Some properties of the Liang-Zeger method applied to clustered binary regression

被引:2
作者
Balemi, A [1 ]
Lee, A [1 ]
机构
[1] Univ Auckland, Dept Stat, Auckland 1, New Zealand
关键词
binary regression; clusters; generalized estimating equations; sandwich estimator;
D O I
10.1111/1467-842X.00060
中图分类号
O21 [概率论与数理统计]; C8 [统计学];
学科分类号
020208 ; 070103 ; 0714 ;
摘要
The Generalized Estimating Equation (GEE) method popularized by Liang and Zeger provides a very general method for fitting regression models to observations that occur in clusters. Features of the method are the specification of a 'working correlation' (a guess at the true correlation structure of the data) which is used to improve efficiency in estimating the regression coefficients, and the 'information sandwich' which provides a way of consistently estimating the standard errors of the estimated regression coefficients even if (as we might expect) the working correlation is wrong. This paper develops asymptotic expressions for the bias and efficiency both of the regression coefficient estimates and of the sandwich estimate, and uses them to study the behaviour of the estimates. It looks at the effect of the choice of the working correlation on the estimate and also examines the effect of different cluster sizes and different degrees of correlation between the covariates. The performance of these methods is found to be excellent, particularly when the degree of correlation in the responses and covariates is small to moderate.
引用
收藏
页码:43 / 58
页数:16
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