Comparing multilevel and Bayesian spatial random effects survival models to assess geographical inequalities in colorectal cancer survival: a case study.
Comparing multilevel and Bayesian spatial random effects survival models to assess geographical inequalities in colorectal cancer survival: a case study.
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DOI：
10.1186/1476-072x-13-36
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发表时间：
2014-10-04
影响因子：
4.9
通讯作者：
Baade PD
中科院分区：
文献类型：
--
作者：
Dasgupta P;Cramb SM;Aitken JF;Turrell G;Baade PD
多层次和空间模型正越来越多地用于获取有关地区一级癌症存活率不平等的实质性信息。多层次模型假设独立的地理区域，而空间模型明确纳入地理相关性，通常通过条件自回归先验。然而，这些方法在大规模人群研究中的相对优点尚未得到探讨。使用案例研究的方法，我们报告的影响，使用多层次和空间的生存模型来研究地理不平等的全因生存。多水平离散时间和贝叶斯空间生存模型被用于研究1997-2007年来自澳大利亚昆士兰州的22,727例年龄在20-84岁之间确诊的结直肠癌病例的全因生存率的地理不平等。这两种方法在这个大型数据集上都是可行的，并且产生了类似的固定效应估计值。在加入地区水平协变量后，使用多水平离散时间模型的生存率的地区间变异性不再显著。在调整总面积水平协变量后，生存的空间不平等也显着减少。然而，只有多水平方法提供了对个体患者之间生存期总变异的地理变异贡献的估计。在固定效应的估计中，两种方法之间观察到的差异很小，如果有一个明确的分层数据结构，并且测量个体和地区水平效应对生存差异的独立影响是主要兴趣，则应青睐多水平模型。如果区域之间的空间相关性很重要，如果优先事项是评估小区域生存差异和绘制空间格局图，贝氏空间分析可能是首选。这两种方法可以很容易地适应地理上启用的生存数据，从国际环境。本文的在线版本（doi：10.1186/1476- 072 X-13-36）包含补充材料，可供授权用户使用。
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. The online version of this article (doi:10.1186/1476-072X-13-36) contains supplementary material, which is available to authorized users.