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Abstract Details
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Activity Number:
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158
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Type:
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Topic Contributed
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Date/Time:
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Monday, July 30, 2012 : 10:30 AM to 12:20 PM
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Sponsor:
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Biometrics Section
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| Abstract - #305265 |
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Title:
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Correlated Logistic Regression with Generalized Method of Moments Estimators
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Author(s):
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Trent L Lalonde*+ and Jeffrey R Wilson
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Companies:
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University of Northern Colorado and Arizona State University
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Address:
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Department of Applied Statistics and Research Methods, McKee Hall 520, Greeley, CO, , US
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Keywords:
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Binary Response ;
Clustered Response ;
Time Dependent Covariates ;
Generalized Linear Model
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Abstract:
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Correlated responses and predictors are commonly encountered in longitudinal studies involving binary responses. The generalized linear model (GLM) is often inappropriate for analyzing correlated data due to extravariation (McCullagh and Nelder (1989)). The marginal GEE model (Zeger and Liang (1986)) assumes that the covariates are time-independent. Neither of these methods is suitable when covariates are time-dependent. For time-dependent covariates, a generalized method-of-moments (GMM) has been developed (Lai and Small (2007)), in which the estimating equations are determined by the type of time-varying covariate.
This paper will introduce data situations with both correlated responses and covariates. The GLM will be described, as well as the extension to GEE. The GMM method will be described for logistic regression and advantages over GEE will be discussed.
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