Document Detail


A bias correction for covariance estimators to improve inference with generalized estimating equations that use an unstructured correlation matrix.
MedLine Citation:
PMID:  23255154     Owner:  NLM     Status:  Publisher    
Abstract/OtherAbstract:
Generalized estimating equations (GEEs) are routinely used for the marginal analysis of correlated data. The efficiency of GEE depends on how closely the working covariance structure resembles the true structure, and therefore accurate modeling of the working correlation of the data is important. A popular approach is the use of an unstructured working correlation matrix, as it is not as restrictive as simpler structures such as exchangeable and AR-1 and thus can theoretically improve efficiency. However, because of the potential for having to estimate a large number of correlation parameters, variances of regression parameter estimates can be larger than theoretically expected when utilizing the unstructured working correlation matrix. Therefore, standard error estimates can be negatively biased. To account for this additional finite-sample variability, we derive a bias correction that can be applied to typical estimators of the covariance matrix of parameter estimates. Via simulation and in application to a longitudinal study, we show that our proposed correction improves standard error estimation and statistical inference. Copyright © 2012 John Wiley & Sons, Ltd.
Authors:
Philip M Westgate
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Publication Detail:
Type:  JOURNAL ARTICLE     Date:  2012-12-16
Journal Detail:
Title:  Statistics in medicine     Volume:  -     ISSN:  1097-0258     ISO Abbreviation:  Stat Med     Publication Date:  2012 Dec 
Date Detail:
Created Date:  2012-12-20     Completed Date:  -     Revised Date:  -    
Medline Journal Info:
Nlm Unique ID:  8215016     Medline TA:  Stat Med     Country:  -    
Other Details:
Languages:  ENG     Pagination:  -     Citation Subset:  -    
Copyright Information:
Copyright © 2012 John Wiley & Sons, Ltd.
Affiliation:
Department of Biostatistics, College of Public Health, University of Kentucky, Lexington, KY 40536, U.S.A.
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