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Activity Number: 67
Type: Contributed
Date/Time: Sunday, July 29, 2007 : 4:00 PM to 5:50 PM
Sponsor: ENAR
Abstract - #309159
Title: Multiple Parameter Combination of Between-Cluster and Within-Cluster Regression Coefficient Estimates
Author(s): Julie A. Stoner*+ and Brian G. Leroux
Companies: University of Nebraska Medical Center and University of Washington
Address: Division of Biostatistics CoPH, Omaha, NE, 68198-4350,
Keywords: Between-cluster Effects ; Within-cluster Effects ; Correlated Data ; Generalized Estimating Equations
Abstract:

A marginal regression modeling method that optimally weights and combines between-cluster and within-cluster coefficient estimates in a correlated data setting is presented. The combination accounts for correlation among multiple between-cluster and within-cluster coefficient estimators and may be applied to multiple coefficient parameters in a given model. In settings where combining coefficient estimates is appropriate, the method results in increased efficiency while maintaining stability and coverage probabilities and avoids direct correlation parameter estimation. The method is appealing, relative to typical implementations of GEE, in settings with large cluster sizes relative to the number of independent clusters, varying cluster sizes, and complicated correlation structures. Method performance is studied by simulation and an application. (NIH grant R01DE15651)

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Revised September, 2007