Likelihood-based estimation of spatial intensity and variation in disease risk from locations observed with error
Likelihood-based estimation of spatial intensity and variation in disease risk from locations observed with error
复制标题
基于可能性的空间强度估计以及来自错误观察位置的疾病风险变化
DOI：
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发表时间：
2012
期刊：
影响因子：
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通讯作者：
D. Zimmerman
中科院分区：
文献类型：
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作者：
X. Fang;Peng Sun;D. Zimmerman
在许多空间流行病学调查中，准确地将地理编码分配到研究人群的住所是数据获取/同化阶段的重要组成部分。然而，不幸的是，当住宅地址地理编码是通过最常见的街道段与地理参考道路文件匹配并随后插值的方法进行时，数百米的位置误差是常见的，特别是在农村地区。在统计分析中忽略这些错误可能会导致有偏差的估计，降低功率，和不正确的结论。本文对现有的基于似然的程序进行了修改，以估计泊松空间点过程的强度和与两个这样的过程相关的相对风险函数，从无误差确定的位置，以便允许从有误差观察到的位置做出有效的推断。通过仿真研究了改进后的方法相对于忽略位置误差的方法的性能。该方法应用于爱荷华州一个县的呼吸系统疾病数据。我们的调查表明，相对于研究区域内强度变化率或相对风险的位置误差标准差的大小决定了考虑位置误差的分析是否会比不考虑位置误差的分析更好；误差必须足够大，才能实现改进。
The accurate assignment of geocodes to the residences of subjects in a study population is an important component of the data acquisition/assimilation stage of many spatial epidemiological investigations. Unfortunately, however, when residential address geocoding is performed by the most common method of street-segment matching to a georeferenced road file and subsequent interpolation, positional errors of hundreds of meters are commonplace, especially in rural locations. Ignoring these errors in a statistical analysis may lead to biased estimators, a reduction in power, and incorrect conclusions. This article develops modifications to existing likelihood-based procedures for estimating the intensity of a Poisson spatial point process and the relative risk function relating two such processes, from locations ascertained without error, so as to permit valid inferences to be made from locations observed with error. The performance of the modified methods relative to methods that ignore positional errors is investigated by simulation. The methodology is applied to respiratory disease data from an Iowa county. Our investigation indicates that the magnitude of the positional error standard deviation relative to the rate of change in intensity or relative risk across the study area determines whether an analysis that accounts for positional errors will improve upon an analysis that does not; errors must be sufficiently large for an improvement to be realized.
库齐克和爱德华兹在确切位置未知的情况下进行了测试。
影响因子：
5
作者：
Jacquez,GM
通讯作者：
Jacquez,GM