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Abstract Details
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Activity Number:
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243
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Type:
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Contributed
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Date/Time:
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Monday, July 30, 2012 : 2:00 PM to 3:50 PM
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Sponsor:
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Biometrics Section
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| Abstract - #305564 |
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Title:
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Bias-Corrected GEE Estimation for Longitudinal Data
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Author(s):
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Sudhir Paul*+ and Xuemao Zhang
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Companies:
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University of Windsor and Lakehead University
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Address:
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Dept of Mathematics and Statistics, Windsor, ON, N9B 3P4, Canada
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Keywords:
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Bias Correction ;
Bias Reduction ;
Generalized estimating equations ;
Longitudinal data
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Abstract:
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Generalized estimating equations (GEE) is a widely used method to estimate marginal regression parameters for correlated responses. The advantage of the GEE is that the estimates of the regression parameters are asymptotically unbiased even if the correlation structure is misspecified, although their small sample properties are not known. In this paper, two bias corrected GEE estimators of the regression parameters in longitudinal data are proposed when the number of subjects is small and the number of observations on each subject is fixed. Simulations show that both methods do well in reducing bias and have, in general, higher efficiency than the GEE estimates.
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