Efficient parameter estimation in longitudinal data analysis using a hybrid GEE method

School of Economics, Singapore Management University, 90 Stamford Road, Singapore.
Biostatistics (Impact Factor: 2.65). 05/2009; 10(3):436-45. DOI: 10.1093/biostatistics/kxp002
Source: PubMed


The method of generalized estimating equations (GEEs) provides consistent estimates of the regression parameters in a marginal
regression model for longitudinal data, even when the working correlation model is misspecified (Liang and Zeger, 1986). However, the efficiency of a GEE estimate can be seriously affected by the choice of the working correlation model. This
study addresses this problem by proposing a hybrid method that combines multiple GEEs based on different working correlation
models, using the empirical likelihood method (Qin and Lawless, 1994). Analyses show that this hybrid method is more efficient than a GEE using a misspecified working correlation model. Furthermore,
if one of the working correlation structures correctly models the within-subject correlations, then this hybrid method provides
the most efficient parameter estimates. In simulations, the hybrid method's finite-sample performance is superior to a GEE
under any of the commonly used working correlation models and is almost fully efficient in all scenarios studied. The hybrid
method is illustrated using data from a longitudinal study of the respiratory infection rates in 275 Indonesian children.

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Available from: You-Gan Wang, Feb 23, 2015
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    • "The findings in our Theorem 1 show that it is meaningful to investigate how the within-subject correlation or the within-block dependence affect the efficiency of the EL inference. More recently, by using the EL method, Leung et al. (2009) proposed a hybrid method that combined multiple GEEs based on different working correlation structures. They showed that is more robust and efficient for the choice of the working correlation structures. "
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