Abstract #301911

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JSM 2003 Abstract #301911
Activity Number: 216
Type: Contributed
Date/Time: Tuesday, August 5, 2003 : 8:30 AM to 10:20 AM
Sponsor: Biometrics Section
Abstract - #301911
Title: Adjusting for Conditional Dependence in Generalized Linear Mixed Models
Author(s): Howard Michael Mackey*+ and Brian Scott Caffo
Companies: Centocor, Inc. and Johns Hopkins University
Address: 200 Great Valley Parkway, Malvern, PA, 19355-1307,
Keywords: mixed model ; random effects ; conditional dependence
Abstract:

Generalized linear mixed models (GLMMs) are gaining popularity in many areas research. Most existing methods assume conditional independence of the response at the experimental unit level given the random effects. This assumption may not hold in models where data is collected longitudinally, yet GLMMs are often used in such scenarios. Adjusting for residual covariance is relatively straightforward in linear mixed models. For non-Gaussian data, however, little methodology exists when the conditional independence assumption does not hold. We outline a simple method for adjusting the traditional GLMM in the event of residual covariance. The properties of this method will be examined and the method will be illustrated with real data.

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