Dasgupta Paramita, Cramb Susanna M, Aitken Joanne F, Turrell Gavin, Baade Peter D
Cancer Council Queensland, PO Box 201, Spring Hill, QLD 4004, Australia.
Int J Health Geogr. 2014 Oct 4;13:36. doi: 10.1186/1476-072X-13-36.
Multilevel and spatial models are being increasingly used to obtain substantive information on area-level inequalities in cancer survival. Multilevel models assume independent geographical areas, whereas spatial models explicitly incorporate geographical correlation, often via a conditional autoregressive prior. However the relative merits of these methods for large population-based studies have not been explored. Using a case-study approach, we report on the implications of using multilevel and spatial survival models to study geographical inequalities in all-cause survival.
Multilevel discrete-time and Bayesian spatial survival models were used to study geographical inequalities in all-cause survival for a population-based colorectal cancer cohort of 22,727 cases aged 20-84 years diagnosed during 1997-2007 from Queensland, Australia.
Both approaches were viable on this large dataset, and produced similar estimates of the fixed effects. After adding area-level covariates, the between-area variability in survival using multilevel discrete-time models was no longer significant. Spatial inequalities in survival were also markedly reduced after adjusting for aggregated area-level covariates. Only the multilevel approach however, provided an estimation of the contribution of geographical variation to the total variation in survival between individual patients.
With little difference observed between the two approaches in the estimation of fixed effects, multilevel models should be favored if there is a clear hierarchical data structure and measuring the independent impact of individual- and area-level effects on survival differences is of primary interest. Bayesian spatial analyses may be preferred if spatial correlation between areas is important and if the priority is to assess small-area variations in survival and map spatial patterns. Both approaches can be readily fitted to geographically enabled survival data from international settings.
多级模型和空间模型越来越多地用于获取癌症生存方面地区层面不平等的实质性信息。多级模型假定地理区域相互独立，而空间模型则明确纳入地理相关性，通常通过条件自回归先验来实现。然而，这些方法在大型基于人群的研究中的相对优点尚未得到探讨。我们采用案例研究方法，报告使用多级和空间生存模型研究全因生存方面地理不平等的影响。
使用多级离散时间模型和贝叶斯空间生存模型，对1997年至2007年期间在澳大利亚昆士兰州诊断的22727例年龄在20 - 84岁的基于人群的结直肠癌队列的全因生存地理不平等进行研究。
两种方法在这个大型数据集上都是可行的，并且对固定效应产生了相似的估计。在添加地区层面协变量后，使用多级离散时间模型的生存地区间变异性不再显著。在调整汇总的地区层面协变量后，生存的空间不平等也明显降低。然而，只有多级方法提供了地理变异对个体患者生存总变异贡献的估计。
在固定效应估计方面，两种方法之间几乎没有差异。如果存在清晰的分层数据结构，并且测量个体和地区层面效应对生存差异的独立影响是主要关注点，那么应优先选择多级模型。如果地区间的空间相关性很重要，并且优先事项是评估小区域生存差异并绘制空间模式，那么贝叶斯空间分析可能更受青睐。两种方法都可以很容易地应用于来自国际环境的具有地理信息的生存数据。
