Prostate cancer patients who undergo prostatectomy are closely monitored for recurrence and metastasis using routine prostate-specific antigen (PSA) measurements. When PSA levels rise, salvage therapies are recommended to decrease the risk of metastasis. However, due to the side effects of these therapies and to avoid over-treatment, it is important to understand which patients and when to initiate these salvage therapies. In this work, we use the University of Michigan Prostatectomy registry Data to tackle this question. Due to the observational nature of this data, we face the challenge that PSA is simultaneously a time-varying confounder and an intermediate variable for salvage therapy. We define different causal salvage therapy effects defined conditionally on different specifications of the longitudinal PSA history. We then illustrate how these effects can be estimated using the framework of joint models for longitudinal and time-to-event data. All proposed methodology is implemented in the freely-available R package JMbayes2.

翻译：接受前列腺切除术的前列腺癌患者通过常规前列腺特异性抗原（PSA）监测来密切追踪复发与转移情况。当PSA水平升高时，会建议采用挽救治疗以降低转移风险。然而，考虑到这些疗法的副作用及避免过度治疗，明确哪些患者应何时启动挽救治疗尤为重要。本研究利用密歇根大学前列腺切除术登记数据库解决该问题。由于数据的观察性特征，我们面临PSA同时作为时变混杂变量和挽救治疗中间变量的挑战。我们定义了基于不同纵向PSA历史条件设定的因果挽救治疗效果，并阐释如何通过纵向与时间-事件数据联合模型框架估计这些效应。所有提出的方法均通过开源R包JMbayes2实现。