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Abstract Details
Activity Number:
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471
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Type:
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Contributed
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Date/Time:
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Wednesday, August 3, 2011 : 8:30 AM to 10:20 AM
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Sponsor:
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Section on Survey Research Methods
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Abstract - #303244 |
Title:
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Generalized Estimating Equation Model for Binary Outcomes with Covariates Missing by Design
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Author(s):
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Qixuan Chen*+ and Myunghee C. Paik
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Companies:
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Columbia University and Columbia University
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Address:
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722 West 168th Street, R652, New York, NY, 10032,
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Keywords:
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dioxins ;
estimating equations ;
jackknife estimator ;
missing data
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Abstract:
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We propose a method to handle monotone missing covariates in a generalized estimating equation (GEE) model for correlated binary outcomes, when the covariates are missing by design. The regression coefficients are obtained by solving an aggregate unbiased estimating function, and the variance of the regression coefficients is estimated using the one-step jackknife estimator (Lipsitz et al. 1994). The advantages of the proposed new method over the complete cases analysis and the inverse probability weighted estimating equation are demonstrated by simulation studies. The new method is used to study whether concentration of dioxin congeners in house perimeter soil is an important predictor for having a high concentration of dioxin congeners in household dust, where structurally similar and correlated congeners are present and a limited numbers of soil samples are measured.
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