In spatio-temporal disease mapping models, identifiability constraints affect PQL and INLA results
In spatio-temporal disease mapping models, identifiability constraints affect PQL and INLA results
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DOI：
10.1007/s00477-017-1405-0
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发表时间：
2018-03-01
影响因子：
4.2
通讯作者：
Hodges, J. S.
中科院分区：
文献类型：
--
作者：
Goicoa, T.;Adin, A.;Hodges, J. S.
疾病映射研究相对风险或比率在空间和时间上的分布，并且通常依赖于广义线性混合模型（GLSTs），包括固定效应和空间、时间和时空随机效应。这些GLI通常是不可识别的，并且需要约束来实现合理的结果。然而，约束的自动指定有时会导致误导性的结果。特别是，惩罚准似然拟合技术自动集中的随机效应，即使这是不必要的。在贝叶斯方法中，最近引入的集成嵌套拉普拉斯近似计算技术也可以产生错误的结果，如果约束条件没有很好地指定。在本文中，空间，时间，时空相互作用的随机效应重新参数化，使用其精度矩阵的谱分解，以建立适当的可识别性约束。来自西班牙的乳腺癌死亡率数据被用来说明这一想法。
Disease mapping studies the distribution of relative risks or rates in space and time, and typically relies on generalized linear mixed models (GLMMs) including fixed effects and spatial, temporal, and spatio-temporal random effects. These GLMMs are typically not identifiable and constraints are required to achieve sensible results. However, automatic specification of constraints can sometimes lead to misleading results. In particular, the penalized quasi-likelihood fitting technique automatically centers the random effects even when this is not necessary. In the Bayesian approach, the recently-introduced integrated nested Laplace approximations computing technique can also produce wrong results if constraints are not well-specified. In this paper the spatial, temporal, and spatio-temporal interaction random effects are reparameterized using the spectral decompositions of their precision matrices to establish the appropriate identifiability constraints. Breast cancer mortality data from Spain is used to illustrate the ideas.