Doubly robust inference procedure for relative survival ratio in population‐based cancer registry data
Doubly robust inference procedure for relative survival ratio in population‐based cancer registry data
复制标题
基于人群的癌症登记数据中相对生存率的双稳健推理程序
DOI：
10.1002/sim.8521
复制
发表时间：
2020
影响因子：
2
通讯作者：
Hattori Satoshi
中科院分区：
文献类型：
--
作者：
Komukai Sho;Hattori Satoshi
癌症登记系统在癌症控制的研究和政策制定中发挥着重要作用。一般而言，癌症登记数据中没有关于死亡原因的信息。为了在缺乏死因信息的情况下推断癌症患者的生存率，在基于人群的癌症研究中广泛使用相对生存率，该研究使用了一般人群的外部生命表。分析癌症登记数据的另一个困难是信息审查。在这篇文章中，我们提出了一个双重鲁棒推理程序的相对生存率在某种类型的信息删失，称为协变量依赖删失。建议的估计是双重稳健的，在这个意义上，它是一致的，如果至少有一个回归模型的死亡时间和删失时间是正确指定的。此外，我们还引入了一种双重稳健性检验，以评估至死亡时间和删失时间之间的潜在条件独立性假设。该检验是基于模型的，但具有双重稳健性，因为正确指定了至事件时间和删失时间的至少一个模型，并保持其标称显著性水平。这一显著特征要求我们依赖于假设对癌症登记数据进行推断，这些假设比现有方法弱得多，并且可以通过经验验证。我们研究的理论和经验性质，我们提出的方法的渐近理论和模拟研究。我们用日本大坂的癌症登记数据说明了所提出的方法。
Cancer registry system has been playing important roles in research and policy making in cancer control. In general, information on cause of death is not available in cancer registry data. To make inference on survival of cancer patients in the absence of cause of death information, the relative survival ratio is widely used in the population‐based cancer research utilizing external life tables for the general population. Another difficulty arising in analyzing cancer registry data is informative censoring. In this article, we propose a doubly robust inference procedure for the relative survival ratio under a certain type of informative censoring, called the covariate‐dependent censoring. The proposed estimator is doubly robust in the sense that it is consistent if at least one of the regression models for the time‐to‐death and for the censoring time is correctly specified. Furthermore, we introduced a doubly robust test assessing underlying conditional independence assumption between the time‐to‐death and the censoring time. This test is model based, but is doubly robust in the sense that at least one of the models for the time to event and for the censoring time is correctly specified, it maintains its nominal significance level. This notable feature entails us to make inference on cancer registry data relying on assumptions, which are much weaker than the existing methods and are verifiable empirically. We examine the theoretical and empirical properties of our proposed methods by asymptotic theory and simulation studies. We illustrate the proposed method with cancer registry data in Osaka, Japan.