LONGITUDINAL DATA-ANALYSIS FOR DISCRETE AND CONTINUOUS OUTCOMES
LONGITUDINAL DATA-ANALYSIS FOR DISCRETE AND CONTINUOUS OUTCOMES
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DOI：
10.2307/2531248
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发表时间：
1986-03-01
期刊：
影响因子：
1.9
通讯作者：
LIANG, KY
中科院分区：
文献类型：
--
作者：
ZEGER, SL;LIANG, KY
纵向数据集由重复观察的结果和一组协变量组成，用于许多受试者中的每一个。统计分析的一个目的是描述作为协变量的函数的结果变量的边际期望，同时考虑给定受试者的重复观察之间的相关性。本文提出了一种统一的方法，这种分析的各种离散和连续的结果。提出了一类回归参数的广义估计方程。这些方程是准似然法（Wedderburn，1974，Biometrika 61，439-447）中所用方程的扩展。GEE的解决方案是一致的，渐近高斯，即使当时间依赖性是错误的，因为我们经常期望。一致的方差估计。我们说明了使用的GEE方法与纵向数据从母亲的压力对儿童的发病率的影响的研究。
Longitudinal data sets are comprised of repeated observations of an outcome and a set of covariates for each of many subjects. One objective of statistical analysis is to describe the marginal expectation of the outcome variable as a function of the covariates while accounting for the correlation among the repeated observations for a given subject. This paper proposes a unifying approach to such analysis for a variety of discrete and continuous outcomes. A class of generalized estimating equations (GEEs) for the regression parameters is proposed. The equations are extensions of those used in quasi-likelihood (Wedderburn, 1974, Biometrika 61, 439-447) methods. The GEEs have solutions which are consistent and asymptotically Gaussian even when the time dependence is misspecified as we often expect. A consistent variance estimate is presented. We illustrate the use of the GEE approach with longitudinal data from a study of the effect of mothers'' stress on children''s morbidity.