In population-based cancer studies, net survival is a crucial measure for population comparison purposes. However, alternative measures, namely the crude probability of death (CPr) and the number of life years lost (LYL) due to death according to different causes, are useful as complementary measures for reflecting different dimensions in terms of prognosis, treatment choice, or development of a control strategy. When the cause of death (COD) information is available, both measures can be estimated in competing risks setting using either cause-specific or subdistribution hazard regression models or with the pseudo-observation approach through direct modeling. We extended the pseudo-observation approach in order to model the CPr and the LYL due to different causes when information on COD is unavailable or unreliable (i.e., in relative survival setting). In a simulation study, we assessed the performance of the proposed approach in estimating regression parameters and examined models with different link functions that can provide an easier interpretation of the parameters. We showed that the pseudo-observation approach performs well for both measures and we illustrated their use on cervical cancer data from the England population-based cancer registry. A tutorial showing how to implement the method in R software is also provided.

中文翻译：

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对癌症死亡的粗略概率和因癌症损失的生命年数进行直接建模，无需死因：相对生存环境中的伪观察方法。


在基于人群的癌症研究中，净生存率是用于人群比较的重要指标。然而，替代指标，即粗死亡概率（CPr）和因不同原因死亡而损失的生命年数（LYL），可作为补充指标，反映预后、治疗选择或死亡等不同维度。制定控制策略。当死亡原因（COD）信息可用时，可以使用特定原因或次分布风险回归模型或通过直接建模的伪观察方法在竞争风险设置中估计这两种措施。我们扩展了伪观测方法，以便在 COD 信息不可用或不可靠（即在相对生存环境中）时对由于不同原因而导致的 CPr 和 LYL 进行建模。在模拟研究中，我们评估了所提出的方法在估计回归参数方面的性能，并检查了具有不同链接函数的模型，这些函数可以更轻松地解释参数。我们表明，伪观察方法在这两种测量中都表现良好，并且我们说明了它们在来自英格兰基于人口的癌症登记处的宫颈癌数据上的使用。还提供了一个教程，展示如何在 R 软件中实现该方法。