Li Hong, Gatsonis Constantine
Department of Preventive Medicine, Rush University Medical Center, Chicago, IL 60612, U.S.A.
Department of Biostatistics, Brown University, Providence, RI 02912, U.S.A.
Sci China Math. 2012 Aug 1;55(8):1565-182. doi: 10.1007/s11425-012-4475-y.
Surveillance to detect cancer recurrence is an important part of care for cancer survivors. In this paper we discuss the design of optimal strategies for early detection of disease recurrence based on each patient's distinct biomarker trajectory and periodically updated risk estimated in the setting of a prospective cohort study. We adopt a latent class joint model which considers a longitudinal biomarker process and an event process jointly, to address heterogeneity of patients and disease, to discover distinct biomarker trajectory patterns, to classify patients into different risk groups, and to predict the risk of disease recurrence. The model is used to develop a monitoring strategy that dynamically modifies the monitoring intervals according to patients' current risk derived from periodically updated biomarker measurements and other indicators of disease spread. The optimal biomarker assessment time is derived using a utility function. We develop an algorithm to apply the proposed strategy to monitoring of new patients after initial treatment. We illustrate the models and the derivation of the optimal strategy using simulated data from monitoring prostate cancer recurrence over a 5-year period.
监测癌症复发是癌症幸存者护理的重要组成部分。在本文中，我们讨论了基于每位患者独特的生物标志物轨迹以及在前瞻性队列研究背景下定期更新的风险估计，设计疾病复发早期检测的最优策略。我们采用一种潜在类别联合模型，该模型同时考虑纵向生物标志物过程和事件过程，以解决患者和疾病的异质性，发现不同的生物标志物轨迹模式，将患者分类到不同风险组，并预测疾病复发风险。该模型用于制定一种监测策略，根据定期更新的生物标志物测量值和其他疾病扩散指标得出的患者当前风险动态调整监测间隔。最优生物标志物评估时间通过效用函数得出。我们开发了一种算法，将所提出的策略应用于初始治疗后新患者的监测。我们使用来自为期5年监测前列腺癌复发的模拟数据来说明模型和最优策略的推导。
