A pseudo‐penalized quasi‐likelihood approach to the spatial misalignment problem with non‐normal data
A pseudo‐penalized quasi‐likelihood approach to the spatial misalignment problem with non‐normal data
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DOI：
10.1111/biom.12175
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发表时间：
2014-09
期刊：
影响因子：
1.9
通讯作者：
Kenneth K. Lopiano;L. Young;C. Gotway
中科院分区：
文献类型：
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作者：
Kenneth K. Lopiano;L. Young;C. Gotway
来自多个来源的空间参考数据集通常被合并以评估各种结果和协变量之间的关系。与数据相关的地理单位，如地理坐标或地级行政单位，往往在空间上不对齐，即在不同的位置观测或在不同的地理单位上汇总。因此，协变量通常在观察到响应的位置进行预测。在随后对已对齐数据进行建模时，必须考虑用于对齐不同数据集的方法。在这里，我们考虑当响应变量是非正态分布时，使用克里格来对齐点对点和点对面不对齐问题中的数据集的情况。如果使用广义线性模型对关系进行建模，则使用克里格均值作为协变量引起的额外不确定性引入了伯克森误差结构。在本文中，我们开发了一种伪惩罚的准似然算法来解释在估计回归参数和相关不确定性度量时的额外不确定性。该方法应用于一个点对点的例子，评估了佛罗里达州历史上最大的野火（Bugaboo灌木丛火灾）发生后，低出生体重与PM2.5水平之间的关系。在评估火灾发生后佛罗里达州各县哮喘事件与PM2.5水平之间的关系时，提出了一个点对区偏差问题。最后，通过仿真研究对该方法进行了验证。我们的结果表明，该方法在95%置信区间的覆盖率方面表现良好，而忽略额外不确定性的幼稚方法往往低估了与参数估计相关的可变性。在泊松回归模型中，低估最为严重。
Spatially referenced datasets arising from multiple sources are routinely combined to assess relationships among various outcomes and covariates. The geographical units associated with the data, such as the geographical coordinates or areal‐level administrative units, are often spatially misaligned, that is, observed at different locations or aggregated over different geographical units. As a result, the covariate is often predicted at the locations where the response is observed. The method used to align disparate datasets must be accounted for when subsequently modeling the aligned data. Here we consider the case where kriging is used to align datasets in point‐to‐point and point‐to‐areal misalignment problems when the response variable is non‐normally distributed. If the relationship is modeled using generalized linear models, the additional uncertainty induced from using the kriging mean as a covariate introduces a Berkson error structure. In this article, we develop a pseudo‐penalized quasi‐likelihood algorithm to account for the additional uncertainty when estimating regression parameters and associated measures of uncertainty. The method is applied to a point‐to‐point example assessing the relationship between low‐birth weights and PM2.5 levels after the onset of the largest wildfire in Florida history, the Bugaboo scrub fire. A point‐to‐areal misalignment problem is presented where the relationship between asthma events in Florida's counties and PM2.5 levels after the onset of the fire is assessed. Finally, the method is evaluated using a simulation study. Our results indicate the method performs well in terms of coverage for 95% confidence intervals and naive methods that ignore the additional uncertainty tend to underestimate the variability associated with parameter estimates. The underestimation is most profound in Poisson regression models.