To GEE or Not to GEE Comparing Population Average and Mixed Models for Estimating the Associations Between Neighborhood Risk Factors and Health
To GEE or Not to GEE Comparing Population Average and Mixed Models for Estimating the Associations Between Neighborhood Risk Factors and Health
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DOI：
10.1097/ede.0b013e3181caeb90
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发表时间：
2010-07-01
期刊：
影响因子：
5.4
通讯作者：
Satariano, William A.
中科院分区：
文献类型：
--
作者：
Hubbard, Alan E.;Ahern, Jennifer;Satariano, William A.
在多层次研究(社区内的受试者)中，通常使用两种建模方法来估计邻里特征与个体水平的健康结果之间的关联。随机效应模型(或混合模型)使用最大似然估计。总体平均模型通常使用广义估计方程(GEE)方法。这些方法被用来取代基本的回归方法，因为同一社区居民的健康可能是相关的，从而违反了传统回归程序所做的独立性假设。这一违规行为与估计的可变性特别相关。尽管文献似乎支持混合模型方法，但几乎没有提供理论指导来证明这一选择的合理性。在这篇文章中，我们回顾了这两种方法提供的估计和推断背后的假设。我们提出了一种观点来看待回归模型在大多数情况下是什么：对一些真实的潜在关系的合理近似。我们一般认为，混合模型涉及对数据生成分布的不可验证的假设，这会导致潜在的误导性估计和有偏见的推断。我们的结论是，总体平均模型的估计方程方法提供了对事实更有用的近似。
Two modeling approaches are commonly used to estimate the associations between neighborhood characteristics and individual-level health outcomes in multilevel studies (subjects within neighborhoods). Random effects models (or mixed models) use maximum likelihood estimation. Population average models typically use a generalized estimating equation (GEE) approach. These methods are used in place of basic regression approaches because the health of residents in the same neighborhood may be correlated, thus violating independence assumptions made by traditional regression procedures. This violation is particularly relevant to estimates of the variability of estimates. Though the literature appears to favor the mixed-model approach, little theoretical guidance has been offered to justify this choice. In this paper, we review the assumptions behind the estimates and inference provided by these 2 approaches. We propose a perspective that treats regression models for what they are in most circumstances: reasonable approximations of some true underlying relationship. We argue in general that mixed models involve unverifiable assumptions on the data-generating distribution, which lead to potentially misleading estimates and biased inference. We conclude that the estimation-equation approach of population average models provides a more useful approximation of the truth.