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Activity Number:
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569
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Type:
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Contributed
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Date/Time:
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Thursday, August 6, 2009 : 8:30 AM to 10:20 AM
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Sponsor:
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Biometrics Section
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| Abstract - #304084 |
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Title:
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A Random Pattern Mixture Model for Longitudinal Binary Outcome with Informative Dropouts
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Author(s):
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Chengcheng Liu*+ and Wensheng Guo and Sarah Ratcliffe
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Companies:
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Merck & Co., Inc. and University of Pennsylvania and University of Pennsylvania
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Address:
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, , ,
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Keywords:
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informative dropout ; random pattern mixture model ; EM algorithm
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Abstract:
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When informative dropouts exist for longitudinal studies, ignoring the informative dropout will result in biased results. Joint modeling of the outcome and dropout time can take into account some information from informative dropouts and correct some biases. We introduce a random pattern mixture model in this talk to jointly model the longitudinal binary outcome and dropout time; the random pattern effects in this context are defined as the latent effects linking the dropout process and the longitudinal outcome. Conditional on the random pattern effects, longitudinal binary outcome and dropout time are assumed independent. An EM algorithm is used for estimation. The method is applied to a data set from the Prevention of Suicide in Primary Care Elderly Collaborative Trial (PROSPECT).
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- The address information is for the authors that have a + after their name.
- Authors who are presenting talks have a * after their name.
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