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Activity Number:
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276
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Type:
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Contributed
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Date/Time:
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Tuesday, August 4, 2009 : 8:30 AM to 10:20 AM
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Sponsor:
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Biometrics Section
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| Abstract - #305159 |
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Title:
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Covariate-Adjusted Linear Mixed Effects Model for Longitudinal Data
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Author(s):
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Danh Nguyen*+
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Companies:
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University of California, Davis
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Address:
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1 Shields Ave, Davis, CA, 95616,
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Keywords:
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covariate-adjusted regression ; linear mixed model ; longitudinal data ; multiplicative effects
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Abstract:
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Linear mixed effects (LME) models are useful for longitudinal data/repeated measurements. We present a new class of covariate-adjusted LME models for longitudinal data that nonparametrically adjusts for a normalizing covariate. The proposed method involves fitting a parametric LME model to the data after adjusting for the nonparametric effects of a baseline covariate. In particular, the effect of the observable covariate on the response and predictors of the LME model is modeled nonparametrically via smooth unknown functions. Estimation for the variance components is also developed. Numerical properties of the proposed estimators are investigated with simulation studies. The consistency of the proposed estimators are also established. We present an application to a longitudinal data set on calcium absorption, accounting for baseline distortion from body mass index.
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- The address information is for the authors that have a + after their name.
- Authors who are presenting talks have a * after their name.
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