This is the program for the 2010 Joint Statistical Meetings in Vancouver, British Columbia.
Abstract Details
Activity Number:
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111
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Type:
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Topic Contributed
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Date/Time:
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Monday, August 2, 2010 : 8:30 AM to 10:20 AM
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Sponsor:
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ENAR
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Abstract - #307035 |
Title:
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Modeling Batched Gaussian Longitudinal Data Subject to Informative Dropout
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Author(s):
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Paul S. Albert*+ and Joanna H. Shih
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Companies:
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Eunice Kennedy Shriver National Institute of Child Health and Human Development and National Cancer Institute
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Address:
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6100 Executive Blvd room 7B05F, Bethesda , MD, 20906,
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Keywords:
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dropout ;
marginal inference ;
missing data ;
pooling ;
repeated measures ;
longitudinal data
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Abstract:
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Modeling longitudinal data subject to informative dropout is an active area in statistical research. This article focuses on modeling such longitudinal data when the outcome at each follow-up time is collected in batches rather than individually collected. The motivating example is a study that compared the weight of mice over time between a control and a treatment group, where animal weight was measured in groups of five animals per cage. We develop both a shared parameter and a pattern mixture modeling approach for accounting for potentially informative dropout due to an animal's death. Through simulations, we show that both approaches due well under a correctly specified dropout mechanism. However, the pattern mixture modeling approach is more sensitive to informative dropout and more robust to model misspecification than the shared random parameter model.
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