PERFORMANCE OF GENERALIZED ESTIMATING EQUATIONS IN PRACTICAL SITUATIONS
PERFORMANCE OF GENERALIZED ESTIMATING EQUATIONS IN PRACTICAL SITUATIONS
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DOI：
10.2307/2533218
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发表时间：
1994-03-01
期刊：
影响因子：
1.9
通讯作者：
LAIRD, NM
中科院分区：
文献类型：
--
作者：
LIPSITZ, SR;FITZMAURICE, GM;LAIRD, NM
Liang和Zeger（1986，Biometrika 73，13-22）提出了用于分析重复二元响应的矩方法，并由普伦蒂斯（1988，Biometrics 44，1033-1048）扩展。Liang和Zeger（1986）和普伦蒂斯（1988）在广义估计方程（GEE）中估计了与个体二元反应向量的期望值以及二元反应对之间的相关性相关的参数。在本文中，我们讨论一步估计，即，估计量，并将其性能与完全迭代估计量在小样本情况下的性能进行了比较。在模拟中，我们发现一步估计的性能是定性类似的完全迭代估计。当样本量较小且二进制响应之间的关联性较高时，我们建议使用一步估计量来规避与完全迭代GEE算法相关的收敛问题。此外，我们发现GEE方法是更有效的比普通的logistic回归方差校正估计随时间变化的协变量的影响。
Moment methods for analyzing repeated binary responses have been proposed by Liang and Zeger (1986, Biometrika 73, 13-22), and extended by Prentice (1988, Biometrics 44, 1033-1048). In their generalized estimating equations (GEE), both Liang and Zeger (1986) and Prentice (1988) estimate the parameters associated with the expected value of an individual's vector of binary responses as well as the correlations between pairs of binary responses. In this paper, we discuss one-step estimators, i.e., estimators obtained from one step of the generalized estimating equations, and compare their performance to that of the fully iterated estimators in small samples. In simulations, we find the performance of the one-step estimator to be qualitatively similar to that of the fully iterated estimator. When the sample size is small and the association between binary responses is high, we recommend using the one-step estimator to circumvent convergence problems associated with the fully iterated GEE algorithm. Furthermore, we find the GEE methods to be more efficient than ordinary logistic regression with variance correction for estimating the effect of a time-varying covariate.