Relative survival represents the preferred framework for the analysis of population cancer survival data. The aim is to model the survival probability associated to cancer in the absence of information about the cause of death. Recent data linkage developments have allowed for incorporating the place of residence or the place where patients receive treatment into the population cancer data bases; however, modeling this spatial information has received little attention in the relative survival setting. We propose a flexible parametric class of spatial excess hazard models (along with inference tools), named ``Relative Survival Spatial General Hazard'' (RS-SGH), that allows for the inclusion of fixed and spatial effects in both time-level and hazard-level components. We illustrate the performance of the proposed model using an extensive simulation study, and provide guidelines about the interplay of sample size, censoring, and model misspecification. We present two case studies, using real data from colon cancer patients in England, aiming at answering epidemiological questions that require the use of a spatial model. These case studies illustrate how a spatial model can be used to identify geographical areas with low cancer survival, as well as how to summarize such a model through marginal survival quantities and spatial effects.

翻译：相对生存是分析人群癌症生存数据的首选框架，其目标是在缺乏死因信息的情况下，对与癌症相关的生存概率进行建模。近期数据链接技术的发展使得居住地或患者接受治疗的地点能够被纳入人群癌症数据库；然而，在相对生存框架下，对这种空间信息的建模尚未引起足够关注。我们提出了一类灵活的参数化空间超额风险模型（附带推断工具），命名为"相对生存空间一般风险模型"（RS-SGH），该模型允许在时间层面和风险层面成分中同时纳入固定效应和空间效应。通过广泛的模拟研究，我们展示了所提出模型的性能，并提供了关于样本量、删失和模型错误设定之间相互作用的指导原则。我们还利用英格兰结肠癌患者的真实数据进行了两项案例研究，旨在回答需要使用空间模型的流行病学问题。这些案例研究展示了空间模型如何用于识别癌症生存率较低的地理区域，以及如何通过边际生存量和空间效应对此类模型进行总结。