Abstract Details
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Activity Number:
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581
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Type:
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Topic Contributed
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Date/Time:
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Wednesday, August 7, 2013 : 2:00 PM to 3:50 PM
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Sponsor:
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Section on Statistical Consulting
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| Abstract - #309634 |
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Title:
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Multiple Imputation for Competing Risk and Longitudinal Data with Informative Dropout
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Author(s):
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Bo Hu*+
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Companies:
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Cleveland Clinic
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Keywords:
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Multiple imputation ;
Informative dropout ;
longitudinal data ;
survival data
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Abstract:
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We propose a sequential multiple imputation (SMI) procedure to impute missing longitudinal and competing-risk survival data in the joint-modeling setting, where these two types of data are correlated and the survival outcome informatively censors the observations of the longitudinal outcomes. Our procedure uses a series of parametric regression models to impute the missing data in a sequential fashion. The procedure can handle complex competing-risk survival data and multivariate longitudinal data. We illustrate the SMI procedure by applying it to data from a longitudinal chronic kidney disease (CKD) study with 11-year follow-up. The proposed SMI procedure can be widely applied in longitudinal studies.
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Authors who are presenting talks have a * after their name.
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