Leontyeva Yuliya, Huang Yuxin, Cramb Susanna, Cameron Jessica, Baade Peter, Mengersen Kerrie, Thompson Helen
Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden.
School of Mathematical Science, Queensland University of Technology, Brisbane, Australia.
Stat Med. 2025 Feb 10;44(3-4):e10287. doi: 10.1002/sim.10287.
To date, there have not been any population-based cancer studies quantifying geographical patterns of the loss in life expectancy (LLE) and crude probability of death due to cancer ( ). These absolute measures of survival are complementary to the more typically used relative measures of excess mortality and relative survival, and, together, they provide a fuller understanding of geographical disparities in survival outcomes for cancer patients. We propose using a spatially flexible parametric relative survival model in the Bayesian framework, which allows for the inclusion of spatial effects in hazard-level model components. The relative survival framework is the preferred approach to analyze cancer survival data because it does not require information on the cause of death, and the Bayesian spatial modeling approach allows complex and robust small-area estimation. The calculation of spatial estimates for LLE and are demonstrated using publicly available simulated datasets. The associated computer program scripts are available to support the understanding and implementation of our methodology in other spatial cancer modelling applications.
迄今为止，尚未有基于人群的癌症研究对预期寿命损失（LLE）和癌症所致粗死亡率（ ）的地理模式进行量化。这些生存的绝对指标是对更常用的超额死亡率和相对生存等相对指标的补充，并且它们共同提供了对癌症患者生存结果地理差异的更全面理解。我们建议在贝叶斯框架中使用空间灵活的参数化相对生存模型，该模型允许在风险水平模型组件中纳入空间效应。相对生存框架是分析癌症生存数据的首选方法，因为它不需要死亡原因信息，而贝叶斯空间建模方法允许进行复杂且稳健的小区域估计。使用公开可用的模拟数据集演示了LLE和 的空间估计计算。相关的计算机程序脚本可用于支持在其他空间癌症建模应用中对我们方法的理解和实施。
