Haneuse Sebastien J-P A, Wakefield Jonathan C
Center for Health Studies, Group Health Cooperative, Seattle, Washington 98101, USA.
Biometrics. 2007 Mar;63(1):128-36. doi: 10.1111/j.1541-0420.2006.00673.x.
The ecological study design suffers from a broad range of biases that result from the loss of information regarding the joint distribution of individual-level outcomes, exposures, and confounders. The consequent nonidentifiability of individual-level models cannot be overcome without additional information; we combine ecological data with a sample of individual-level case-control data. The focus of this article is hierarchical models to account for between-group heterogeneity. Estimation and inference pose serious computational challenges. We present a Bayesian implementation based on a data augmentation scheme where the unobserved data are treated as auxiliary variables. The methods are illustrated with a dataset of county-specific infant mortality data from the state of North Carolina.
生态研究设计存在广泛的偏差，这些偏差源于个体层面的结果、暴露因素和混杂因素联合分布信息的缺失。如果没有额外信息，就无法克服由此导致的个体层面模型不可识别的问题；我们将生态数据与个体层面病例对照数据样本相结合。本文的重点是用于解释组间异质性的分层模型。估计和推断带来了严峻的计算挑战。我们基于一种数据扩充方案提出了一种贝叶斯实现方法，其中将未观测数据视为辅助变量。我们用北卡罗来纳州特定县的婴儿死亡率数据集对这些方法进行了说明。
