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 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 a case study using real data from colon cancer patients in England. This case study illustrates 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优选框架，其目标是在缺乏死因信息的情况下对癌症相关生存概率建模。近期数据链接技术的进展使得将居住地信息纳入人群癌症数据库成为可能，然而在相对生存场景中，对空间信息的建模尚未得到充分关注。我们提出一类灵活的参数化空间超额风险模型（配套推断工具），命名为"相对生存空间广义风险模型"（RS-SGH），该模型允许在时间水平和风险水平分量中纳入固定效应与空间效应。通过大规模模拟研究验证模型性能，并提供样本量、删失与模型误设交互作用的指导准则。我们利用英格兰结肠癌患者真实数据进行案例研究，展示了空间模型如何识别癌症低生存地理区域，以及如何通过边际生存量与空间效应对此类模型进行综合概括。