As cancer patient survival improves, late effects from treatment are becoming the next clinical challenge. Chemotherapy and radiotherapy, for example, potentially increase the risk of both morbidity and mortality from second malignancies and cardiovascular disease. To provide clinically relevant population‐level measures of late effects, it is of importance to (1) simultaneously estimate the risks of both morbidity and mortality, (2) partition these risks into the component expected in the absence of cancer and the component due to the cancer and its treatment, and (3) incorporate the multiple time scales of attained age, calendar time, and time since diagnosis. Multistate models provide a framework for simultaneously studying morbidity and mortality, but do not solve the problem of partitioning the risks. However, this partitioning can be achieved by applying a relative survival framework, allowing us to directly quantify the excess risk. This article proposes a combination of these two frameworks, providing one approach to address (1) to (3). Using recently developed methods in multistate modeling, we incorporate estimation of excess hazards into a multistate model. Both intermediate and absorbing state risks can be partitioned and different transitions are allowed to have different and/or multiple time scales. We illustrate our approach using data on Hodgkin lymphoma patients and excess risk of diseases of the circulatory system, and provide user‐friendly Stata software with accompanying example code.

中文翻译：

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结合超额危险估计和多个时间尺度的多状态模型


随着癌症患者生存率的提高，治疗的后期效应正成为下一个临床挑战。例如，化疗和放疗可能会增加第二恶性肿瘤和心血管疾病的发病率和死亡率风险。为了提供临床相关的人群水平的晚期影响衡量标准，重要的是（1）同时估计发病率和死亡率的风险，（2）将这些风险划分为不存在癌症时预期的风险部分和由于癌症导致的风险部分。癌症及其治疗，(3) 纳入年龄、日历时间和诊断以来时间的多个时间尺度。多状态模型提供了同时研究发病率和死亡率的框架，但没有解决风险划分的问题。然而，这种划分可以通过应用相对生存框架来实现，使我们能够直接量化超额风险。本文提出了这两个框架的组合，提供了一种解决（1）到（3）的方法。使用最近开发的多状态建模方法，我们将过度危险的估计纳入多状态模型中。中间状态风险和吸收状态风险都可以划分，并且允许不同的转换具有不同和/或多个时间尺度。我们使用霍奇金淋巴瘤患者和循环系统疾病风险过高的数据来说明我们的方法，并提供用户友好的 Stata 软件以及随附的示例代码。