censoring
Authors: Yingqi Zhao - Fred Hutchinson Cancer Research Center (United States) [presenting]
Abstract: 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. As the key AS outcome is often ascertained subject to interval censoring, constructing or evaluating such decision rules is challenging. A nonparametric kernel-based method is proposed to estimate the true positive rates (TPRs) and true negative rates (TNRs) of a tailored surveillance strategy. Based on these estimates, a weighted classification framework is further developed to estimate the optimal tailored active surveillance strategy under interval censoring. A version incorporating the cost-benefit ratio to target cost-effective strategies is also proposed. Theoretically, a uniform generalization error bound of the derived surveillance strategy is provided accommodating all possible trade-offs between TPR and TNR. Simulation and application to a prostate cancer active surveillance study show the superiority of the proposed method.
