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Activity Number:
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24
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Type:
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Contributed
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Date/Time:
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Sunday, August 2, 2009 : 2:00 PM to 3:50 PM
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Sponsor:
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Biometrics Section
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| Abstract - #304483 |
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Title:
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Marginal Methods for the Analysis of Longitudinal Data Arising in Clusters with Missing Covariates
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Author(s):
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Baojiang Chen*+ and Grace Yi and Richard Cook and Xiao-Hua (Andrew) Zhou
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Companies:
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University of Washington and University of Waterloo and University of Waterloo and University of Washington
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Address:
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, , ,
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Keywords:
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Association ; Estimating equation ; Longiudinal data ; Missing data
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Abstract:
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Many analyses for incomplete longitudinal data are directed at examining the impact of covariates on the marginal mean responses. We discuss the setting in which longitudinal responses collected from individuals nested within clusters. We discuss methods for assessing covariate effects on the mean response as well as association parameters when covariates are incompletely observed. Weighted first and second order estimating equations are constructed to obtain consistent estimates of mean and association parameters for data that are missing at random. Empirical studies demonstrate that the proposed estimators have neglible empirical bias in moderate samples. An application to the National Alzheimer's Coordinating Center (NACC) uniform data set (UDS) demonstrated the usefulness of the proposed method.
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