Efficient measurement error correction with spatially misaligned data
Efficient measurement error correction with spatially misaligned data
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DOI：
10.1093/biostatistics/kxq083
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发表时间：
2011-10-01
期刊：
影响因子：
2.1
通讯作者：
Lumley, Thomas
中科院分区：
文献类型：
--
作者：
Szpiro, Adam A.;Sheppard, Lianne;Lumley, Thomas
环境统计中的关联研究往往涉及在空间上不一致的暴露和结果数据。一种常见的策略是采用空间模型（如通用克里金）来预测具有结果数据的位置处的暴露，然后使用预测的暴露来估计感兴趣的回归参数。这会导致测量误差，因为预测的曝光量并不完全对应于真实值。我们通过将其分解为Berkson类和经典类分量来表征测量误差。一种校正方法是参数自举，其是有效的但计算密集的，因为其需要解决针对每个自举样本中的暴露模型参数的非线性优化问题。我们提出了一种计算量较小的替代方案，称为“参数引导”，只需要解决一个非线性优化问题，我们还比较了引导方法与其他最近提出的方法。我们说明了我们的方法在模拟和公开的数据从环境保护局。
Association studies in environmental statistics often involve exposure and outcome data that are misaligned in space. A common strategy is to employ a spatial model such as universal kriging to predict exposures at locations with outcome data and then estimate a regression parameter of interest using the predicted exposures. This results in measurement error because the predicted exposures do not correspond exactly to the true values. We characterize the measurement error by decomposing it into Berkson-like and classical-like components. One correction approach is the parametric bootstrap, which is effective but computationally intensive since it requires solving a nonlinear optimization problem for the exposure model parameters in each bootstrap sample. We propose a less computationally intensive alternative termed the "parameter bootstrap" that only requires solving one nonlinear optimization problem, and we also compare bootstrap methods to other recently proposed methods. We illustrate our methodology in simulations and with publicly available data from the Environmental Protection Agency.