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.

翻译：摘要：主动监测（AS）通过重复活检监测疾病进展，已成为癌症治疗中替代即刻手术干预的主流方案。然而，活检属于侵入性操作，常导致感染和出血等严重副作用。为减轻重复监测活检的负担，研究者寻求基于生物标志物的决策规则，以针对个体患者定制活检频率而非采用统一方案。构建或评估此类决策规则颇具挑战性：关键AS结局常因区间删失而无法精确观测；此外，患者一旦获得阳性监测活检结果便会退出AS研究，因此患者退出行为受活检结果影响。本研究提出一种基于非参数核函数的方法，在考虑区间删失和即刻退出机制的前提下，估计定制化AS策略的真阳性率（TPR）和真阴性率（TNR）。基于这些估计，我们构建加权分类框架以估计最优定制化AS策略，并进一步纳入成本效益比用于医疗决策的经济性评估。理论层面，我们给出了所导出AS策略的统一泛化误差界，该界限可容纳TPR与TNR间的所有可能权衡。通过模拟研究与前列腺癌监测数据的实证分析，验证了所提方法的优越性。