A hierarchical aggregate data model with spatially correlated disease rates

被引：6
|
作者：
Guthrie, KA
Sheppard, L
Wakefield, J
机构：
[1] Fred Hutchinson Canc Res Ctr, Seattle, WA 98109 USA
[2] Univ Washington, Dept Biostat, Seattle, WA 98195 USA
[3] Univ Washington, Dept Environm Hlth, Seattle, WA 98195 USA
[4] Univ Washington, Dept Stat, Seattle, WA 98195 USA
来源：
BIOMETRICS | 2002年 / 58卷 / 04期
关键词：
aggregate data analysis; Bayesian disease mapping; ecological bias; spatial dependence;
D O I：
10.1111/j.0006-341X.2002.00898.x
中图分类号：
Q [生物科学];
学科分类号：
07 ; 0710 ; 09 ;
摘要：
The aggregate data study design (Prentice and Sheppard, 1995, Biometrika 82, 113-125) estimates individual-level exposure effects by regressing population-based disease rates on covariate data from survey samples in each population group. In this work, we further develop the aggregate data model to allow for residual spatial correlation among disease rates across populations. Geographical variation that is not explained by model predictors and has a spatial component often arises in studies of rare chronic diseases, such as breast cancer. We combine the aggregate and Bayesian disease-mapping models to provide an intuitive approach to the modeling of spatial effects while drawing correct inference regarding the exposure effect. Based oil the results of simulation studies, we suggest guidelines for use of the proposed model.
引用
收藏
页码：898 / 905
页数：8
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