A Poisson regression approach for modelling spatial autocorrelation between geographically referenced observations.
A Poisson regression approach for modelling spatial autocorrelation between geographically referenced observations.
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DOI：
10.1186/1471-2288-11-133
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发表时间：
2011-10-03
影响因子：
4
通讯作者：
Jolley D
中科院分区：
文献类型：
--
作者：
Mohebbi M;Wolfe R;Jolley D
流行病学中常用的分析方法不考虑观测值之间的空间相关性。在回归分析中，忽略这种自相关性会使参数估计值产生偏差，并产生不正确的标准误差估计值。我们使用2001年至2005年Babol癌症登记处食管癌（EC）的年龄标准化发病率（SIR），并从伊朗统计中心提取社会经济指标。SIR使用了以下模型：（1）具有聚集特异性非空间随机效应的泊松回归;（2）具有聚集特异性空间随机效应的泊松回归。基于距离和基于邻域的自相关结构用于定义空间随机效应，并采用伪随机方法估计模型参数。贝叶斯信息准则（BIC），赤池的信息准则（AIC）和调整后的伪R2，用于模型比较。一个高斯半变异函数的有效范围为225公里的最佳拟合空间自相关的聚集水平EC的发病率。Moran's I指数大于其预期值，表明EC的系统地理聚集。基于距离和基于邻域的泊松回归估计值基本相似。当残差空间依赖性建模时，协变量效应的点和区间估计值与从非空间泊松模型获得的估计值不同。空间格局明显的EC SIR和观察点估计值和标准误差不同的建模方法表明会计剩余的空间相关性在分析EC发病率在伊朗里海地区的重要性。我们的研究结果还表明，空间平滑必须小心应用。
Analytic methods commonly used in epidemiology do not account for spatial correlation between observations. In regression analyses, omission of that autocorrelation can bias parameter estimates and yield incorrect standard error estimates. We used age standardised incidence ratios (SIRs) of esophageal cancer (EC) from the Babol cancer registry from 2001 to 2005, and extracted socioeconomic indices from the Statistical Centre of Iran. The following models for SIR were used: (1) Poisson regression with agglomeration-specific nonspatial random effects; (2) Poisson regression with agglomeration-specific spatial random effects. Distance-based and neighbourhood-based autocorrelation structures were used for defining the spatial random effects and a pseudolikelihood approach was applied to estimate model parameters. The Bayesian information criterion (BIC), Akaike's information criterion (AIC) and adjusted pseudo R2, were used for model comparison. A Gaussian semivariogram with an effective range of 225 km best fit spatial autocorrelation in agglomeration-level EC incidence. The Moran's I index was greater than its expected value indicating systematic geographical clustering of EC. The distance-based and neighbourhood-based Poisson regression estimates were generally similar. When residual spatial dependence was modelled, point and interval estimates of covariate effects were different to those obtained from the nonspatial Poisson model. The spatial pattern evident in the EC SIR and the observation that point estimates and standard errors differed depending on the modelling approach indicate the importance of accounting for residual spatial correlation in analyses of EC incidence in the Caspian region of Iran. Our results also illustrate that spatial smoothing must be applied with care.
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影响因子：
4.9
作者：
Mohebbi, Mohammadreza;Wolfe, Rory;Burton, Robert C.
通讯作者：
Burton, Robert C.
影响因子：
3.1
作者：
Monestiez, P;Dubroca, L;Guinet, C
通讯作者：
Guinet, C
影响因子：
2.7
作者：
MORAN, PAP
通讯作者：
MORAN, PAP
影响因子：
1.7
作者：
Jang, Myoung Jin;Lee, Youngjo;Browne, William J.
通讯作者：
Browne, William J.
影响因子：
8.8
作者：
Mahboubi, E;Kmet, J;Cook, P J;Day, N E;Ghadirian, P;Salmasizadeh, S
通讯作者：
Salmasizadeh, S