Using generalized estimating equations for longitudinal data analysis
Using generalized estimating equations for longitudinal data analysis
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DOI：
10.1177/1094428104263672
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发表时间：
2004-04-01
影响因子：
9.5
通讯作者：
Ballinger, GA
中科院分区：
文献类型：
--
作者：
Ballinger, GA
Zeger和Liang的广义估计方程（GEE）方法有助于分析纵向、嵌套或重复测量设计中收集的数据。GEE使用广义线性模型来估计相对于普通最小二乘回归更有效和无偏的回归参数，部分原因是它们允许指定一个工作相关矩阵，该矩阵说明了许多不同分布（包括正态分布、二项式分布和泊松分布）的因变量的受试者内相关性。作者简要解释了GEE背后的理论及其有益的统计特性和局限性，并通过两个例子将GEE与分析纵向数据的次优方法进行了比较。第一个演示将GEE应用于分析来自具有计数响应变量的纵向实验室研究的数据;第二个演示将GEE应用于分析来自组织的分支办公室内嵌套的受试者的具有正态分布响应变量的数据。
The generalized estimating equation (GEE) approach of Zeger and Liang facilitates analysis of data collected in longitudinal, nested, or repeated measures designs. GEEs use the generalized linear model to estimate more efficient and unbiased regression parameters relative to ordinary least squares regression in part because they permit specification of a working correlation matrix that accounts for the form of within-subject correlation of responses on dependent variables of many different distributions, including normal, binomial, and Poisson. The author briefly explains the theory behind GEEs and their beneficial statistical properties and limitations and compares GEEs to suboptimal approaches for analyzing longitudinal data through use of two examples. The first demonstration applies GEEs to the analysis of data from a longitudinal lab study with a counted response variable; the second demonstration applies GEEs to analysis of data with a normally distributed response variable from subjects nested within branch offices of an organization.