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Abstract Details
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Activity Number:
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344
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Type:
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Contributed
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Date/Time:
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Tuesday, August 2, 2011 : 10:30 AM to 12:20 PM
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Sponsor:
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Biometrics Section
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| Abstract - #302067 |
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Title:
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Classifying Time-Dependent Covariates in Modeling Correlated Data
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Author(s):
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Jeffrey Wilson*+ and Anh Nguyen
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Companies:
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Arizona State University and Banner Good Samaritan Medical Center
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Address:
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, , ,
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Keywords:
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dependency ;
estimating equation ;
method of moments ;
longitudinal data
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Abstract:
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When analyzing longitudinal data it is essential to model both the correlation inherent from the repeated measures of the responses as well as the correlation created on account of the feedback created between the responses at a particular time and values of the predictors at other times. The generalized method of moment (GMM) for estimating the coefficients in longitudinal data with correct classification of time-dependent covariates provides substantial gains in efficiency over generalized estimating equations (GEE) with the independent working correlation. While the method provides advantages over generalized estimating equations with independent working correlation it requires correct classification into of the three types of time-dependent covariates. While most covariates in healthcare data may be type III it is determined if we misclassified them as type I or type II, as we saw in a subset of Arizona Medicare data 2003-05 on rehospitalization.
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