This is the program for the 2010 Joint Statistical Meetings in Vancouver, British Columbia.

Abstract Details

Activity Number: 111
Type: Topic Contributed
Date/Time: Monday, August 2, 2010 : 8:30 AM to 10:20 AM
Sponsor: ENAR
Abstract - #307035
Title: Modeling Batched Gaussian Longitudinal Data Subject to Informative Dropout
Author(s): Paul S. Albert*+ and Joanna H. Shih
Companies: Eunice Kennedy Shriver National Institute of Child Health and Human Development and National Cancer Institute
Address: 6100 Executive Blvd room 7B05F, Bethesda , MD, 20906,
Keywords: dropout ; marginal inference ; missing data ; pooling ; repeated measures ; longitudinal data

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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