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Activity Number:
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468
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Type:
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Contributed
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Date/Time:
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Wednesday, August 1, 2007 : 2:00 PM to 3:50 PM
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Sponsor:
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Biometrics Section
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| Abstract - #309413 |
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Title:
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Analysis of Longitudinal Data with Informative Dropout
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Author(s):
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Li Chen*+ and Danyu Lin and Donglin Zeng
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Companies:
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The University of North Carolina at Chapel Hill and The University of North Carolina at Chapel Hill and The University of North Carolina at Chapel Hill
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Address:
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Department of Biostatistics, Chapel Hill, NC, 27599,
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Keywords:
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Counting process ; Repeated measures ; Informative dropout ; Linear regression ; Incomplete data
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Abstract:
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The analysis of longitudinal data is often complicated by informative dropout. We specify a semiparametric bivariate linear regression model for the repeated measures and the time to informative dropout. We develop efficient estimating functions for the regression parameters. The resulting estimators are shown to be consistent and asymptotically normal with limiting covariance matrices that can be estimated through an efficient resample method. Simulation studies show that the proposed estimators perform well in practical situations and are more efficient than those of Lin and Ying (2003, Biostatistics). An application to an HIV study is provided.
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