Residual spatial correlation between geographically referenced observations - A Bayesian hierarchical modeling approach
Residual spatial correlation between geographically referenced observations - A Bayesian hierarchical modeling approach
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DOI：
10.1097/01.ede.0000164558.73773.9c
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发表时间：
2005-07-01
期刊：
影响因子：
5.4
通讯作者：
Waller, LA
中科院分区：
文献类型：
--
作者：
Boyd, HA;Flanders, WD;Waller, LA
背景：流行病学中常用的分析方法不能解释观察结果之间的空间相关性。在回归分析中，这种遗漏可能会使参数估计值产生偏差，并产生不正确的标准误估计值。我们提出了一种贝叶斯分层模型（BW方法，该模型考虑了空间相关性，并通过将这种建模方法应用于海地班氏丝虫感染的数据来说明其优点和缺点。方法：一项消除海地淋巴丝虫病的计划评估了W.莱奥甘市57所学校发生班氏感染。我们使用半变异函数和相关图分析了患病率数据的空间模式。然后，我们建模的数据使用（1）标准逻辑回归（GLM）;（2）非贝叶斯逻辑广义线性混合模型（GLMM）与学校特定的非空间随机效应;（3）BHM与学校特定的非空间随机效应;（4）BHM与空间random effects.Results：一个指数半变异函数的有效范围为2.15公里最适合的数据。GLMM和非空间BHM点估计值具有可比性，并且与边际GLM点估计值基本相似。相反，与非空间混合模型结果相比，空间BHM点估计值明显衰减。bancrofti患病率数据和观察点估计值和标准误差根据建模方法的不同而不同，表明在W. bancrofiti感染数据。贝叶斯层次模型提供了一种灵活的，易于实现的方法来建模空间相关的数据。然而，我们的研究结果也表明，空间平滑必须小心应用。
Background: Analytic methods commonly used in epidemiology do not account for spatial correlation between observations. In regression analyses, this omission can bias parameter estimates and yield incorrect standard error estimates. We present a Bayesian hierarchical model (BW approach that accounts for spatial correlation, and illustrate its strengths and weaknesses by applying this modeling approach to data on Wuchereria bancrofti infection in Haiti.Methods: A program to eliminate lymphatic filariasis in Haiti assessed prevalence of W. bancrofti infection in 57 schools across Leogane Commune. We analyzed the spatial pattern in the prevalence data using semi-variograms and correlograms. We then modeled the data using (1) standard logistic regression (GLM); (2) non-Bayesian logistic generalized linear mixed models (GLMMs) with school-specific nonspatial random effects; (3) BHMs with school-specific nonspatial random effects; and (4) BHMs with spatial random effects.Results: An exponential semi-variogram with an effective range of 2.15 km best fit the data. GLMM and nonspatial BHM point estimates were comparable and also were generally similar with the marginal GLM point estimates. In contrast, compared with the nonspatial mixed model results, spatial BHM point estimates were markedly attenuated.Discussion: The clear spatial pattern evident in the Haitian W. bancrofti prevalence data and the observation that point estimates and standard errors differed depending on the modeling approach indicate that it is important to account for residual spatial correlation in analyses of W. bancrofiti infection data. Bayesian hierarchical models provide a flexible, readily implementable approach to mod-eling spatially correlated data. However, our results also illustrate that spatial smoothing must be applied with care.