Activity Number:
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141
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Type:
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Invited
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Date/Time:
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Monday, August 1, 2016 : 10:30 AM to 12:20 PM
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Sponsor:
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SSC
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Abstract #318399
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Title:
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Weighted Estimating Equations for Semiparametric Transformation Models with Missing Covariates
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Author(s):
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Grace Yi* and Yang Ning and Nancy Reid
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Companies:
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University of Waterloo and and University of Toronto
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Keywords:
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Estimating equation ;
Measurement error ;
Missing covariate
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Abstract:
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In survival analysis, covariate measurements often contain missing observations; ignoring this feature can lead to invalid inference. We propose a class of weighted estimating equations for right censored data with missing covariates under semiparametric transformation models. Time-specific and subject-specific weights are accommodated in the formulation of the weighted estimating equations. We establish unified results for estimating missingness probabilities that cover both parametric and nonparametric modeling schemes. To improve estimation efficiency, the weighted estimating equations are augmented by a new set of unbiased estimating equations. The resultant estimator has the so called "double robustness" property and enjoys the optimal property among a class of estimators.
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Authors who are presenting talks have a * after their name.
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