The use of GEE for analyzing longitudinal binomial data: A primer using data from a tobacco intervention
The use of GEE for analyzing longitudinal binomial data: A primer using data from a tobacco intervention
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DOI：
10.1016/j.addbeh.2006.03.030
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发表时间：
2007-01-01
影响因子：
4.4
通讯作者：
Brandon, Thomas H.
中科院分区：
文献类型：
--
作者：
Lee, Ji-Hyun;Herzog, Thaddeus A.;Brandon, Thomas H.
成瘾行为研究中的纵向研究设计很常见，因为研究人员越来越关注各种解释变量如何随着时间的推移影响反应。特别是，这种设计用于具有多个随访点的干预研究。这些设计通常涉及重复测量参与者的响应，因此期望每个参与者内的相关性。只有考虑到重复测量之间的参与者内部相关性，才能获得正确的推断，这可能会使纵向数据的分析复杂化。近年来，广义估计方程（GEE）已成为分析非正态纵向数据的标准方法，但它往往没有被成瘾研究者使用。本文的目的是提供一个概述的GEE方法分析相关的二进制数据的行为研究人员，使用的数据从干预研究的预防吸烟复发。(c)2006爱思唯尔有限公司保留所有权利。
Longitudinal study designs in addictive behaviors research are common as researchers have focused increasingly on how various explanatory variables affect responses over time. In particular, such designs are used in intervention studies that have multiple follow-up points. These designs typically involve repeated measurement of participants' responses, and thus correlation within each participant is expected. Correct inferences can only be obtained by taking into account this within-participant correlation between repeated measurements, which can complicate the analysis of longitudinal data. In recent years, generalized estimating equations (GEE) has become a standard method for analyzing non-normal longitudinal data, yet it often is not utilized by addiction researchers. The goal of this article is to provide an overview of the GEE approach for analyzing correlated binary data for behavioral researchers, using data from an intervention study on the prevention of relapse to tobacco smoking. (c) 2006 Elsevier Ltd. All rights reserved.