Abstract Details
Activity Number:
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537
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Type:
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Invited
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Date/Time:
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Wednesday, August 6, 2014 : 2:00 PM to 3:50 PM
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Sponsor:
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ENAR
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Abstract #310800
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Title:
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Estimating Longitudinal Trajectories Nonparametrically with Informative Yet Explainable Dropouts
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Author(s):
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Lu Wang*+ and Xihong Lin
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Companies:
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University of Michigan and Harvard School of Public Health
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Keywords:
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nonparametric regression ;
missing data ;
kernel GEE ;
seemingly unrelated kernel estimating equations ;
estimation efficiency ;
longitudinal study
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Abstract:
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We consider nonparametric regression for longitudinal data subject to dropout with explainable mechanism. The reason people drop out may depend on the history of both outcome and covariates, but is independent of future outcome and covariates. We propose inverse probability weighted (IPW) kernel generalized estimating equations (kernel GEEs) and IPW seemingly unrelated (SUR) kernel estimating equations using either complete cases or all available cases. We show that all these IPW kernel estimators are consistent when the probability of dropout is known by design or is estimated using a correctly specified parametric model. The most efficient IPW kernel GEE estimator is obtained by ignoring the within-subject correlation, while in contrast the most efficient IPW SUR kernel estimator is obtained by accounting for the within-subject correlation, and is more efficient than the most efficient IPW kernel GEE counterpart. When appropriate covariance matrices are used, the IPW kernel estimators obtained using all available cases are more efficient than those using complete cases only. We perform simulations to evaluate the finite sample performance of the proposed methods.
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Authors who are presenting talks have a * after their name.
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